diff --git a/notebooks/vertical_skill.ipynb b/notebooks/vertical_skill.ipynb
new file mode 100644
index 000000000..7c137eb52
--- /dev/null
+++ b/notebooks/vertical_skill.ipynb
@@ -0,0 +1,435 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "568821f0",
+   "metadata": {},
+   "source": [
+    "### Test implementation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4376a422",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import modelskill as ms\n",
+    "import pandas as pd\n",
+    "from pathlib import Path\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3695d1d4",
+   "metadata": {},
+   "source": [
+    "**Read observations and create VerticalObservation object**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7e1e20ff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load obs from csv\n",
+    "csv_file = Path(\"../tests/testdata/vertical/VerticalProfile_obs1.csv\")\n",
+    "metadata = pd.read_csv(csv_file, nrows=2, header=None)\n",
+    "xpos = float(metadata.values[0, 0].split(\":\")[-1].lstrip())\n",
+    "ypos = float(metadata.values[1, 0].split(\":\")[-1].lstrip())\n",
+    "obs_1 = pd.read_csv(csv_file, sep=\",\", comment=\"#\", index_col=0, parse_dates=True)\n",
+    "vo = ms.VerticalObservation(\n",
+    "    obs_1,\n",
+    "    item=\"salt\",\n",
+    "    z_item=\"z\",\n",
+    "    x=xpos,\n",
+    "    y=ypos,\n",
+    "    quantity=ms.Quantity(\"salinity\", \"psu\"),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "84af2662",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "vo.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "01dae719",
+   "metadata": {},
+   "source": [
+    "**Read dfsu-3D model results**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "454ccc2f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mr = ms.DfsuModelResult(\"../tests/testdata/vertical/sigma_z_coast.dfsu\", item=\"Salinity\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ead5ea0",
+   "metadata": {},
+   "source": [
+    "**Plot spatial overview of model and observation**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f7d0d6ef",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\lajo\\offlineSpace\\github\\modelskill\\.venv\\Lib\\site-packages\\mikeio\\spatial\\_FM_geometry.py:802: FutureWarning: boundary_polylines is renamed to boundary_polygons\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = ms.plotting.spatial_overview(vo, mr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70413d43",
+   "metadata": {},
+   "source": [
+    "**Matching to get a comparer, skill and plot**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "dd260352",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>n</th>\n",
+       "      <th>bias</th>\n",
+       "      <th>rmse</th>\n",
+       "      <th>urmse</th>\n",
+       "      <th>mae</th>\n",
+       "      <th>cc</th>\n",
+       "      <th>si</th>\n",
+       "      <th>r2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>observation</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>salt</th>\n",
+       "      <td>108</td>\n",
+       "      <td>-0.197348</td>\n",
+       "      <td>0.20948</td>\n",
+       "      <td>0.070255</td>\n",
+       "      <td>0.197348</td>\n",
+       "      <td>0.940359</td>\n",
+       "      <td>0.010997</td>\n",
+       "      <td>-0.030702</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               n      bias     rmse     urmse       mae        cc        si  \\\n",
+       "observation                                                                   \n",
+       "salt         108 -0.197348  0.20948  0.070255  0.197348  0.940359  0.010997   \n",
+       "\n",
+       "                   r2  \n",
+       "observation            \n",
+       "salt        -0.030702  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cmp = ms.match(vo, mr)\n",
+    "cmp.skill()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c115f13a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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IEJNuAiPTMToEqUpwyZcv32N1gdFfGJHz9ttvq/3790sahS+++OKxKeMYnYIR8KgD1Nv7778vo3cocUD7aNWqlUydPXz4sKQQePPNN6McDdGrVy8ZjbRkyRLpB3/88YfavXv3Y49Dv8AoJ7QZjNRE+8QIJkzvxeMLFy4s1817PKlNxwRt27RzXPCemEJcvXr1Jz4Xo0cxiq9EiRLSVzdv3iyjIc0Ihk8//VT64qxZs6TcSKlQr149x6wPzMDImzevjMjCSK6BAweqzz77TP3888+OURvIcV6jRg3ZBuA98Nkwq6Jly5aqd+/eqmTJko6y4zYiX4ZtA34v8HuXI0cOVa5cOTVt2rQYn4N+Ubt2bafb0M9wuz+JT925wjbp1q1bKmvWrMqfxLfuvv/+e5nlN2jQIOWv4lN35jlff/21CggIUEWLFlV9+vRR9+7dU/4kPnWH/XOkT8QsDOwDXbx4US1YsEA1bNjQY+Um7+rfv796/vnnZfZSqVKlZJ/ZjqlrcNyL/V4c5+L4tWfPnur06dPKjn788Uc5Vv7mm29kHz1p0qRunSUdVx9//LGkd0KsAfEBuzHHYzieQSwDx0EoZ86cOZ3utwscj+bPn19+czCDJkOGDMqOgoODpQ9h9t2oUaPkt2LHjh1yHGinOsXxa6FChSSGhONnxHtwXAt26keIUxUsWFDicdh2Ik6H9GF2MnjwYCnb4sWL5botMil4O9JOnrFt2zYZObl48eIo78cIVdyPM9gYmYrR364jfxo0aCD/Hz16tIzgjIiIeOx1zpw5IyO0cfbWCiNlzZlcM6ITZ86sMDq2Vq1ajuu//fZblKO8rRo1aiSjxw3XUbbgOqIbI3Pr1Knj9JhPPvlERoMZqIO2bds6ruOsVI4cOfSUKVOiLQv5ll27dkm7wChjV9YR3RjNnSJFChn9beBsJUYqu47otrYZnMnG63/xxReO27Zu3Sq34b7YtunYuHfvnq5SpYpu3LhxrEZztGrVSkZpRwUj2vF5MZLFQF/HqPOvv/462tfs3r27btasmWOECT4nzjxHxV9HEFLihd8qXPA7t3v3bpkBhNkOM2fOjPY5RYoU0SEhIU63LV++XPqO68yrxCw+dedqxIgROkuWLLIP40/iU3dHjx6V/Zm///7br7fH8ak7zLbAc/A7vX37dumv+O0PCgrS/iS+ffbnn3+WWZLJkyeX7dxrr70W5bEEJS44JmzevLmuWLGi7BfiWBH7xjjuWrJkibbTSFn0ccxE3rRpk37w4IGePXu2zLZcunSptiPUK0Yh165dW/fp08c2o7rxHeO4AbM4sK20uyZNmsjoY+POnTvaTjCbKGPGjPqFF16QtgmYiYv4jN2sWLFClytXTsoM+/fv1zVr1tQDBgzQdjJ37lzp6+vWrZOZyr/++qvUKWYf2MnatWtl9if2NzDrABkHUL/WmIM3XblyRb/77ru6fPnysj+E2QbYz7TDqG6O6PYzsT2LhpEXrtcx6hVwVg5nEnEGDKOncebG5N3CCGmcDcUol/Tp0zsumzZtUidOnHDKm12mTBmn98DIbYyqPXfunGPRGoyKNbmS8bpDhw6VM64YtYXXxSj1sLCwONUBPgdG3VrhOkacW3NyWcuHs1I4Gx0eHh6n9yL7wowFjGpGe0Kbxmgk5J10hVFvkZGRqnLlyk45vKPKyWltM2Y0gHW2g7nNtKOEatMYlY7RjHPmzJHRHLEd0R0V9FN8XmsfQR5OfH6zDYBJkybJaHQsBodyT5061VFufJagoCAZnYqR4jhTjpHbRIkVRn5gUcmQkBAZ3YgZDPh9xDoY5N66w3YPo4cwowSjS/1JXOsOvzmtW7eW+sJ+mj+LT7vDc7A/iP1T/CZiNPKYMWNk9pM/jeqOT91hlBxGo2EEHWZWrlq1SkbJdu3a1aNlJ887cuSI9JsJEybITD+MSB0yZIi6c+eOjOwHO4zyxL4v+jTy72N0J45VmzVrJmW32/bSHHdjJidmKL/88stq6dKl0rdwHODt+sQix5j1gu0kLpgditmi2F6uXbtWZrTaxbZt2yR+gZH7mB2L2b24YNuEuAR4uz4vXLggMwxQPrTNiIgIGSmPYz+75enGrB3AbAjAKF+0SevsHW/XJyB+lSdPHlWrVi2VLFky9cYbb0hbxaLK48aNs0050a9RPtQfFnHGDGqMPF+/fr2aPXu2LbZFuXPnlm06FubGDHjEMrA99faobga6/QSmj6CxWQNVVrgdU2AQtIpNygSk8Jg8ebKkQUFnw0YXDfr27dvSGfFDi2CaueD1Eewy8DzXxl+pUiVJ7YCUJzhgwAbIpC0BTNPAa/Tt21dt2LBBXheBNGzs3QHBPSuU107TWOjpoJ0iDQkWUkIKj4kTJ0rwGgvEJUSbMe07qttMO0qINj1s2DD5QcFU4thOYXNNXxRX6KOYro1FpVavXi3l7tixo1O5MTUeKRgCAwNlWiUOErAzSZQYYScP2xErTNGO6aQVTp6ag3wD1zNmzPjUfTSx1511W4QFPBHkdk0D4w/iWnc4KMZBHKZmIxCBCw5OsKgV/o8DJ38Rn3aH5yB4g5Pd1ufgYPjff/9V/iI+dTd8+HA5gf7JJ5/IoADs6+A4AgfGPBGeuJjgkAnG4vvGNscMosI+MB6DvmT2h70REDHlNIOckJIP6QMRmDML9iKI/ODBA/XVV1/Jcak3Al/mPa3HoNheAwLICHgiIP/ss8+qKVOmSL0jcOvJsrp+5wgg4rcF+//o602bNpUTHkgTimN7pDSxS13i2AkDnXA80717d6lPxDUwAK5x48ZyXOjp9mnKidgKYPAQ2qJprzgJg8FTKCP2Gb0VkI2qPhFzwoKeOMbG7yLSU2K/A6nSkAYWde2t+jTlRApdtFV819ZjVxyz47cMJ2Sxv+TpcqJM1u8S5UX7xLbJejIdg8jQNr/88kuPx6YeupQRg9s++OADGZyKkwZ169aVRboRG/A2Brr9BM741qlTR3YqXUed4CwhRqdgQ2Q6tGtACtfR8Q0chKOT4ew8znYioIUfVYzswAYYI1axobNecFD/JPjxQ1lw9gpn/9BpDJwhwtk25DvGaFyMKD969KjT87Hhf9JKyfgceC0rXEcgDsFP8h9o7zjwwug2nCVH+zG5pQy0M/zwIbeYcePGjcfaXnzEpk3HBHm0sSOJIA9OEsUWDjrWrVsX5X14HdSDtY9gRwuf3xzY4j4EsHGSC30e/ds6Y8PAfcjJuGXLFjlwwMjL2PZTIl+C7YjrGg7oyzjwjA4O+l37IQ4MXGdUJXbxqTvAjjROsOGvdV/Bn8S17nBAjH0160AEjFrDSV78v0qVKspfxKfd4TmYdYhBHdbnYH8VB6L+Ij51h8CC64wzs89th1FzlDAwaAS5Wk0wFt/tM88841g/BkEZtAMcJ2JtGuusR2+V07RDlBeDvgD55NGe0W4xghKjzwcMGCD7tN4qp2v/QTARxyc4dsA+NoLJOH7GPjbW/nHXQLDYfOdQrVo19d5770nucOTjR9Ab6/agHhE38PSMt+jqEscjZu0F1CXWEMN3vmzZMqlXjEQHTwUUreV0HXRnLTuCijhJaGZMeJprfZrv3QQ8kYMfx5SXL1+W41UEZrHGFYL2ntzuu5YT32PatGnluBsZBzCzGmVEcBtxLXz3CN56un3iZDD6MGbdYfAa9jNQXgS6z5w5I9tLAyc5MOsAfR0Dy7xVxjt37kifx2xK0z8wAA/5w7GGH04WevU33quJU8ijkC/nmWee0dWqVZP8TmFhYZI/CbnHkCvUrCKP/DrIA4Wcl8jhiBV9kXd71apVjhzb06dPl5xLWPn1888/12nSpNGXL1+W+9u0aaMLFCigFy5cqE+ePCm5uZCHdNmyZY7nY9X6qBw7dkzy9pUpU0by/Vh9/PHHOl++fLJa96FDh3SnTp2knCaXMnTu3FlyK506dUpW9kaeMtcc3cjNnDRpUj1kyBD5fMgpiPKjXAbqYOzYsU7vjxyWyGVJiSdvPfJc7dixQ3LJIXdkypQpJbeYNUc3oK0VLFhQr1+/Xh84cEByUWfIkEF/9NFHMbYZ17z4aJe4zazmHZs2HR30P+QJR/9Dzm9zMf04Jmj3+Kzvv/++3rt3rz58+LCePHmy9BlA7nHk1sP24eDBg1IfyH9rVp4fP368lBPbBLwWyoDrJs8r+n2/fv1klW3kQEe+/WzZssl7APJ/p0uXTuoB73n//v0nlpnIzkJDQyXvLLYp+B1DG0f//PHHHx2PQZ9o166d4zr6CR6DNSLQBydNmuT0W+sv4lN3eAyegzqzbv/Mau/+Ij5158pfc3THp+5u3bql8+bNK3lx8duIfWnsP+O325/Ep+6wj43nYD8Axw6bN2+WnM3IkUq+D+suIb91kiRJdOnSpSXvbUz5opHzGn3H0/t/sSmnyStr9tUN5BVHbtwbN27YopzIb4/1dvC7h/31nDlzyr548eLFJb+49bN4sozId2yNPWB9IpTblB3HKXhejx49nB7r6XJGRkY6/mLtItw/cuRIp8+A9ZkQOzExDjv1IeRuRrxlzZo1bi9bXOrTwPEv1mgzx46AHP1Y3wExKG+V06wLgb/Ia4915/Ad4y9iRoByDx06VHsC4mTIvY54HOIIWG8O25lRo0Y51uHKmjWr/KZiHS0D/ah69eryO+ytMo51iXuYfoO6w3bI5Gn3Vr5uBrr9DIJOCFrhxxALziHI9sEHHzhtwBGwCw4OlmTy2HHNlSuXBLasGy4sfIcfUwSrXnzxRdnYGthwDBw4UDa+eI/cuXPLQhn79u17YqAbsNOLYCCCilbo0AgAYiEbLKSE4Fr79u2dgoIIuqE8CFzjNRBYdA10w4IFC2QRFJQvf/78jh82ax0w0J24IbCMH8Ds2bPLjy5+4CZOnCj3uQa6sSAlFjE1/QGLt6Kd4kfnaQLdsWnT0TGLurpe8OMTG9jZCAwMlM+eOXNmqQvTR/Cjiu0CfvhxP3akcWBr4MAEi2+hH+O5CJijLkyw5MKFC7KwC/o+AuqoG2wTzM4ano+TBXguymw9yUTkq3Dgjp1A9Bns4E2dOtXpfmxXXPsnfp+w84h+gkWb/LUvxLXu8P+otn94nL+JT7uz8tdAd3zrDielsPAb9jMR9O7Vq5dfLR77NHU3YcIE2fdG3WH/AANjsAAg+b7Ro0fr119/Xc+aNUs3bNhQTnKYwFdUAQ4cZ2K/2sCJDwyQsls5rbcjGIo2jf7u7qBNbMqJwSQBAQGy/cZxBAJe3333na5QoYJj8Xh3LkwZUxnN+7rWk7m9WLFiukuXLm4rW2zLaYKeiDlgm4TjHSvUKeIKOJlgh+/cCsdqiMPMmTPHo4uQPqmcpqzDhw+XY0uradOmycAxs1iht8ppbacYJIFFla0QH0P53Q2DvdAPMFATJ9INLDKK282iqIg7INiNBSmtnn/+eRk0580yRlgWlDbfPf5iu4RBqxjUg4U+TYzFkxjoJiKKI5xRRZAXMxuIiIiIiPwVgkWY4QDjxo2TAVGYMRtdkA4DLRCUxYmOBg0ayGwm83g7ldPAbOCaNWtKmT0hpnJaA5rly5eXk0qYWQEYuIbrmN1sHf1pp7rE4DiUD7NZPSGmclpHlH/11Vcy6AgnL48cOaKPHz+u69Sp4zSoyU71ie8XgeT33nvPI+WLazkHDBggJ4Yw4AuBZbRR1GfHjh09EpSPbR9yhcGQaJ8I0LobgsiDBw+W7QuYoDFGmrvOdqpVq5Z+5ZVX9Ny5c6XdYpQ1gsnuHtF/KQ5ltPYpzJTHYEIMKMVsLpzo9jQGuomIngBnenHGHDs92NBjxDUC3SbVBxERERGRv/vnn39kVnCjRo1khp9rYAmzb5EODwFuzGbCPrUnUkPEtZwIdv7++++Szg+zmBFQ9MbsjajKaYJNmInpGrTDrBPryEtvldFaLsyixUzSnj17ynePUajWkaDeLKcpB0Ztz549W0bOYrYKvnOMWvV0XUZXzqiCs5gRiJkx3koBGVN9Ir0X0nxh9jwC8qhPzIrALGm71Sf60ZIlSySdDmZH9O/fXwK2nki3YU35Yt4PJ6vMSG1rfWIGNdLv4gRC6tSpZXa1J777yCeUMaoMEl27dpW0MTixEZu0qu7AxSiJiGJh1KhRsnBF7dq1ZfEFLKiBBXbcJSwsTKVPnz7aC+6PToMGDaJ9XkhIiNvKTERERET+CQuSYWFWLFiGRQi/++67xxb/w+LlWKTs5s2bshgcFi3Lli2b7cqJxd9Gjx4tCyhioWgsTpcmTRpblNMsUpg5c2ZHmc2Cb8WLF5f9fTvV5d69e9WXX36pdu/erVauXKnGjBkT5UKL3iinKUemTJlUu3bt5HvHAn/btm1T8+bN82hdxrY+Hz58KH/79eun+vbtq1KlSuXRMsamPkuUKCGLO2Ih0tdee036+pw5c2RxRTvWZ2hoqCzWvXbtWjlWxiK17l7kE33WuoCreb/jx4+rcuXKyf9xv6nPSZMmSfyhU6dOasuWLdJO3f3d61iU0XWxyfHjx8t2ffv27WrGjBmyuKdXeCW8TkREMTLTvKK7uC76YYWpoNE9z1tnVYmIiIjIt2B9GYzOi2qhZNd9UXMdI58xAhojD7HoOWDxdwgPD9erV6+2fTmRHxcLp9q1nCaVgDvSQCRUGc36Pkiz4Y68zAlVzp07d8pfd6XUSOjv3F2LeCZ0H3IXd3zv7hi5H5dymu8Uz8mQIYNjbTuThsVdTrmhjN6YBREVjugmIrIhnD197rnnor2YM7xRCQgIiPZ5XjurSkREREQ+47PPPlPPP/+8unTpkrp7965j5J75a0b6zZo1y3EdIygx8rlly5ZyHaMjMdOwcuXK6uzZsyp79uyqTp06ti7nuXPnVNq0aVWhQoVsW86KFSuq8+fPO41MtVsZq1SpInWZLl06VaRIEduWs1KlSm6pS3d95xhtbOdyog+hnO7gru89oUfux7Wc5jvFjIfChQur0qVLy/YSZX799dfVtWvXErR87iyjp2dBRMvbkXYiIiIiIiIisod169bJopFRjfQzpk6dKgv4NWzYUF+8eNHpPlwvWbKk5Glt2rSp5G1lOe1dn/zOWZ92bZuJuX1ilouBvNdYFyAkJESnSZNGFqHETG1/LOPTYqCbiIiIiIiIiES7du3kAlu3btUDBgzQM2bMcKSgmDdvng4ICNDffffdY6kU8Hgs6le8eHG9efNmt9Yoy8m6ZNtkH0oM2ySkSypQoIAE4osWLeqWFE++VManlQT/eHtUORERERERERF5D6b5379/X6aiBwUFybT2r776Sr300kuyWBvuw8KMWGAO092R4sMVFm1fvHixatu2LcvpA/XpC2VkOVmfib19YrHMjz76SNWvX1+1bt3ab8uYUBjoJiIiIiIiIvIzw4cPV+Hh4ap48eKqY8eOKmXKlHI78mg/evRI5c+fX7Vv315Vr15dcra+8cYbKiIiQoIjZcuWfez1MIYuSZIkLKeN65PfecJifbI+n7av+3MZ3cbbQ8qJiIiIiIxnn31Wjx07NtFUyIYNGzB7Ul+7dk37kg4dOug33njDcb1GjRqSlzG2Tp06JZ/7r7/+StBymdfFpWzZstqTUAfmvRP6cxF50pEjR3SJEiV06dKldcuWLXWWLFl0zZo19ZYtW+T+uXPn6hQpUuh8+fI55V/dtWuXzp07t+R4hf/++4/l9JH69IUyspysT7ZP/+lD7pTwS8sSEREREbn4559/1DvvvKPy5Mkjo0qeffZZ9eGHH6orV64kmrqqWbOmTOu0CgwMVOfPn1eZMmVSvmzRokVq6NChsX58vnz55HOXKlVKrm/cuFFGA12/fj1ByrN27Vq1bt065ek6CA0N9eh7ErnD8uXLZZu0e/duNW/ePHXo0CF17do1NXbsWBUWFqZq1aol2zOM8sPIPzOir1y5curBgwfq9OnTcpu7R/ixnKxLtk32IW6TEud2050Y6CYiIiIitzp58qSqWLGiOnbsmJo7d646fvy45AFEoBK5AZH3z1uwk4+8he6CoH6uXLl8+oABsmbNqjJkyBDrxydLlkw+Nw6k3CFbtmxy8XQdZM+e3aPvSZTQHj58qA4ePKhy5Mgh/RTQVwcMGCBBkGnTpsl9vXv3VhcvXlQTJ06UE5XYhq1YsUI999xzMvXd3VhO1iXbJvsQt0mJc7vpbgx0ExEREZFbde/eXQK+q1evVjVq1JC8gA0aNJBRuWfPnpUdcKtbt26pVq1aqXTp0qmAgAA1adIkx30YdTJ48GB5jVSpUskI8Z49ezrux2iUPn36yPPw/CpVqshoYmPmzJkqc+bMasmSJapEiRLyGtOnT1epU6d+bLQxRpxj5Atg5DnKhNfFAj2lS5eWoL2BhX02bdqkxo8fLwcMuGBUTFQjmRcuXKhKliwp712gQAE1evRop/fFbSEhITICHsFlfNapU6fGqc7PnDkjCwplyZJF6gHvh4MYE9x/9913VcGCBVWaNGlUsWLFpNxxGa3+pDLis+Nz79mzR/7/yiuvyO0oD25Hfc2ePVuC1fjOrJo0aaLatWsXp8+L18PzgoODJRidMWNG1bVrV8k3aSxYsEC+N3xmvG/t2rVlkaqoPp8pB16XKDHBySf0uXv37slJPjOir0WLFnJCcvPmzWrfvn2qXr16asKECWrOnDmyHWzevLl6++23pd9gO8hy+k59+kIZWU7WJ9un//Qht3NrYhQiIiIi8mtXrlzRSZIk0SEhIVHe37lzZ8kfaHIBIkd3hgwZ9PDhw/Xff/+tJ0yYoJMlS6ZXr14t9//yyy86Y8aMesWKFfrMmTN6+/bteurUqY7X69Spkw4MDNS///67Pn78uB45cqROlSqVPnr0qNz//fffS25CPObPP/+UXIa3b9/WOXPm1NOnT3e8zsOHD51uQx5DvBZyM584ccJRLrw/XL9+Xb/00kvyec6fPy8XvIZrju6dO3fqpEmT6iFDhsjnQ3nSpEkjfw3UQdasWfWkSZP0sWPHpC7wHJQ1tho1aqTr1Kmj9+3bJ+VdunSp3rRpk9wXERGhBw4cqHfs2KFPnjypf/zxR502bVo9f/78WOfoflIZrTm6UQ8LFy6U6/jMqBvU1927d3WmTJn0zz//7Hjdixcv6uTJk+v169fHKfc3yps+fXrJR3ngwAG9bNkynT17dv3ZZ5/J/efOnZPXHTNmjLwG6gVlv3XrVpSfD/D58bqxeX8iX4C+CNguob+adhwZGSl/N27cqAsXLuy0LcB24ttvv9V9+/bVe/fuZTl9rD59oYwsJ+uT7dN/+pAnMNBNRERERG6zbds2CQwuXrw4yvsReMT9CHCaAGr9+vWdHoPgZYMGDeT/o0eP1kWLFpVgrSsEvhF8Pnv2rNPtr776qu7fv7/8HwFlvN+ePXucHoMgZ61atRzXf/vtNwmQx7SIJILJvXv3dlyPKljqGuhu3bq1BKCtPvnkE1k4yEAdtG3b1nEdJwFy5Mihp0yZomMLixANHjw41o/v3r27btasWZwC3TGV0TUgHN2inO+//77juzXfb6FChaJdBCmmQDcC73fu3HHchrIg+P3o0SNZZAnPO336dJSvy0A3JRYmqBHTfffu3ZM2X7t2bblu7W8IhOBEHMvpO/XpC2VkOVmfbJ/+04e8jalLiIiIiMgTswhj/Vjk7Xa9fvjwYcf0S0zJLFSokOrcubNavHix5CSE/fv3yzTNokWLqvTp0zsuSCly4sQJx+shjUqZMmWc3qNNmzaSZuTcuXNy/aefflKNGjWSNCeA18VijEh9gVzNeN3ffvtNch7GBT5H1apVnW7DdeQvN1NMwVo+pPpAjsXw8PBYvw/SuQwbNkxee9CgQTJV1QrpYCpUqCBpPvBZkHYkrp/lacsI+A6R0gYpbExqGaQLiU9O87Jly0paGWu7uX37tuSfxH2vvvqqfH9oQ8hTicWZiBILpOn59NNPVZcuXVSvXr1kbQTDbCPN4mM3btyQND/YNmK9BLN9Rp9AqiNs41hO+9cnv3PWp13bJtunf9alXTDQTURERERug4VtELQ0gWpXuB15m2O7yF++fPnU33//rSZPniy5lrt166aqV6+uIiMjJaiJxXd27doluaHNBe9hzUGN57kGUitVqqQKFy4sK9QjkI4AOoLfxsiRI+U1+vbtqzZs2CCvixyH1hzQCSlFihRO11HeuCya2alTJzkQQq5rnABAbkYsOgT4jMhjjjzdCDLjs3Ts2DHOn+VpywjlypWTIDTydeN7wyJK7siLjXaxZs0atXLlSsnNjrpAbvJTp07J/UmTJn3sZAzaFJEv+OWXXyTn/s6dO1XevHnV/PnzJUf9li1b5H6zKCxysuJk0KpVq2S9BJwEw+W9995Tf/zxh5zMwxoJOCnEctq7Pvmdsz7t2jbZPv2zLm3F20PKiYiIiChxq1u3rg4ICJCczFbI1Yzc0F27dnVKiWFNZQFvv/32Y7cZyAmNXVqkpkD+Z/wf+bmjg9QlyAsdFaT6KF++vOQvxGPu37/vuK9x48b6nXfecVxHOowiRYo4pfdASpIePXrEK3VJyZIlnepg7NixTo8pW7asHjRokI6vfv36SToTQBmtaVpMehe8R1xSl8RURtcUI8iHjuuXL19+rGyTJ0+WdDRIn4K2EpMnpS6xtrH//e9/jtQlUeWyRJtEqhR46623dIsWLZzuz58/P3N0k+2hL2D7iDz5RlhYmC5YsKCeM2eOXEdO/DZt2ug8efLoWbNmOU1lx3oD1apVk+0D+rBZd4DltG998jtnfdq1bbJ9+mdd2g0D3URERETkVlgI8plnnpGdbSyIiJ30lStX6lKlSkmwGAtWWgOoWGxyxIgRErj+5ptvJO/2qlWrHIFqLBC5f/9+WWTx888/l8UcTQAVO/sFChSQxQ+x0CJ26rEQJhYnfFKgG4sqIohapkwZ/e677zrd9/HHH+t8+fJJwPbQoUOy6CXKaQ0GYyHKSpUqSTD20qVLEmB1DXQjIG9djHLmzJlRLkb5tIFuBKVRZ6gDvGeVKlUkmAvjx4+XsuN+lAF1iOvuDHRjMU8sSorPGx4e7lgE0hyk4YRHypQp9bx582L8XE9ajLJVq1b64MGDevny5bKYKAL8Jlf8l19+KQsvIZc7FsDE+2FRUxMURxnQTg4fPizfJeqEi1GS3WEbh7UCzNoEZv0CnLRD3zY5W0NDQ/WNGzccz7OeAML/sa1gOX2jPn2hjCwn65Pt03/6kN0w0E1EREREbodFABE0RPAxRYoUEjT+4IMPHhvhiwBqcHCwjK5F4DFXrlwSmDWwqCWCtghCpkuXTr/44ot67dq1jvtxEDBw4EAJduN9cufOrZs2bar37dv3xEA3VK5cWQKp69evd7odwXgEfhFMxaKLOMBo3769UzAYQWOUB4FrvAaCslEtwrhgwQJZfBLlw6jhkSNHPlYHTwp0I/DsGoS1wqhtLDqEBTWzZ8+u27Vr56hrjFQPCgqSesicObMsCImAsDsD3YDgPr5PBLxdy47yYUS2dRR9XAPdKC+++2zZssn3hGC1eT2cnKhXr57UBeoEI8gnTpzo1G5QDygDvl+MnsLrMdBNdvPLL7/oNWvWPLborhVOHhUrVkxOKHoLy8m6jArbJvuQt/jCNskXyugLGOgmIiIiIvIxCJBbR4H7OqRSwYmPJ3lSoNvdont/InebPXu2nITByTicsKlatapetGiR3Iep6tYRfJi1gNkyx48f9/gXw3KyLtk22Ye4TUqc201fwcUoiYiIiIh8CBZszJQpk2rfvr3yddeuXZOFPzdu3Ki6d+8e6+cFBgbKxZMaNGigSpYs6dH3JHr48KEshDt8+HAVEhIii4r9+uuvsnjutGnT1IMHD2QhWOuCqlgwF//PkyePowKvXr0qf10XXU0oLCfrkm2TfYjbpMS53fQ1DHQTEREREfkQBFv37dsnBz6+rly5ciooKEiNGDFCFStW7ImPz5s3rzp27Jh8/vnz5ytPmj59utq7d6+8f4kSJTz63uS/7ty5oy5duqQ6dOigOnbsqFKmTCknedAGb968KYESAwER+L//+z/VuHFjlSZNGrVnzx5Vt25dNXToUAmCmMewnPatT37nrE+7tk22T/+sS1/j+3vHRERERETkk06fPq1u3Lih+vTpE6vHJ0+eXD333HNyyZcvn+P2mTNnyigodwoICHC8Nw5GidwFJ1PMyDzM3mjevLn0EZzc+u+//+R2tH8ESVKkSOH0XNyGPlWlShXVrVs3VbFiRZUjRw719ddfJ3gQhOVMuPpkXbJtsq/7Rz/yhTL6PG/nTiEiIiIiIiLyd/Pnz5eFdLHQGHK1Tp8+3el+a57W1q1by6KyEBkZ6bh9z549suArLlgcFwuxspz2rU9+5wmL9cn6ZF8nBrqJiIiIiIiIvGj16tUS5J40aZJetWqV7tWrl06RIoWeOnWqvnfvnmNRMlxwvUyZMvqHH3547HV+//13XbNmTb1mzRqW0+b1ye+c9WnXtsn26Z91mVgw0E1ERERERETkBQhsQHBwsK5QoYKOiIhw3NetWzddsWJFvWjRIqfnnD17VoImR48elev4+9FHH7GcPlKfvlBGlpP1yfbpP30osWGObiIiIiIiIiIvMHlVDx06pAoXLiw5WSMjI+W2YcOGqdSpU8viYxcuXHA8Z+3atZLDNXfu3OrDDz+UhcvCwsLkeSbHK8tp3/rkd876tGvbZPv0z7pMbJJ7uwBERERERERE/mDNmjVq6dKlqlChQiowMFBVrlxZbn/11VdV79691aNHjxzBkCxZsqj27durUaNGqSNHjqhcuXLJImbLli1TBw4cUAUKFJDbtm7dKouSsZz2rE9+52yb7Ov+0Y98oYz+gCO6iYiIiIiIiNzo/Pnz6rXXXlNt27ZVV69eVTNmzFB169ZVoaGhcn+NGjVUxowZVXBwsFxHwAM6d+6sbt68qfbs2SPX7927J5d06dKpSZMmSUAkIYMgLGfC1Sfrkm2Tfd0/+pEvlNGveDt3ChEREREREVFidefOHd2hQwfdsmVLffLkScftlStX1kFBQfL/mzdv6mHDhuk0adLosLAwp/yuNWrU0J06dXI8b+fOnSynzeuT3znr065tk+3TP+vSn3BENxEREREREZGbpE2bVqVKlUoFBQWpggULqocPH8rtDRs2VIcPH5bRfRkyZFCtW7dW5cuXV2+99ZY6c+aM5HdFbtbw8HDVpEkTx+tVqFCB5bR5ffI7Z33atW2yffpnXfqTJIh2e7sQRERERERERIkVcrIiNytgQbGkSZOqNm3ayBT1qVOnOh539uxZVbNmTQmWYMr6li1bVPHixdWcOXNUzpw5WU4fqk9fKCPLyfpk+/SfPuQvGOgmIiIiIiIi8rCXX35ZcrR26NBBgiOAAMnx48fVrl271Pbt21XZsmXlfm9iOVmXbJvsQ9wmJc7tZmLEQDcRERERERGRB508eVIFBgaq5cuXO6aqR0REqJQpU9rqe2A5WZfAtsk+ZBe+sE3yhTImZszRTUREREREROQBJnPo5s2bVfr06R1BkODgYPXhhx9KvlY7YDlZl2yb7EPcJiXO7WZil9zbBSAiIiIiIiLyB1iADEJDQ1WzZs3UmjVrVJcuXdTdu3fVDz/8oHLkyKHsgOVkXbJtsg9xm5Q4t5uJHVOXEBEREREREXnI/fv3VenSpdWJEydkKjtG+/Xt29d29c9ysi7ZNtmH7MQXtkm+UMbEjoFuIiIiIiIiIg+qU6eOKlKkiBozZoxKnTq1beue5WRd2hXbJuvTrttOX2mbiRUD3UREREREREQe9OjRI5UsWTLb1znLybq0K7ZN1qdd+UrbTKwY6CYiIiIiIiIiIiIin5bU2wUgIiIiIiIiIiIiInoaDHQTERERERERERERkU9joJuIiIiIiIiIiIiIfBoD3URERERERERERETk0xjoJiIiIiIiIiIiIiKfxkA3EREREREREREREfk0BrqJiIiIiIiIiIie0saNG1WSJEnU9evXWZdEXpBEa6298cZERERERERERES+qmbNmuqFF15Q48aNk+sRERHq6tWrKmfOnBLwJiLPSu7h9yMiIiIiIiIiIkp0UqZMqXLlyuXtYhD5LaYuISIiIiIiIiIiioOgoCC1adMmNX78eBm9jcvMmTOdUpfgeubMmdWyZctUsWLFVNq0aVXz5s3V3bt31axZs1SBAgVUlixZVM+ePdWjR48cr/3gwQPVp08fFRAQoNKlS6eqVKkiaVGIKGYc0U1ERERERERERBQHCHAfPXpUlSpVSg0ZMkRuO3jw4GOPQ1B7woQJat68eerWrVvqzTffVE2bNpUA+IoVK9TJkydVs2bNVNWqVVXLli3lOT169FCHDh2S5+TJk0ctXrxY1a9fX+3fv18VKVKE3xNRNBjoJiIiIiIiIiIiioNMmTJJqhKM0jbpSo4cOfLY4yIjI9WUKVNU4cKF5TpGdP/www/q4sWLKn369KpEiRLqlVdeURs2bJBAd1hYmPr+++/lL4LcgNHdq1atkttDQkL4PRFFg4FuIiIiIiIiIiIiN0Ag3AS5AQtVImUJgtzW28LDw+X/GLWNNCZFixZ1eh2kM8mWLRu/I6IYMNBNRERERERERETkBilSpHC6jhzeUd3233//yf9v376tkiVLpnbt2iV/razBcSJ6HAPdREREREREREREcYTUJdZFJBNCuXLl5DUxwrtatWr8TojiIGlcHkxERERERERERERKUpBs375dnT59Wl2+fNkxKvtpIGVJmzZtVPv27dWiRYvUqVOnVGhoqBo+fLhavnw5q50oBgx0ExERERERERERxREWiUR6ESwomT17dllAMiFg0UkEunv37q2KFSummjRponbs2KHy58/P74goBkm01jqmBxARERERERERERER2RlHdBMRERERERERERGRT2Ogm4iIiIiIiIiIiIh8GgPdREREREREREREROTTGOgmIiIiIiIiIiIiIp/GQDcRERERERERERER+TQGuomIiIiIiIiIiIjIpzHQTUREREREREREREQ+jYFuIiIiIiIiIiIiIvJpDHQTERERERERERERkU9joJuIiIiIiIiIiIiIfBoD3URERERERERERESkfNn/A9MdttQiSI3aAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1800x1200 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(2,3, figsize=(18,12))\n",
+    "_ = cmp.plot(ax=ax[0,0])\n",
+    "_ = cmp.plot.hist(ax=ax[0,1])\n",
+    "_ = cmp.plot.kde(ax=ax[0,2])\n",
+    "_ = cmp.plot.box(ax=ax[1,0])\n",
+    "_ = cmp.plot.qq(ax=ax[1,1])\n",
+    "_ = cmp.plot.timeseries(ax=ax[1,2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e0f6a98c",
+   "metadata": {},
+   "source": [
+    "**Select specific depth interval to validate**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "1c192f8c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>n</th>\n",
+       "      <th>bias</th>\n",
+       "      <th>rmse</th>\n",
+       "      <th>urmse</th>\n",
+       "      <th>mae</th>\n",
+       "      <th>cc</th>\n",
+       "      <th>si</th>\n",
+       "      <th>r2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>observation</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>deep</th>\n",
+       "      <td>63</td>\n",
+       "      <td>-0.194522</td>\n",
+       "      <td>0.205873</td>\n",
+       "      <td>0.067416</td>\n",
+       "      <td>0.194522</td>\n",
+       "      <td>0.895853</td>\n",
+       "      <td>0.010336</td>\n",
+       "      <td>-0.841364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>surface</th>\n",
+       "      <td>45</td>\n",
+       "      <td>-0.201304</td>\n",
+       "      <td>0.214428</td>\n",
+       "      <td>0.073865</td>\n",
+       "      <td>0.201304</td>\n",
+       "      <td>0.662412</td>\n",
+       "      <td>0.011912</td>\n",
+       "      <td>-3.729550</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1m</th>\n",
+       "      <td>9</td>\n",
+       "      <td>-0.187027</td>\n",
+       "      <td>0.197700</td>\n",
+       "      <td>0.064078</td>\n",
+       "      <td>0.187027</td>\n",
+       "      <td>0.548066</td>\n",
+       "      <td>0.010388</td>\n",
+       "      <td>-6.162230</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              n      bias      rmse     urmse       mae        cc        si  \\\n",
+       "observation                                                                   \n",
+       "deep         63 -0.194522  0.205873  0.067416  0.194522  0.895853  0.010336   \n",
+       "surface      45 -0.201304  0.214428  0.073865  0.201304  0.662412  0.011912   \n",
+       "1m            9 -0.187027  0.197700  0.064078  0.187027  0.548066  0.010388   \n",
+       "\n",
+       "                   r2  \n",
+       "observation            \n",
+       "deep        -0.841364  \n",
+       "surface     -3.729550  \n",
+       "1m          -6.162230  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Compare skill for different depth ranges\n",
+    "cmp_deep = cmp.where(cmp.data['z']<-10).rename({cmp.name: \"deep\"})\n",
+    "cmp_surface = cmp.where(cmp.data['z']>=-10).rename({cmp.name: \"surface\"})\n",
+    "cmp_1m = cmp.where(cmp.data['z']==-2).rename({cmp.name: \"1m\"})\n",
+    "\n",
+    "# Comparer collection to compare skill across different depth ranges\n",
+    "cmps = ms.ComparerCollection([cmp_deep, cmp_surface, cmp_1m])\n",
+    "cmps.skill()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b88ae8ad",
+   "metadata": {},
+   "source": [
+    "**Plot verical profile at specific time**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a73645ac",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "avail_times = cmp.time.unique()\n",
+    "data  = cmp.data.sel(time=avail_times[0])\n",
+    "z = data['z'].values\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 1, figsize=(8, 6), sharey=True)\n",
+    "ax.plot(data[cmp.mod_names[0]].values, z, 'o-', linewidth=2, label=cmp.mod_names[0])\n",
+    "ax.plot(data['Observation'].values, z, 'o-', linewidth=2, label='Observation')\n",
+    "ax.set_xlabel('Value')\n",
+    "ax.set_ylabel('Depth (m)')\n",
+    "ax.legend()\n",
+    "ax.grid(True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "modelskill",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.14.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/vertical_skill_temporary.ipynb b/notebooks/vertical_skill_temporary.ipynb
new file mode 100644
index 000000000..8893e9fba
--- /dev/null
+++ b/notebooks/vertical_skill_temporary.ipynb
@@ -0,0 +1,1768 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "568821f0",
+   "metadata": {},
+   "source": [
+    "### Test implementation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4376a422",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import modelskill as ms\n",
+    "import pandas as pd\n",
+    "from pathlib import Path\n",
+    "import mikeio\n",
+    "\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3695d1d4",
+   "metadata": {},
+   "source": [
+    "**Read obs from csv**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7e1e20ff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load obs from csv\n",
+    "csv_file = Path(\"../tests/testdata/vertical/VerticalProfile_obs1.csv\")\n",
+    "metadata = pd.read_csv(csv_file, nrows=2, header=None)\n",
+    "xpos = float(metadata.values[0, 0].split(\":\")[-1].lstrip())\n",
+    "ypos = float(metadata.values[1, 0].split(\":\")[-1].lstrip())\n",
+    "obs_1 = pd.read_csv(csv_file, sep=\",\", comment=\"#\", index_col=0, parse_dates=True)\n",
+    "vo = ms.VerticalObservation(\n",
+    "    obs_1,\n",
+    "    item=\"salt\",\n",
+    "    z_item=\"z\",\n",
+    "    x=xpos,\n",
+    "    y=ypos,\n",
+    "    quantity=ms.Quantity(\"salinity\", \"psu\"),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "84af2662",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "vo.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b261605",
+   "metadata": {},
+   "source": [
+    "from https://docs.xarray.dev/en/stable/whats-new.html:\n",
+    "\n",
+    "\"Calling Dataset.sel() or DataArray.sel() on a 1-dimensional coordinate without an index will \n",
+    "now automatically create a temporary PandasIndex to perform the selection (GH9703, PR11029).\" "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e30b8878",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Fk+BgCYkodN/3hYAEAAB8Qvp8+SR84IAbISk42Czr+vvtgfv+HQEAAJKRo0ULyVy7tulW05Yjb4QjRUACAAA+JX2+fF4LRj7TxXbs2DFp166d5M6dWzJmzCjlypWTrVu3Jrv9nDlzpGHDhpInTx7Jli2b1KhRQ5YuXXrTdv/85z+lcOHCkiFDBqlWrZps3rzZ4/GrV69K165dzffNkiWLNG/eXE6ePHlPagQAAP7FqwHp7NmzUqtWLUmfPr0sXrxYoqKiZMSIEZIzZ85kn7N27VoTkBYtWiTbtm2TevXqyXPPPSc7duxwb/PVV19Jz549pX///rJ9+3YpX768NG7cWE6dOuXepkePHrJgwQKZNWuWrFmzRo4fPy7NmjW75zUDAADfF2RZluWtb/7OO+/I999/L999991dvU6ZMmWkdevW0q9fP7OsLUZVqlSRzz77zCwnJCRIwYIF5Y033jDf89y5c6YFatq0adKiRQuzzS+//CKlSpWSDRs2SPXq1W/7Pc+fPy/Zs2c3r6UtWQAAwPel9O+3V1uQ5s+fL5UrV5aWLVtK3rx5pWLFijJhwoRUvYaGnwsXLkiuXLnMcnx8vGlZatCggXub4OBgs6zhR+nj165d89imZMmSUqhQIfc2AAAgcHk1IB04cEDGjh0rxYsXN+OIOnfuLN26dZPJkyen+DWGDx8uFy9elFatWpnlM2fOyPXr1yUsLMxjO12O+f8zcep9SEiI5MiRI9lt7OLi4kzqTHwDAADO5NWz2LT1R1uQBg8ebJa1BWn37t0ybtw46dChw22fr11kAwYMkHnz5pkWqHtpyJAh5nsBAADn82oLUnh4uJQuXdpjnY4Dio6+/ZTiM2bMkI4dO8rMmTM9usoefPBBSZcu3U1npOlyvv9/yqDea1dcbGxsstvY9enTx/RXum5HjhxJVa0AAMB/eDUg6Rlse/bs8Vi3d+9eiYiIuOXzpk+fLi+//LK5b9Kkicdj2nX22GOPycqVKz1aqnRZpwRQ+rieOZd4G90PDWaubexCQ0PNYK7ENwAA4Exe7WLTU+1r1qxputh0DJHOVTR+/HhzS9xyo3MlTZkyxd2tpt1vo0ePNmerucYM6RxKOipd6Sn+uo1231WtWlVGjRolly5dMqFK6XavvPKK2U4Hd2vY0TPcNByl5Aw2AADgcJaXLViwwCpbtqwVGhpqlSxZ0ho/frzH4x06dLDq1KnjXtavdbftN90usU8//dQqVKiQFRISYlWtWtXauHGjx+NXrlyxunTpYuXMmdPKlCmT9cILL1gnTpxI8X6fO3fOfF+9BwAA/iGlf7+9Og+SP2MeJAAA/I9fzIMEAADgiwhIAAAANgQkAAAAGwISAACADQEJAADgTuZBqlSpkqRGUFCQuRBtgQIFUvU8AAAAvwlIO3fulF69ekmWLFluu63OGjB06FBzcVcAAABHz6T9j3/8I8UXhB0xYsTd7BMAAIDvB6SDBw9Knjx5UvyiUVFRkj9//rvZLwAAAN8OSLe7eKxdwYIF73R/AAAA/O9itWvXrr3l40888cTd7A8AAID/BaS6desmedaay/Xr1+9+rwAAAPxpHqSzZ8963E6dOiVLliyRKlWqyLJly+7NXgIAAPhyC5JeAdeuYcOGEhISIj179pRt27al1b4BAAD490zaYWFhsmfPnrR6OQAAAP9pQdq1a9dNE0OeOHHCTA5ZoUKFtNw3AAAA/whIGoJ0ULYGo8SqV68uX3zxRVruGwAAgH8EJJ00MrHg4GAziWSGDBnScr8AAAD8JyAlNWlkbGwsAQkAAATuIO2PPvpIvvrqK/dyq1atJFeuXFKgQAH54Ycf0nr/AAAAfD8gjRs3zn0pkeXLl5ubzoP09NNPmwvaAgAABFwXW0xMjDsgLVy40LQgNWrUSAoXLizVqlW7F/sIAADg2y1IOXPmlCNHjpivteWoQYMG5ms9q43LjAAAgIBsQWrWrJm0adNGihcvLr/99pvpWlM7duyQYsWK3Yt9BAAA8O2ANHLkSNOdpq1Iw4YNkyxZspj1Ollkly5d7sU+AgAA3FdBln3GR6TI+fPnzXXpzp07J9myZeOnBgCAg/5+p7oFSek11z799FP5+eefzXKpUqXkjTfekEceeeTO9xgAAMBfB2l//fXXUrZsWdm2bZuUL1/e3LZv327W6WOpdezYMWnXrp3kzp1bMmbMKOXKlZOtW7cmu7125ekYqBIlSphZvLt3737TNnXr1jWXQ7HfmjRp4t4mMjLypsefeuqpVO8/AABwnlS3IL311lvSp08fGThwoMf6/v37m8eaN2+e4tc6e/as1KpVS+rVqyeLFy82lyzZt2+fOVMuOXFxcWa7vn37mvFQSZkzZ47Ex8e7l3UwuQa5li1bemyngWjixInu5dDQ0BTvOwAAcK5UByRtwWnfvv1N67UV6OOPP071rNw6p1LikFKkSJFbPkcHiI8ePdp8ndzFcXVm78RmzJghmTJluikgaSDKly9fqvYZAAA4X6q72LT76rvvvrtp/bp16+Txxx9P1WvNnz9fKleubIJL3rx5pWLFijJhwgRJa59//rm8+OKLkjlzZo/1q1evNt9Xx0517tzZtDTdquVKB3YlvgEAAGdKdQvSX/7yF3n77bfNGKTq1aubdRs3bpRZs2bJgAEDTOhJvO2tHDhwQMaOHSs9e/aUd999V7Zs2SLdunWTkJAQ6dChg6SFzZs3y+7du01Isnev6ZxO2mL166+/mu+vczpt2LBB0qVLd9PrDBkyxNQHAACcL9Wn+evA6BS9cFDQbWfW1iCkLUjr1693r9OApEFJg0pKWrMqVKggo0aNSnabv//97+a1du3adduwVrRoUVmxYoXUr18/yRYkvbloC5J2D3KaPwAAzjvNP9VdbAkJCSm6peSyI+Hh4VK6dGmPdTplQHR0tKSFS5cumfFHr7zyym23ffjhh+XBBx+U/fv3J/m4jlfSH2TiGwAAcKZUB6S0pGew6ZxKie3du1ciIiLS5PW1209bfXQA+e0cPXrUjEHS0AYAAAJbigLSmDFj5OrVqyl+0XHjxsmFCxduu12PHj3M+KXBgweblptp06bJ+PHjpWvXru5tdEoB+1lzO3fuNLeLFy/K6dOnzddRUVE3vb6OO2ratKmZYykxfd4//vEP870PHTokK1eulOeff95cS65x48YprhMAAATwGCQdtBwTE2PmH0oJ7X7S0KLdVrezcOFCE4J0/iMdMK0Dtjt16uQxoaOGGD3jzL3TQUE3vY62Oul2LtoyVbJkSVm2bJk0bNjQY9srV66Y4KQX2I2NjZX8+fNLo0aNZNCgQRIWFpaiGrnUCAAA/ielf79TFJB0YLbOlP3AAyk76e3HH380ASUlAclfEZAAAAjwa7HpLNmpod1V9skaAQAAHHuaP/5ECxIAAP7nnp3mDwAA4HQEJAAAABsCEgAEsGsxMXJp4yZzD+AGAhIABKjY2bNl/5P1JToy0tzrMoA7DEirVq1K7VMAAD5GW4xO9Ouv14/6c0VCglmmJQm4w4D01FNPmYu6fvDBB3LkyJHUPh0A4APiDx2+EY5cEhIk/nDaXAsTCLiAdOzYMXn99ddl9uzZZiJIvTTHzJkzJT4+/t7sIQAgzYUUjtBZgD1XBgdLSEQhftrAnQQkveK9XkNNLyWyadMmKVGihHTp0sVcrqNbt27yww8/8IMFAB+XPl8+CR844EZICg42y7oeQBpMFHn8+HFzgdmhQ4eaS5HoRW1r1KhhLlhbpkwZx/6MmSgSgBPomCPtVtOWI8IRAsH5ezlR5LVr10wX2zPPPGMuErt06VL57LPP5OTJk7J//36zrmXLlnez/wCA+0BDUeZqVQlHwN22IL3xxhsyffp00af99a9/lY4dO5oL2SYWExNjutwS7AMAHYQWJAAAAvxitYlFRUXJp59+Ks2aNZPQ0NBkxykxHQAAAPBXqe5i69+/v+k+s4ejP/74Q9auXWu+1rFIderUSbu9BAAA8OWAVK9ePfn9999vWq9NVfoYAABAwAUkHXsUFBR00/rffvtNMmfOnFb7BQAA4DUpHoOkY46UhqPIyEiPLrbr16/Lrl27pGbNmvdmLwEAAHwxIOmIb1cLUtasWSVjxozux0JCQqR69erSqVOne7OXAAAAvhiQJk6caO4LFy4svXv3pjsNAAA41l3PpB2omAcJAIAAnwepUqVKsnLlSsmZM6dUrFgxyUHaLtu3b7+zPQYAAPARKQpIzz//vHtQdtOmTe/1PgEAAHgVXWx3iC42AAD8zz271IhLfHy8nDp16qbrrRUqVOhOXxIAAMAnpDog7d27V1555RVZv359khNI6pxIAAAAATWT9ssvvyzBwcGycOFC2bZtmxmUrbcdO3bc0QDtY8eOSbt27SR37txmbqVy5crJ1q1bk93+xIkT0qZNGylRooTZj+7du9+0zaRJk0xYS3zLkCHDTYGuX79+Eh4ebr5vgwYNZN++fanefwAA4DypbkHauXOnCUYlS5a8629+9uxZqVWrlrmG2+LFiyVPnjwmpOjZcsmJi4sz2/Xt21dGjhyZ7Hbar7hnzx73sv3Mu2HDhsmYMWNk8uTJUqRIEXnvvfekcePGEhUVdVOYAgAAgSXVAal06dJy5syZNPnmH330kRQsWNA9CaXSsHIrOlHl6NGjzddffPFFsttpIMqXL1+Sj2nr0ahRo0zI0jP01JQpUyQsLEy++eYbefHFF++wIgAAEJBdbBpq3nrrLVm9erW5QK2OBk98S4358+dL5cqVpWXLlpI3b14zx9KECRMkLVy8eFEiIiJMANMQ9NNPP7kfO3jwoMTExJhuNRcd0V6tWjXZsGFDsi1Xd1MrAABwcEDSULFx40apX7++CTXaHaa3HDly3LJrLCkHDhyQsWPHSvHixWXp0qXSuXNn6datm+n2uhuPPPKIaV2aN2+e/Pe//zVn2umFdI8ePWoe13CktMUoMV12PWY3ZMgQE6JcNw1eAADAmVLdxbZq1ao0++YaXLQFafDgwWZZW5B2794t48aNkw4dOtzx69aoUcPcXDQclSpVSv7973/LoEGD7ug1+/TpIz179nQvawsSIQkAAGdKdUCqU6dOmn1zPYNMxzQlpkHm66+/lrSUPn16E772799vll1jk06ePGn2wUWXK1SokORr6EzirtnEAQCAs6UoIO3atUvKli1rTqvXr2/l0UcfTfE31zPYEp9p5ppnSccOpSWdm+nHH3+UZ555xj0QXEOSXl/OFYi0RWjTpk2mmw8AAAS2FAUkDRE6NkfHHOnXeoaYnglml9qJInv06GG6v7SLrVWrVrJ582YZP368uSXu2tK5kvQss8RTDbgGYp8+fdosh4SEuFujBg4cKNWrV5dixYpJbGysfPzxx3L48GHp2LGjez91/qQPPvjAjH9yneafP39+rjUHAABSFpD0rC+de8j1dVqpUqWKzJ0714QgDTUaVPT0+7Zt23pMDBkdHe3xPO0uc9E5maZNm2ZanQ4dOuSeX6lTp04m1OnA8ccee8zM/J24O0/PxLt06ZK8+uqrJkTVrl1blixZwhxIAACAi9XeKS5WCwBAgF+sVucrSqm//OUvKd4WAADAF6UoIDVt2jRFL8bFagEAQMAEJJ2vCAAAIFCkeiZtAAAAp0v1RJFKz/5as2aNObssPj7e4zG9VAgAAEBABaQdO3aYCRcvX75sglKuXLnkzJkzkilTJjNPEgEJAAAEXBebTu743HPPmbmGMmbMaC5cq5Mw6lxDw4cPvzd7CQAA4MsBSWet7tWrl7nsSLp06SQuLs5ctHXYsGHy7rvv3pu9BAAA8OWApBd+1XCktEvNNcu1Trp05MiRtN9DAAAAXx+DpJf52LJli7mGWZ06daRfv35mDNLUqVPNBW0BAAACrgVJLywbHh5uvv7www/Ntc46d+5sLhqb+CKzAAAA/irIsizL2zvhj7gWGwAAzv37neoWpCtXrphT/F30DLZRo0bJsmXL7nxvAQAAfEiqA9Lzzz8vU6ZMMV/HxsZK1apVZcSIEWb92LFj78U+AgAA+HZA2r59uzz++OPm69mzZ0u+fPlMK5KGpjFjxtyLfQQAAPDtgKTda1mzZjVfa7das2bNzGn/1atXN0EJAAAg4AJSsWLF5JtvvjFzHi1dulQaNWpk1p86deqWg50AAAAcG5B03qPevXtL4cKFpVq1alKjRg13a5LOkQQAABCQp/nHxMTIiRMnpHz58u5ZtTdv3mxakEqWLCmBgNP8AQBw7t/vVM+krXRgtt4S07PZAAAAArKLDQAAwOkISAAAADYEJAAAABsCEgAAgA0BCQAAwIaABAAA4GsB6dixY9KuXTvJnTu3ZMyYUcqVKydbt25Ndnudf6lNmzZSokQJMwdT9+7db9pmwoQJ5npxOXPmNLcGDRqYeZoSi4yMlKCgII/bU089dU9qBAAA/sWrAens2bNSq1YtSZ8+vSxevFiioqJkxIgRJtQkJy4uTvLkySN9+/Y1E1UmZfXq1fLSSy/JqlWrZMOGDVKwYEFzSRQNY4lpINLA5bpNnz49zWsEAAD+544mikwrH330kQkvEydOdK8rUqTILZ+jlzgZPXq0+fqLL75Icpsvv/zSY/k///mPfP3117Jy5Upp3769e31oaOhNE14CAAB4tQVp/vz5UrlyZWnZsqXkzZvXXMtNu8fS2uXLl+XatWuSK1eum1qa9Ps+8sgj0rlzZ/ntt99u2XKl05MnvgEAAGfyakA6cOCAjB07VooXLy5Lly41IaVbt24yefLkNP0+b7/9tuTPn9+MRUrcvTZlyhTTqqQtWWvWrJGnn35arl+/nuRrDBkyxFy7xXXTli8AAOBMd3Sx2rQSEhJiWpDWr1/vXqcBacuWLWbs0O3UrVtXKlSoIKNGjUp2m6FDh8qwYcNMa9Gjjz56y7BWtGhRWbFihdSvXz/JFiS9uWgLkoak213sDgAA+N/Far3aghQeHi6lS5f2WFeqVCmJjo5Ok9cfPny4CUjLli27ZThSDz/8sDz44IOyf//+JB/X8Ur6g0x8AwAAzuTVQdp6BtuePXs81u3du1ciIiLu+rW11ejDDz80XXfaSnU7R48eNWOQNLQBAIDA5tUWpB49esjGjRtl8ODBpuVm2rRpMn78eOnatat7mz59+niceaZ27txpbhcvXpTTp0+br3WKABcdU/Tee++Zs9z0rLeYmBhz0+2V3v/jH/8w3/vQoUNmHNLzzz8vxYoVk8aNG9/HnwAAAPBFXh2DpBYuXGhC0L59+8wp/j179pROnTp5TOioIUbHELnopI522uqk2ykNRYcPH75pm/79+8v7778vV65ckaZNm8qOHTskNjbWDODWeZIGDRokYWFhadqHCQAAfEdK/357PSD5KwISAAD+xy8GaQMAAPgiAhIAAIANAQkAAMCGgAQAAGBDQAIAALAhIAEAANgQkAAAAGwISAAAADYEJAAAABsCEgAAgA0BCQAAwIaABABwnGsxMXJp4yZzD9wJAhIAwFFiZ8+W/U/Wl+jISHOvy0BqEZAAAI6hLUYn+vUXSUj4c0VCglmmJQmpRUACADhG/KHDN8KRS0KCxB+O9tYuwU8RkAAAjhFSOEIk2PanLThYQiIKeWuX4KcISAAAx0ifL5+EDxxwIyQFB5tlXQ+kxgOp2hoAAB+Xo0ULyVy7tulW05YjwhHuBAEJAOA4GooIRrgbdLEBAADYEJAAAABsCEgAAAA2BCQAAAAbAhIAAIANAQkAAMCGgAQAAOBrAenYsWPSrl07yZ07t2TMmFHKlSsnW7duTXb7EydOSJs2baREiRISHBws3bt3T3K7WbNmScmSJSVDhgzmNRctWuTxuGVZ0q9fPwkPDzfft0GDBrJv3740rw8AAPgfrwaks2fPSq1atSR9+vSyePFiiYqKkhEjRkjOnDmTfU5cXJzkyZNH+vbtK+XLl09ym/Xr18tLL70kr7zyiuzYsUOaNm1qbrt373ZvM2zYMBkzZoyMGzdONm3aJJkzZ5bGjRvL1atX70mtAADAfwRZ2pTiJe+88458//338t13393R8+vWrSsVKlSQUaNGeaxv3bq1XLp0SRYuXOheV716dbOtBiItOX/+/NKrVy/p3bu3efzcuXMSFhYmkyZNkhdffPG23/v8+fOSPXt287xs2bLd0f4DAID7K6V/v73agjR//nypXLmytGzZUvLmzSsVK1aUCRMm3PXrbtiwwXSZJaatQ7peHTx4UGJiYjy20R9WtWrV3Nsk1XKlP9TENwAA4ExeDUgHDhyQsWPHSvHixWXp0qXSuXNn6datm0yePPmuXlfDj7YGJabLut71uGtdctvYDRkyxIQo161gwYJ3tY8AAMB3eTUgJSQkSKVKlWTw4MGm9ejVV1+VTp06mW4wX9OnTx/THOe6HTlyxNu7BAAAnBiQ9Ayy0qVLe6wrVaqUREdH39Xr5suXT06ePOmxTpd1vetx17rktrELDQ01fZWJbwAAwJm8GpD0DLY9e/Z4rNu7d69ERETc1evWqFFDVq5c6bFu+fLlZr0qUqSICUKJt9ExRXo2m2sbAAAQuB7w5jfv0aOH1KxZ03SxtWrVSjZv3izjx483t8RdWzpX0pQpU9zrdu7cae4vXrwop0+fNsshISHu1qg333xT6tSpY6YMaNKkicyYMcPMreR63aCgIDN/0gcffGDGP2lgeu+998yZbTodgDddi4mR+EOHJaRwhKRPpjULAADcY5aXLViwwCpbtqwVGhpqlSxZ0ho/frzH4x06dLDq1KnjsU53236LiIjw2GbmzJlWiRIlrJCQEKtMmTLWt99+6/F4QkKC9d5771lhYWHme9evX9/as2dPivf73Llz5vvqfVo5O2uWFVWqtBX1SElzr8sAACDtpPTvt1fnQfJnaT0PkrYc7X+yvo5cv7EyOFiK/W8lLUkAAATSPEi4QbvVPMKRSkiQ+MN3N2AdAACkHgHJR+iYI20x8hAcLCERhby1SwAABCwCko/QAdnhAwfcCEnBwWaZgdoAAATYWWzwlKNFC8lcu7bpVtOWI8IRAADeQUDyMRqKCEYAAHgXXWwAAAA2BCQAAAAbAhIAAIANAQkAAMCGgAQAAGBDQAIAALAhIAEAANgQkACk6qLKlzZuMvcA4GQEJAApEjt7tux/sr5ER0aae10GAKciIAG4LW0xOtGvv0hCwp8rEhLMMi1JAJyKgATgtuIPHb4RjlwSEsx1AwHAiQhIAG4rpHCESLDt7SI42FxU2ekYdwUEJgISgNvSCyiHDxxwIyQFB5tlp19YmXFXQOAKsizL8vZO+KPz589L9uzZ5dy5c5ItWzZv7w5w31pTtFtNW46cHo60Vh2M7tG1GBwsxf630vG1A06W0r/fD9zXvQLg1zQYBEo4uNW4q0D5GQCBjC42AEhCII+7AkBAAoAkBeq4KwB/oosNAJKRo0ULyVy7dsCMuwJwAwEJAG4hkMZdAbiBMUgAAAA2BCQAAABfC0jHjh2Tdu3aSe7cuSVjxoxSrlw52bp16y2fs3r1aqlUqZKEhoZKsWLFZNKkSR6PFy5cWIKCgm66de3a1b1N3bp1b3r8tddeu2d1AgAA/+HVMUhnz56VWrVqSb169WTx4sWSJ08e2bdvn+TMmTPZ5xw8eFCaNGliwsyXX34pK1eulI4dO0p4eLg0btzYbLNlyxa5fv26+zm7d++Whg0bSsuWLT1eq1OnTjJw4ED3cqZMme5JnQAAwL94NSB99NFHUrBgQZk4caJ7XZEiRW75nHHjxpltRowYYZZLlSol69atk5EjR7oDkgatxIYOHSpFixaVOnXqeKzXQJSPwZcAAMCXutjmz58vlStXNi07efPmlYoVK8qECRNu+ZwNGzZIgwYNPNZpMNL1SYmPj5f//ve/8re//c10oyWmLVAPPviglC1bVvr06SOXL19O9vvGxcWZ6ckT3wAAgDN5NSAdOHBAxo4dK8WLF5elS5dK586dpVu3bjJ58uRknxMTEyNhYWEe63RZA8uVK1du2v6bb76R2NhYiYyM9Fjfpk0bE5xWrVplwtHUqVPNWKjkDBkyxFy7xXXTli8AAOBMXu1iS0hIMC1IgwcPNsvagqTjhbQbrUOHDmnyPT7//HN5+umnJX/+/B7rX331VffXOjBcxzDVr19ffv31V9MdZ6chqmfPnu5lDWSEJAAAnMmrLUgaSkqXLu2xTscURUdHJ/scHTN08uRJj3W6rFfk1bPgEjt8+LCsWLHCDOK+nWrVqpn7/fv3J/m4njGn3yPxDQAAOJNXA5KewbZnzx6PdXv37pWIiIhkn1OjRg1z5lpiy5cvN+vtdPC3jm3Ss95uZ+fOne7QBgAAAptXA1KPHj1k48aNpotNW26mTZsm48eP95ivSLu22rdv717W0/t17NJbb70lv/zyi/zrX/+SmTNnmteyd99pQNKuugce8OxJ1G60QYMGybZt2+TQoUNmsLh+jyeeeEIeffTR+1A5AADwZV4NSFWqVJG5c+fK9OnTzZlkGlpGjRolbdu2dW9z4sQJjy43PcX/22+/Na1G5cuXN6f7/+c//3Gf4u+iXWv6PD17zS4kJMQ83qhRIylZsqT06tVLmjdvLgsWLLjHFQMAAH8QZFmW5e2d8Ec6SFvPZjt37hzjkQAAcNjfb69fagQAAMDXEJAAAABsCEgAAAA2BCQAAAAbAhIAAIANAQkAAMCGgAQAAGBDQALukWsxMXJp4yZzDwDwLwQk4B6InT1b9j9ZX6IjI829LgMA/AcBCUhj2mJ0ol9/vSDgnysSEswyLUkA4D8ISEAaiz90+EY4cklIkPjDN64pCADwbQQkII2FFI4QCbb91woOlpCIQvysAcBPEJCANJY+Xz4JHzjgRkgKDjbLuh4A4B8e8PYOAE6Uo0ULyVy7tulW05YjwhEA+BcCEnCPaCgiGAGAf6KLDQAAwIaABAAAYENAAgAAsCEgAQAA2BCQAAAAbAhIAAAANgQkAAAAGwISAACADQEJAADAhpm075BlWeb+/Pnzd/oSAADgPnP93Xb9HU8OAekOXbhwwdwXLFjwTl8CAAB48e949uzZk308yLpdhEKSEhIS5Pjx45I1a1YJCgry+zStQe/IkSOSLVs2cSKn1+j0+hQ1+j+Oof8774D3Go09Go7y588vwcHJjzSiBekO6Q/1oYceEifRX3Z//YVPKafX6PT6FDX6P46h/8vm5+81t2o5cmGQNgAAgA0BCQAAwIaABAkNDZX+/fube6dyeo1Or09Ro//jGPq/0AB4r3FhkDYAAIANLUgAAAA2BCQAAAAbAhIAAIANAQkAAMCGgARHcfLE8H/88Yc42enTp+X69eviZD/88APXbwT8BAHJ4U6dOiXTpk2T77//Xs6ePStO8/vvv8tLL70kEydOdGxA0kvaVK1aVfr16ydOdOLECWnWrJl069ZNfvzxR3GiY8eOSatWraRixYoydepUcZqYmBj58MMPzf/DDRs2OPL/4smTJ2X+/Pkm5Dr1w4p+SFmzZo0cOHDA27viEwhIDvbOO+9IsWLF5N///rc89dRT5g/QoUOHxEn++9//yldffSWfffaZCUt6CRi9Tp5T9OjRQwoXLiz58uWT119/XZzC9cdz1qxZUq5cOdNypPWFhYV5PO4EPXv2lEKFCsmVK1ckZ86c5vqNTjJgwADzPrN27VoZPny4tGzZUrZs2WKuUemU46gfTh5++GEZPXq0PPHEE9KlSxeJiooyjznl/ea9996TIkWKyJtvvilly5aVjz/+2AT7QEZAciD9pdZAtGrVKlm4cKEsX75c/vWvf8nOnTtl9+7d4iTfffedNG3aVHLlyiVDhgwRp4iOjpYCBQqYT6zr1q0z93phRadwXeBZWzc1BM6bN09q1arlvj6Sv18AWi1ZssTUo/8P9bZgwQKpXLmyLFq0SJxi8eLF5tjNnj1bli5daj6sFC9e3KxzynGcMWOGqU2P37Jly2TKlCly+PBhiYyMNI/f6mKn/mLlypUyd+5cmTlzpjmmffv2lenTp5sP2YHM/48sbnLt2jV55pln5PPPPzefdkJCQqR58+bmzapEiRKO+Im5mrhz584tL774otSuXdu8gW3bts28Yfn7J9cHHnjABCTtWtPb9u3b5a233pJPPvlEVqxYIVevXhV/t3HjRtOlpi2bO3bsMN1senvttddk9erVZht/Po7a7aStt1qb/j+Mj483rYF6FXG9IroTuMKefiBT2vKg///0/cfFn4+h0uCgH06efPJJSZcunTz//PPm/+TWrVtl1KhRjqhR3zu1Nj1u4eHh8u6775pWsv/9738mEAYqApIDuP5zuga4PvTQQ/LXv/7VvFmp2NhYEyLi4uJk6NCh5j+8P/2Hdu1r4qZsDRBKg4P+0dEAGBERIWPHjjXhSf/w+mONruCnb8gDBw40n8gbN24sL7zwgvzyyy8yadIkadu2rWl18fdjqF1NOi5OP5V37drVHEcNEvv27ZNnn31WDh486FctEK4a9QOK0hYG/X/n+r+pH1S0C1Hr06ug+9PxS+4YateajlvRVuqjR49K69atTXDQS1F07tzZHF9/PIauGi9fvmz+T+rvpgZcl/Tp00upUqVM15sGXn+qUetJ/Lunter/Rf27od3ALs8995z5f6hjy5zSjZhaBCQ/9+mnn8r7779vvtZPAK7woGMd1JEjR0xw0P/o+qng0qVL8n//93/Sp08f8bf67E3Z+sasb1Q6NkDDoIYI/SSkf4i+/fZbjzc0f6lRj53rzevxxx+Xv//972ZslXZhaFjatWuXOX46EHbcuHHiz8dQQ4N2OQ0bNswcw48++sj8jmq3sB5PbTFT/vDmnLhG/Z20c9WtrRA6KF3Drj/9UbUfQ9fvaJMmTUxNI0eOlKJFi8qZM2fk66+/Nn9Ytfvb1Q3lD2HQXqP+3mXKlEnKly9vBi4PGjTI1KehaMyYMeZ3Vbv2/eX/odJhCPo+2aZNG9Ntf/HiRVOrBiTtNkw8BCMsLMy06Or7qeskmIBjwS/t3LnTaty4sRUUFGSVK1fOWrlypVl//fp19zYJCQnmfseOHR7Pfe+996yKFSta586ds/y5vvj4eKtWrVpWbGystXjxYissLMzKli2bVbJkSSsuLs7jZ+BPNf7xxx/ubfbu3Wtt2LDB1O2q/bfffjPPe/311z229Zf6rl275r5/6aWXzOMff/yxWeeqZ9asWdaDDz5onTlzxvJlKfk9TWzFihVW4cKFreXLl1v+4HbH0GXu3LlW/fr1rd9//929bvXq1VZoaKgVHR1t+WON+v7iuu/du7dVokQJ8zup96tWrTKPac2DBg2yfN2mTZusChUqWGXLlrVGjhxp1alTx/wNGD58uHn8ypUrVq5cuax33nnHunjxovt5v/32m/XEE09YH374oRWIaEHy40F1ejVl7XIpWLCgudem08Sf7lyfUCtUqGDuXev3799vmvj1k66vfrJLSX3alK9n5dWpU8ecOaNjWfSTbObMmc3ZJspX67tVjdoS6Go10S6M6tWrm7pdn2r1U6vWrS1krlZDX5RcfdpKpt1Qet+pUydzht4333xjnuOqZ+/evab2xC1q/vp7mpgORNeWT735Q+vYrY6h1ueqUVvEErdcK+1K1K5iXx8vl1yN+v7outfWTW1F0u7gPXv2SN26dd2/p67ufl+lrV46HrVKlSqm5bl79+5mjJ+OR9X9196FDBkymIHZ48ePN3W65MqVy/yu6msEJG8nNNyZEydOWGvWrDFfjxo1yqpWrZo1adKk27aabNu2zapbt671+eef+219iT+dV6pUyerQoYO1b98+s6wtDrpcpUoVj09CTjqG2gqh9X3//feWv9aXuOVr6NChVt68ea2ePXtav/zyi7V//36rYcOG5tOsr0vtMdTfSW2t+Pvf/275g5TW93//93+mVUJbkrR1Sf8/6jF8+eWXk21N87f3GrvZs2eb/4cHDhywfNnp06et999/37z3J24Z01axqlWremz75JNPWvXq1bOmT59u/o9qy1P58uX9psUzrRGQHODIkSNWy5YtrSZNmlgxMTE3/cfWPzpr16613nzzTdMFpW/Oly9ftvy5Ptd/8rNnz970Jvbzzz9bFy5csJx0DKOiokyXRbdu3aycOXNaPXr0cP8M/P0YahfplClTTBO/dgHo72jr1q0ddwxdtKujbdu21tWrVy1/cqtj+NNPP1ktWrSw0qdPbwKgHkPtPj1//rzlpGOo7zfz58833dtZsmSx+vTpY4KEL3fl27tEXfuqHyT1fcR+HDt37mwFBwebwJshQwYrMjLS735X0woByc+5/vNOmzbNqlGjRpJ9xfpJ5/nnnzefDPQTgdPqc/H1N6m7qVE/0ekn8tq1a1sbN260nHgMjx8/bm3ZssWEQX+Tkhpdf6RmzJhh7dq1y3JafXr85s2bZ3322Wc3jXt0So3aGtO3b18THvzl/6HrfdH+/qjjN/WDif0x/Vpbp6dOnWpt377dCmQEJB908OBB67XXXrOWLFly02P2wZGuZW0R0pYh/Y/7ww8/mHX6x0ZdunTJ+vXXXy2n1edqMvbFJvy0qnHz5s3urhkdsO20+rZu3er4Y+j6PfW1AfVp/T7ji+7F76mvtWympkbX76A+J2vWrB5BXbsa4YlB2j5GTx3V+TV0YJwOnnMNgnTduwZHTp482b2sAz0zZsxo5iDR5cGDB8vTTz9tJjPT63jpqap6GrXT6tNTxPWUaV+byTYta6xWrZo5hjrwXGcodlp9OnDU6cfQ9XvqSwPq0/p9RuvzNffq9zRLlizirzW6fgd1tmydlkEv86NXXtB6//KXvzjyep13xRaY4EV6emnNmjWT/CTgMn78eDOg9ZlnnrFOnjzp8ZgulylTxpyu+sILL1iHDh2yfInT6wuEGp1eXyDU6PT6FDXefBxPnTrl/vno2CMdkzp48GArY8aMZnD20aNHvXa8fBUByYf89a9/NTelc9/omSFffPGFu2tFxy4UKFDAnIFmb67X7XWQq84BtG7dOssXOb2+QKjR6fUFQo1Or09RY/LHUYdc6FxcGnB1Tqdly5Z59Vj5MgKSD9B+bf2l1UnHdGDcJ598YlK/Dqx++OGHrfz585szJ5RulxQdo6LP9UVOry8QanR6fYFQo9PrU9R4++Ookz9qgPzyyy+9cIT8S5D+c3eddLiT6d5PnTolJUuWlJdfftlM5a4aNmxoLr9QqFAhad++vbkulfYh68URdVJAvY6aTnufRDepT122wOn1BUKNTq8vEGp0en2KGlN3HH3xGPo0bye0QKLzEZUuXdpMZ6/zvOh8Njpp4/r1692ncus8IgULFvToD9azYMLDw91T4Pvq6exOry8QanR6fYFQo9PrU9TojOPo63x7jnSH0QuoZs+eXdauXWuSfkxMjDz11FPm8hgFChQwF33UKez1UiD6Cc+V+CtWrChxcXHm8hLKVz8BOL2+QKjR6fUFQo1Or09RozOOo6/zrXNrHUyv6fPTTz9J3rx53ada6jWo9Mrs0dHRMmHCBPNYr1695OTJk+bK0keOHDG/3IsWLTLXpdKmcV/l9PoCoUan1xcINTq9PkWNzjiOfsHbTViBRC8v0KhRI3NGQeKzCrp27WqawF2Tkv3nP/8xA+2KFStmNW/e3Exp/+677/rkZHqBVF8g1Oj0+gKhRqfXp6jRGcfR1xGQ7gPXm9SqVavMNW5c0/C7ZjnVa2wVLVrU+uqrr9zP0dlp//3vf1tvv/22+z+Cr3J6fYFQo9PrC4QanV6fokZnHEd/QUBKI/Yp3ZN67MqVK2b6+gYNGtw0eE5/4QcOHGj5KqfXFwg1Or2+QKjR6fUpanTGcXQCxiDdJT2d8q233pJXX31VevbsKQcOHPDoK1c6UFIH0Z07d04GDBgga9askXHjxrmng9fp3fVSErly5RJf4/T6AqFGp9cXCDU6vT5Fjc44jo7i7YTmz2bOnGn6fuvVq2e999575mu94rpeCTmx0aNHWyEhIdakSZPM8gcffGAmaOvYsaO1du1aM+17kSJFrJ9//tnyJU6vLxBqdHp9gVCj0+tT1OiM4+g0BKQ7pP37Tz/9tDVkyBD3uujoaPOLO23aNLMcGxtrBhPqG9rkyZM9mkjHjBljPf7442aukvLly1ubNm2yfInT6wuEGp1eXyDU6PT6FDU64zg6EQHpDukvaK9evaxjx46Z5fj4eHNfqVIlq2/fvu4+5M2bN1vnzp1zPy/xmQX69YEDByxf5PT6AqFGp9cXCDU6vT5Fjc44jk5EQEqhWbNmWcuXL3e/USVFPwE88sgj1uLFiy1/4/T6AqFGp9cXCDU6vT5Fjc44joGAgHQbU6ZMMf2/VatWtfLkyWPVqlXLmjNnjnlMm0ATJ/zDhw9bxYsXt/bv32/5C6fXFwg1Or2+QKjR6fUpanTGcQwkBKRbnGo6atQoq1SpUmYirri4ODMosn379mZMwNWrV93buvqKdVCdTtZ1+fJljysnJ97GVzi9vkCo0en1BUKNTq9PUaMzjmMg4jT/ZFy6dElOnz4tHTp0cF8Ju2bNmlK6dGk5f/68+9TaxNe6mTdvnjz77LOSMWNG2blzpzRq1EgGDRrkk1dQdnp9gVCj0+sLhBqdXp+iRmccx0BEQEpk37597rkm9GKPLVq0kN69e0twcLAkJCSY9QULFjT/4dOnT+/xg9R1Om9FtWrVpEuXLlK5cmVzrZxhw4b5zC+70+sLhBqdXl8g1Oj0+hQ1OuM4BjxvN2H5Ap2yvXDhwmbAnI4B0KbuxBL3G7dp08aKjIy8acbXnTt3WkFBQeZWvXp1KyoqyvIVTq8vEGp0en2BUKPT61PU6IzjiD8FfEBatmyZedP65z//aS1ZssTq2bOnlT59emv8+PHmtEtXf7DedPnRRx+1pk6datnpBF56IUg9A8WXOL2+QKjR6fUFQo1Or09RozOOI24I2IDkGgQ3YMAA67HHHnPPL6K6dOliVa5c2X0WiYueeqtvcnv37jXLet+9e3fLFzm9vkCo0en1BUKNTq9PUaMzjiNuFrBjkFz9vFFRUVK0aFHTR3zt2jWz7oMPPpAMGTKYQXQxMTHu56xYscL0KYeHh8ubb75pBlJGR0eb57nGDvgKp9cXCDU6vb5AqNHp9SlqdMZxRBKsAKHNv2+88YY1cuRIj2natYk7a9as1h9//GGWXZ/wdH2JEiWsVatWuT8ltWzZ0sqZM6eVO3duq0yZMtaWLVssX+H0+gKhRqfXFwg1Or0+RY3OOI64PccHpOPHj1vPPvusmYRNr3Oj17LJnj27+81rz549VoECBcxFIJXOQ+KSL18+80anLl26ZF7noYcesmbMmGH5CqfXFwg1Or2+QKjR6fUpanTGcUTKOTog6S9phw4drNatW3tcw0bPIHGdWXD+/HlzteSMGTOai0Am7lOvU6eOuYKyy9atWy1f4vT6AqFGp9cXCDU6vT5Fjc44jkgdR49BypQpk4SGhkpkZKQUKVLEPVnXM888Iz///LOZiyRr1qzSpk0bqVSpkrRq1UoOHz5s+tS1r/jUqVPStGlT9+s99thj4kucXl8g1Oj0+gKhRqfXp6jRGccRqROkKUkcTAfEuSbp0oFxOhlb27ZtJXPmzDJ+/Hj3dseOHZO6deuaNzedtGv9+vVSsmRJmTZtmoSFhYmvcnp9gVCj0+sLhBqdXp+iRmccR6Sc4wNSUmrXri2dOnUy0/u7zibQN7T9+/fLtm3bZNOmTVK+fHnzuD9yen2BUKPT6wuEGp1en6JGZxxHJMMKML/++qsVFhbm0T+ceKCdv3N6fYFQo9PrC4QanV6fokY4naPHICXmaihbt26dZMmSxd0/PGDAADNHhfYf+zOn1xcINTq9vkCo0en1KWp0xnHE7T0gAcI1mdnmzZulefPmsnz5cnn11Vfl8uXLMnXqVHOhQH/m9PoCoUan1xcINTq9PkWNzjiOSAErgOi1cYoVK2YuEBgaGmoNHTrUchKn1xcINTq9vkCo0en1KWpEIAi4QdoNGzaU4sWLyyeffGKmh3cap9cXCDU6vb5AqNHp9SlqhNMFXEC6fv26pEuXTpzK6fUFQo1Ory8QanR6fYoa4XQBF5AAAABuJ2DOYgMAAEgpAhIAAIANAQkAAMCGgAQAAGBDQAIAALAhIAEAANgQkAAElNWrV5vLZcTGxnp7VwD4MOZBAuBodevWlQoVKsioUaPMcnx8vPz+++8SFhbmvq4YAATsxWoBQIWEhEi+fPn4YQC4JbrYADhWZGSkrFmzRkaPHm1ai/Q2adIkjy42Xc6RI4csXLhQHnnkEcmUKZO0aNFCLl++LJMnT5bChQtLzpw5pVu3bubyGi5xcXHSu3dvKVCggGTOnFmqVatmuu8AOAMtSAAcS4PR3r17pWzZsjJw4ECz7qeffrppOw1DY8aMkRkzZsiFCxekWbNm8sILL5jgtGjRIjlw4IA0b95catWqJa1btzbPef311yUqKso8J3/+/DJ37lx56qmn5McffzQXqgXg3whIABwre/bspktNW4Vc3Wq//PLLTdtdu3ZNxo4dK0WLFjXL2oI0depUOXnypGTJkkVKly4t9erVk1WrVpmAFB0dLRMnTjT3Go6UtiYtWbLErB88ePB9rhRAWiMgAQh4GqBc4UjpAG7tWtNwlHjdqVOnzNfaSqTdbSVKlPD42Wm3W+7cuQP+5wk4AQEJQMBLnz69x89AxygltS4hIcF8ffHiRUmXLp1s27bN3CeWOFQB8F8EJACOpl1siQdXp4WKFSua19QWpccffzxNXxuAb+AsNgCOpl1lmzZtkkOHDsmZM2fcrUB3Q7vW2rZtK+3bt5c5c+bIwYMHZfPmzTJkyBD59ttv02S/AXgXAQmAo+ngae0G04HWefLkMQOr04IOxtaA1KtXLzM9QNOmTWXLli1SqFChNHl9AN7FTNoAAAA2tCABAADYEJAAAABsCEgAAAA2BCQAAAAbAhIAAIANAQkAAMCGgAQAAGBDQAIAALAhIAEAANgQkAAAAGwISAAAADYEJAAAAPH0/wDj0MOP1LI86QAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# vo.sel(z=-4).plot() # only works with xarray >= 2026.01.0\n",
+    "ms.VerticalObservation(\n",
+    "    vo.data.where(vo.data[\"z\"] == -4.0, drop=True)\n",
+    ").plot()  # xarray < 2026.01.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "13623923",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "time",
+         "rawType": "datetime64[ns]",
+         "type": "datetime"
+        },
+        {
+         "name": "z",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "salt",
+         "rawType": "float64",
+         "type": "float"
+        }
+       ],
+       "ref": "014ed72c-1ec7-42aa-9d41-205892e2cb06",
+       "rows": [
+        [
+         "2022-06-12 11:43:00",
+         "-30",
+         "6.530923333847987"
+        ],
+        [
+         "2022-06-12 11:43:00",
+         "-28",
+         "6.609798287009486"
+        ],
+        [
+         "2022-06-12 11:43:00",
+         "-26",
+         "6.566854180191086"
+        ],
+        [
+         "2022-06-12 11:43:00",
+         "-24",
+         "6.695171944159267"
+        ],
+        [
+         "2022-06-12 11:43:00",
+         "-22",
+         "6.718102975909249"
+        ],
+        [
+         "2022-06-12 11:43:00",
+         "-20",
+         "6.553262971606426"
+        ],
+        [
+         "2022-06-12 11:43:00",
+         "-18",
+         "6.539053962169886"
+        ],
+        [
+         "2022-06-12 11:43:00",
+         "-16",
+         "6.605434841509121"
+        ],
+        [
+         "2022-06-12 11:43:00",
+         "-14",
+         "6.550503673328178"
+        ],
+        [
+         "2022-06-12 11:43:00",
+         "-12",
+         "6.591007544998949"
+        ],
+        [
+         "2022-06-12 11:43:00",
+         "-10",
+         "6.565607878506718"
+        ],
+        [
+         "2022-06-12 11:43:00",
+         "-8",
+         "6.3504992675960255"
+        ],
+        [
+         "2022-06-12 11:43:00",
+         "-6",
+         "6.175613961338043"
+        ],
+        [
+         "2022-06-12 11:43:00",
+         "-4",
+         "6.095457684919699"
+        ],
+        [
+         "2022-06-12 11:43:00",
+         "-2",
+         "6.0555155237354095"
+        ],
+        [
+         "2022-06-12 11:43:00",
+         "0",
+         "6.204021225037544"
+        ],
+        [
+         "2022-06-13 11:41:00",
+         "-30",
+         "6.563640371857646"
+        ],
+        [
+         "2022-06-13 11:41:00",
+         "-28",
+         "6.682900313040625"
+        ],
+        [
+         "2022-06-13 11:41:00",
+         "-26",
+         "6.674305231479064"
+        ],
+        [
+         "2022-06-13 11:41:00",
+         "-24",
+         "6.64781647045105"
+        ],
+        [
+         "2022-06-13 11:41:00",
+         "-22",
+         "6.641568783874796"
+        ],
+        [
+         "2022-06-13 11:41:00",
+         "-20",
+         "6.634721998302609"
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+        [
+         "2022-06-14 12:21:00",
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+         "6.53892152605355"
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+        [
+         "2022-06-14 12:21:00",
+         "-26",
+         "6.669357362698129"
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+        [
+         "2022-06-14 12:21:00",
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+         "2022-06-14 12:21:00",
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+         "2022-06-14 12:21:00",
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+         "2022-06-14 12:21:00",
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+         "6.548194762374584"
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+        [
+         "2022-06-14 12:21:00",
+         "-14",
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+        [
+         "2022-06-14 12:21:00",
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+         "6.205054075854417"
+        ],
+        [
+         "2022-06-14 12:21:00",
+         "-10",
+         "6.221582126574461"
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+        [
+         "2022-06-14 12:21:00",
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+         "6.173364771621326"
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+        [
+         "2022-06-14 12:21:00",
+         "-6",
+         "6.173007442266893"
+        ],
+        [
+         "2022-06-14 12:21:00",
+         "-4",
+         "6.064030511254615"
+        ],
+        [
+         "2022-06-14 12:21:00",
+         "-2",
+         "6.102461645927502"
+        ],
+        [
+         "2022-06-14 12:21:00",
+         "0",
+         "6.112659560703031"
+        ],
+        [
+         "2022-06-15 10:57:00",
+         "-30",
+         "6.732605809141489"
+        ],
+        [
+         "2022-06-15 10:57:00",
+         "-28",
+         "6.68766879079597"
+        ]
+       ],
+       "shape": {
+        "columns": 2,
+        "rows": 144
+       }
+      },
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>z</th>\n",
+       "      <th>salt</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>time</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2022-06-12 11:43:00</th>\n",
+       "      <td>-30</td>\n",
+       "      <td>6.530923</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-06-12 11:43:00</th>\n",
+       "      <td>-28</td>\n",
+       "      <td>6.609798</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-06-12 11:43:00</th>\n",
+       "      <td>-26</td>\n",
+       "      <td>6.566854</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-06-12 11:43:00</th>\n",
+       "      <td>-24</td>\n",
+       "      <td>6.695172</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-06-12 11:43:00</th>\n",
+       "      <td>-22</td>\n",
+       "      <td>6.718103</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-06-20 11:06:00</th>\n",
+       "      <td>-8</td>\n",
+       "      <td>6.246086</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-06-20 11:06:00</th>\n",
+       "      <td>-6</td>\n",
+       "      <td>6.168087</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-06-20 11:06:00</th>\n",
+       "      <td>-4</td>\n",
+       "      <td>6.206396</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-06-20 11:06:00</th>\n",
+       "      <td>-2</td>\n",
+       "      <td>6.190989</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-06-20 11:06:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>6.211846</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>144 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      z      salt\n",
+       "time                             \n",
+       "2022-06-12 11:43:00 -30  6.530923\n",
+       "2022-06-12 11:43:00 -28  6.609798\n",
+       "2022-06-12 11:43:00 -26  6.566854\n",
+       "2022-06-12 11:43:00 -24  6.695172\n",
+       "2022-06-12 11:43:00 -22  6.718103\n",
+       "...                  ..       ...\n",
+       "2022-06-20 11:06:00  -8  6.246086\n",
+       "2022-06-20 11:06:00  -6  6.168087\n",
+       "2022-06-20 11:06:00  -4  6.206396\n",
+       "2022-06-20 11:06:00  -2  6.190989\n",
+       "2022-06-20 11:06:00   0  6.211846\n",
+       "\n",
+       "[144 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vo.to_dataframe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "01dae719",
+   "metadata": {},
+   "source": [
+    "**Read model**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "5ef917ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<VerticalModelResult>: vmod\n",
+       "Time: 2022-06-12 00:00:00 - 2022-06-20 22:00:00\n",
+       "Quantity: Salinity [PSU]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fn = \"../tests/testdata/vertical/VerticalModel_at_obs.dfs0\"\n",
+    "ms.model_result(fn, z_item=\"z\", name=\"vmod\", gtype=\"vertical\", item=\"Salinity\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "3b378880",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read model from dfs0 path\n",
+    "vm = ms.VerticalModelResult(\n",
+    "    \"../tests/testdata/vertical/VerticalModel_at_obs.dfs0\", item=\"Salinity\"\n",
+    ")\n",
+    "# Read model from dfs0 dataset\n",
+    "vm = ms.VerticalModelResult(\n",
+    "    mikeio.read(\"../tests/testdata/vertical/VerticalModel_at_obs.dfs0\"), item=\"Salinity\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "554605bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "vm.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa7a95f2",
+   "metadata": {},
+   "source": [
+    "Read from dfsu and extract"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "454ccc2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<VerticalModelResult>: sigma_z_coast\n",
+       "Time: 2022-06-12 00:00:00 - 2022-06-20 22:00:00\n",
+       "Quantity: Salinity [PSU]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfsum = ms.DfsuModelResult(\n",
+    "    \"../tests/testdata/vertical/sigma_z_coast.dfsu\", item=\"Salinity\"\n",
+    ")\n",
+    "dfsum.extract(vo)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c055bbb",
+   "metadata": {},
+   "source": [
+    "**Test below**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70413d43",
+   "metadata": {},
+   "source": [
+    "**Matching**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "dd260352",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Comparer>\n",
+       "Quantity: salinity [psu]\n",
+       "Observation: salt, n_points=108\n",
+       "Model(s):\n",
+       "0: Salinity"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cmp = ms.match(vo, vm)\n",
+    "cmp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34230bab",
+   "metadata": {},
+   "source": [
+    "### Working directly with standard comparer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "76ef75ca",
+   "metadata": {},
+   "source": [
+    "**Overall skill and plots**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "340aa23d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>n</th>\n",
+       "      <th>bias</th>\n",
+       "      <th>rmse</th>\n",
+       "      <th>urmse</th>\n",
+       "      <th>mae</th>\n",
+       "      <th>cc</th>\n",
+       "      <th>si</th>\n",
+       "      <th>r2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>observation</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>salt</th>\n",
+       "      <td>108</td>\n",
+       "      <td>-0.197348</td>\n",
+       "      <td>0.20948</td>\n",
+       "      <td>0.070255</td>\n",
+       "      <td>0.197348</td>\n",
+       "      <td>0.940359</td>\n",
+       "      <td>0.010997</td>\n",
+       "      <td>-0.030702</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               n      bias     rmse     urmse       mae        cc        si  \\\n",
+       "observation                                                                   \n",
+       "salt         108 -0.197348  0.20948  0.070255  0.197348  0.940359  0.010997   \n",
+       "\n",
+       "                   r2  \n",
+       "observation            \n",
+       "salt        -0.030702  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cmp.skill()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "2bd46e12",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'Salinity vs salt'}, xlabel='Observation, salinity [psu]', ylabel='Model, salinity [psu]'>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cmp.plot.scatter()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0051c543",
+   "metadata": {},
+   "source": [
+    "**Slice comparer**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "3d6c58eb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>n</th>\n",
+       "      <th>bias</th>\n",
+       "      <th>rmse</th>\n",
+       "      <th>urmse</th>\n",
+       "      <th>mae</th>\n",
+       "      <th>cc</th>\n",
+       "      <th>si</th>\n",
+       "      <th>r2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>observation</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>deep</th>\n",
+       "      <td>63</td>\n",
+       "      <td>-0.194522</td>\n",
+       "      <td>0.205873</td>\n",
+       "      <td>0.067416</td>\n",
+       "      <td>0.194522</td>\n",
+       "      <td>0.895853</td>\n",
+       "      <td>0.010336</td>\n",
+       "      <td>-0.841364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>surface</th>\n",
+       "      <td>45</td>\n",
+       "      <td>-0.201304</td>\n",
+       "      <td>0.214428</td>\n",
+       "      <td>0.073865</td>\n",
+       "      <td>0.201304</td>\n",
+       "      <td>0.662412</td>\n",
+       "      <td>0.011912</td>\n",
+       "      <td>-3.729550</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1m</th>\n",
+       "      <td>9</td>\n",
+       "      <td>-0.187027</td>\n",
+       "      <td>0.197700</td>\n",
+       "      <td>0.064078</td>\n",
+       "      <td>0.187027</td>\n",
+       "      <td>0.548066</td>\n",
+       "      <td>0.010388</td>\n",
+       "      <td>-6.162230</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              n      bias      rmse     urmse       mae        cc        si  \\\n",
+       "observation                                                                   \n",
+       "deep         63 -0.194522  0.205873  0.067416  0.194522  0.895853  0.010336   \n",
+       "surface      45 -0.201304  0.214428  0.073865  0.201304  0.662412  0.011912   \n",
+       "1m            9 -0.187027  0.197700  0.064078  0.187027  0.548066  0.010388   \n",
+       "\n",
+       "                   r2  \n",
+       "observation            \n",
+       "deep        -0.841364  \n",
+       "surface     -3.729550  \n",
+       "1m          -6.162230  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cmp_deep = cmp.where(cmp.data['z']<-10).rename({cmp.name: \"deep\"})\n",
+    "cmp_surface = cmp.where(cmp.data['z']>=-10).rename({cmp.name: \"surface\"})\n",
+    "cmp_1m = cmp.where(cmp.data['z']==-2).rename({cmp.name: \"1m\"})\n",
+    "cmps = ms.ComparerCollection([cmp_deep, cmp_surface, cmp_1m])\n",
+    "cmps.skill()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c1d6ab54",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x18f4aa04d70>]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# How fix this, see issue modelskill#629\n",
+    "\n",
+    "d = cmp_1m.raw_mod_data[\"Salinity\"].data\n",
+    "cmp_1m.data[\"Observation\"].plot(marker=\"o\", linestyle=\"\", label=\"Observation\")\n",
+    "d.where((d.z == -1.5),drop=True)['Salinity'].plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a8ecd547",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Comparison 847\n",
+    "start=cmp.time[0]-pd.Timedelta(\"1D\")\n",
+    "end=cmp.time[-1]+pd.Timedelta(\"1D\")\n",
+    "d = cmp.data\n",
+    "#d = d.sel(time=d.time.to_index().to_frame().loc[start:end].index)  # type: ignore\n",
+    "d = d.sel(time=slice(start, end))\n",
+    "d"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c420179",
+   "metadata": {},
+   "source": [
+    "### Vertical specific comparer functions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0270482e",
+   "metadata": {},
+   "source": [
+    "**Havmöller**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c595b126",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "cmap = \"viridis\"\n",
+    "\n",
+    "mod_name = \"Salinity\"\n",
+    "mod_at_obs = cmp.raw_mod_data[mod_name].values\n",
+    "z = cmp.raw_mod_data[mod_name].z\n",
+    "t = cmp.raw_mod_data[mod_name].time\n",
+    "v = mod_at_obs\n",
+    "\n",
+    "# Maybe absolute z?\n",
+    "\n",
+    "T = np.array(sorted(set(t)))\n",
+    "# assuming equal number of layers\n",
+    "n_layers = len(z) // len(T)\n",
+    "\n",
+    "Z = z.reshape(len(T), n_layers)\n",
+    "V = v.reshape(len(T), n_layers)\n",
+    "# time grid\n",
+    "T_grid, Zgrid = np.meshgrid(T, np.arange(n_layers), indexing=\"ij\")\n",
+    "fig, ax = plt.subplots(figsize=(18,5))\n",
+    "ax.contourf(T_grid,Z,V, cmap=cmap) # pcolormesh\n",
+    "\n",
+    "# plot obs\n",
+    "# # Overlay observation points\n",
+    "obs = cmp.data[\"Observation\"]\n",
+    "obs_t = set(sorted(obs.time.values))\n",
+    "\n",
+    "for date in obs_t:\n",
+    "    obs_on_date = obs[obs.time == date]\n",
+    "    ax.scatter([date]*len(obs_on_date), obs_on_date['z'], \n",
+    "               c=obs_on_date.values, s=100, edgecolors='white', linewidths=1, \n",
+    "               cmap=cmap, vmin=V.min(), vmax=V.max())\n",
+    "\n",
+    "#ax.invert_yaxis() # if pos z\n",
+    "# ax.set_title(f'{mod_name} profile at {df_to_plt.attrs[\"station_name\"]}')\n",
+    "# ax.invert_yaxis()\n",
+    "# plt.colorbar(c, ax=ax, label=f'{mod_name} (psu)')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "85d20386",
+   "metadata": {},
+   "source": [
+    "**profiles at specific time**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2cfd6938",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cmp.data.sel(time=cmp.data.time[0])\n",
+    "cmp_t = cmp.sel(time=cmp.time[0])\n",
+    "# Above creates problem on this row:\n",
+    "#raw_mod_data = {k: v.sel(time=time) for k, v in raw_mod_data.items()} 854 in comparison.py\n",
+    "# Because raw data dont have exact same times\n",
+    "\n",
+    "def plot_time(cmp, t):\n",
+    "    fig, ax = plt.subplots(1, 1, figsize=(8, 6), sharey=True)\n",
+    "    data  = cmp.data.sel(time=t)\n",
+    "    mod_v = data[cmp.mod_names[0]].values\n",
+    "    z = data['z'].values\n",
+    "    obs_v = data['Observation'].values\n",
+    "    ax.plot(mod_v, z, 'o-', linewidth=2, label='Model')\n",
+    "    ax.plot(obs_v, z, 'o-', linewidth=2, label='Observation')\n",
+    "    ax.set_xlabel('Value')\n",
+    "    ax.set_ylabel('Height')\n",
+    "    ax.legend()\n",
+    "    ax.grid(True)\n",
+    "\n",
+    "    ts = pd.to_datetime(t)\n",
+    "    ax.set_title(f\"Profile at time {ts:%Y-%m-%d %H:%M}\")\n",
+    "\n",
+    "avail_times = cmp.time.unique()\n",
+    "plot_time(cmp, avail_times[2])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6647a8e",
+   "metadata": {},
+   "source": [
+    "**Profile average plots**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "967dc7fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x174b555b380>"
+      ]
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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HY/jw4dc87/HHH8cdd9yhAiWpj7yGLMtv5e3tbStLL4ys0UJERObCgKUM5TdcI7kpMhtIEmxz6yeRsCA00Btrx3bNdYqzdbG0wqpatSreeOMN23BOdpKb8t1332HQoEFqL56vvvrKFoTIfZJrMmnSJJXPIsM3MjQj+vXrp4aObr75ZtWjkZSUhKNHj6q8kuutk7z2O++8gwEDBiAoKEjly+zatUsN/0ieTK1atVSwsnr1akRGRha6HYjoWlxagXKVmQEcXw8kngX8KgO1OgCuZbf3mSkCFln5U/IhVq5ciTNnzqgvtsGDB+PFF1+Ep6fWq/D333+rL0vJn4iPj1d/oUvi6AMPPFDk3oMff/wR9913H8qSBCETezdWs4GkRtmDFmsN5f6SXI9FEl1zI70YMmwiPRWic+fOtunFH3zwgRoukqRbGRLq2rWr7XkyZJSSkoK2bdva2lXOFTZgya9O8jnKUE+XLl3UsQRnDz/8sApY3nzzTRU0yTUiOTXy/kRUPDIbURL8s89WrBLorX7/9Gxahc3qrPYsBJaMBeJP288FVAV6TgUa21MDSpOLpSx25rtOS5Yswc8//6wSJevVq6f+spa8Csl5sOZTvP7667hy5Qpuv/12VK5cGYsXL8bIkSOxYMEC3HnnnXm+tnyxzpw5M8eMlPLly+cYRiiIBEiBgYEqfyMgIECdkyEJ6V2oU6dOkV6ruL8szLwcfUkzc1sU97rJjWwAGRMTo9a4Mduu1SWJ7VD4drAurXD1l4L1X9Gng1uZPmjh9VCMdpBgZc6Qq/6UznZl3PPddQUtuX2HmraHRYKJ7AFFWFiYmjHy6aef2gKW//u//8vxnGeffRZ//fUX5s2bl2/AYg1QQkNDYQTyy+DWxqFc6ZaIDLW0gnw1yf3y+4krbzvZMNCSsbkEK9mujCXjgPBepT48ZIqAJTcSiUkuQ0GPadSoUYGv9dRTT+HRRx9VgZAkcMqwRH5/mcuwh9yyR4fWiFVu1rL8pW+9FYWM+rQLy/mzFeY1rI8xQadZqTNrW1ivl+zXUnFZr8HrfR2zYzsUrh02Hom9ZtHK3JZW2HjkPNqFVYRZ8XooYjscWwfX7MNA17AA8VHIPLYOqF3wshq5KezvKFMGLIcOHVKzUay9K7mZM2eOmgb7+eef5/takydPVnkYkiQqPTKSC5GYmKjWG8mLJIZK0unVzp07Z5udIkmh8iHI0ERZzEqRC8861ddswyAlzcxtIdeKXDeSr+Ph4XFdryWvI0G7tIezDwmxHQpuh0OnLhSqPQ+dOocwP/uyAmbD66Fo7eAddQDlUbD4qANI9mmA4khISDB+wCKrok6dOjXfx+zduzfHrBFZrEyGh2RhMcljyY0sEia9JF9++WWBCZ8vvfSSrSwJnJcvX1YLoOUXsMj0WsmPyd7DUqNGDQQHB+fIYZEPQfIo5FZWrvdLzpGYsS3kWpFfHhUrViyRHBYJ2OS6dPaAhe1QcDvUS5Tu/KMFtme96sEICTF3DwuvBxS+HZIKF4QEVGuAgJCQYn0mhf1dp2vAMmrUKDXjJD8yTGMlq6DKLJEOHTqo1Vhz888//6B3795qxpCs61FUMsNEZpvIkI914bSryfnc7pMP3frBy//lYrDeSptEydb3MVuvQkkzc1tYr5fs19L1vl5JvZaZsR0Kboe2YZVUgn9ew0LWpRXkca4m3z2e10MR2qH2TYB3eSD5Uh4PcFGzhVzlccX8PVPY30+6BiwS2cmtMKRnRYIVWelUZvXk9gPK1GZJsJVem9wWGCsMWca9QoUKeQYrzjDXnYicjyTSTujVCE/9sO2a+0praQUyCfe8vg+zroWeb5bJd5QpclgkWJG1QGRxMMlbkVwRK+vsHhkGkmBFZgf1799frdciZJ0Wa3Lub7/9poZz9u3bp44XLVqEs2fPol27dqpLatmyZWp69OjRo+HMc92JyDn5eec+jCo9K1yHxUnt/k3741m4eQIZqVd9N71ZZt9NpugnlkBCEm1lH5vq1aujSpUqtpvVt99+q1ZWlYTY7PfL7rxWkmAk06Gz5zjIHjmy068sOCYJuu+++y4mTpwIXVjnul+dkR0frZ2X+6+T7O8ji7nJejYyg6pZs2aq7bL3UklbGI3US9bjyT482LFjxxJ9j1deeUV1kUpgm314SdZEkanvRI5uwbYoW/n57g3wwX0t8OOwdmqFbbOvv0LFILN3Vmeb3DJoDvDQYqD/DO3/z+0s0z+kTdHDInkuBeW6yKZ5civK61y9voszzHWXn1/yc2T5etlZWVYRlsX2ZHaKbCBYmqyLuRU3YLl06ZLt85LVjtesWVPCNYQacpStB2TDRCFBcqVKldTy/0SO7EpqBpbu1nqm/b3d8dgtYfD24FC0U9u3GDi3VyvXaAuEdZbEF92qY4qAxWF8fguQGJP7fekpwJXYAue6Y1r9PMcT3XyDgcf+yfMVDh48qHY9PnnypApWhOxoLHv0yPoz1oBFAgtJWN66davK5ZkxY4bqdZHnS8Aj074lw7xv37549dVX1RRumW0lWydID45sXii9VZILJI+XfCPpIZNVFWV1YtmD6OOPP1bvJa9Vs2ZN1fMlw3hPPPGE6imTWVayl9GECRNUXtFnn32mpipL4CK9ZlI/qZMEMWLp0qVquE/qLu/74YcfIiIiQj3+6aefRqdOnbBu3Tp1v/QotWnTJtc2kn2QZHq71EWGGyV4kS0A5LWtZLq89FLJ7DCpkyxaKLPW5LV79eqlpiTLqsvNmzdXM9WkrYtaD6KytmLfWVxO1aYr3940lMGKs7NYgNXT7MedxugarJhmSMhhSLCScDr3W77BSjbyuFye7yK3y3kEQ1m2bdum9liSKbPZyZCYBDHW3KDdu3fjoYceUlsgyBez7KskQyMSZEiekPTO7Ny50za1W6aBy5ey7OMkwYUMM0mgYbVlyxb8/vvvKndIAg1ZI8e68N4vv/yikqkl+VqCJ+nRkEBJniO7L//7778qMJGASvYTktd/+eWXc9RfAiEJbiQA2LFjh5rubq2zkPeVn0fqPWLECLUHVX5knyp5LQmGJDjp0aOH7T45Jwnds2fPxubNm9Vwpcx2kzwr2Qn6hx9+UOel7WSpaVkvyKqo9SAqS/O32Yei+7WoxsZ3dgeXAWd2aOUqLYB63fWuEXtYypRfPnPUC+xhyVKuYq49LPLVbPENtmXzXw8JHLp166bK99xzj/qCloBGegdkQ0npFbnlllvUzs1Cem0kP0gCDCG9LPIaVtL74O/vr8qyXo2sdyO7PMt5GcaT1xTSKyEL90lQIr0y8p5SlqTo/GzcuFEFSXITEthIb4YEEULydawbIkpwlt+Cg0KCiltvvRV+fn7q588+I239+vU4cuSIGkbLTnqIJGdKptNLcCY9KNImMgXfqqj1ICorl5JS8c8B7Q+eEH8vtDXxSrZUUr0rbxmqd0VwSKgs5TNco3JY3m+qJdjmmseizXVXSU655bDI6q6SI5LP20ugIMM6MmSRvZdlw4YNtoXv8lsbRGZfyRew9CpIb8v777+PP/74Q/VkSE/Cbbfdluvz5Ys/Oxlikanpki8iQ0XWvBQZWpF8EekJklwXGfqxrhx8PbIvSiS9IAWtPCy7UMuMNFnNWAKU7ORnlcUIrz4vZs2apYbFZC0gWUBQhqXkuLj1ICorf+46g7QM7fdO7+ZVOXXZ2R1dDZz6TyuHNAYa3gEj4JCQUUgQIlOXFZdSmesuw0GyqJ70mEieiJCkWxnSyL7ir5yTaeJi7ty5avdrmZ0lwY6UZVjnrbfeUsM1ol+/fqpnwfqa8n8ZVsqLPF6GWmRGlwy/WBNxJbFV3keOpcdCAiMrCQCkxyI30gMjQ1QyDCN++uknlZQrgUdxyeKBkp8jvSLZScAmuykvX77cdk56gaRXSeovAZfUVVY5LigJnMgo5mebHcThIEL23JWOo4q9IFxJYw+Lkcj0MNmmO9d1WEpmrvt3332n8ktk+ETWqJG/9GVIRno9rKQHQb5sZXsCecyPP/6oelgkeJFeBDknSbeSCCskz0VyUmS4w7qyrJzLa1sESeSVoZbp06errRespF6SlCv5I3Xr1lV7PFnJrJ3vv/9e5bNYk26tpGdIckrknDXp1lrn4pJk2NwSYuW1ZchH1uqRQE8SjiVpWIbF5P0XLFiAhg0bqjrJtOvjx48Xuw5EZeH0pSvYdEzbRyiski+aVtO2FyEndXwDcCxrBmZQXaCJNmPSCFwsZtvO1oBktogkWEoPQPa9hOQvcVnDo8h7whRjpVv5GK3Ths22HH1JM3NbXNd1cxUJKiUhOSQkxKmX5mc75N8OX6w+jNf/0BbTfK57fTzXvXgb2JkFr4cC2mFWf+BQVg9y3+lAywegx3dobtjDYkQSnNQp2UXRiIhys2C7vTe3L2cHObeorfZgpXxNIOIeGInz/tlFROTkDsUkYPfpeFVuXj0QdSpp6zORk1rzjr188/OAm7F2vGfAUso44ka8XsgMvSt92Lvi3M7s0la2Ff5VgBalPxRUVAxYSokkswqZPUJUWNbrxXr9EJXmH1PWgEU2YO4dwb2CnNqabL0rNz2bzw7N+mEOS2k1rLs7fHx81OqxssliaSc9mjnRtKSZtS0kCU6uF7luirvnElFhbTt5CScuaEsRdKhbCSEB15fkTSZ2/qC2K7OQLV5aPQQj4m/FUiJflLLyqcz4KIuprfIlLV94EhiZ6Uu6NJi5LaTOMk3abPUm81mYYzioqq51IZ2tede+YGn7pwFPHxgRA5ZSJOuVyGJtZTEsJF/Q1hVsnXkKq9nbQq4Zs9WZzCc9IxOLd2gBi6e7K3o2DdW7SqSXi8eBHT9rZe/ywA3aJrhGxICllMmXz/Wup1HYL2kZepL3cvYvPLYFUf7WHY7F+UTtD6lu4SEI8DbWbBAqOy7r3gMs2i7daPck4KXt+2ZEzv3NRkTkhBZsty/F35fDQU7LNfEMsP0H7cDTH2g7HEbGgIWIyIkkp2Vg6a4zquzv7Y7ODfPZRZ4cmu/2r+CSmaYd3DgMKFcBRsaAhYjIiazYG4PLqdoQwO1NQ+HtwSn0TikxBj5752hlDx+g/VMwOgYsREROZGEkl+InwOXfT+CSkaI1RZuHAd9Khm8WBixERE4iPjkdfx84p8oh/l5oF1ZR7yqRHpIuAP/NUEWLm5c2ldkEGLAQETmJlQcvIi1DW2+jd/OqcJMlbsn5/PspXNIua+WWg4EAc6xyzICFiMhJ/LX/gq3M2UFOKjkO2Pi5Klpc3WHp8AzMggELEZETiI67gm2nElVZdmVuVi1Q7yqRHjZ9CaTEqeKVBn2B8jVN8zkwYCEicgKLd0RbF19XvSvc/sEJpV4GNnyiihYXV1xu+RjMhAELEZETWBgZbSv3ac69g5zS5pnAlaxhwab9kRFYC2bCgIWIyMEdiknA7tPxqixDQWHBfnpXicpa2hVg/Ye2Q8vNI033GTBgISJycAuy7czct4U5ZoRQCds2C0g8q5Ub9QGCw03XxAxYiIgcmMVisQUsMon5zmYMWJxOeiqw9n37cafRMCMGLEREDmz7yUs4cSFJlVvX8EdIQOnvHk8Gs+MnIP6UVm7QE6jSHGbEgIWIyEmGg3qGB+laF9JBRjqw5l37cUdz9q4IBixERA4qPSMTi3doAYunuys61zP2brxUCnb9Clw8qpXDOgM1bjBtMzNgISJyUOsPx+J8Yqoqd2kYDD8v7szsVDIzgTVv2487jYGZMWAhInKG2UFce8X57F0InD+glWu2B2rdBDNjwEJE5ICS0zKwdPcZVfb3clc9LORELBZg9VW9Ky7m3uySAQsRkQNasTcGiSnpqtyzaSi8PDgc5FQOLAXO7tTKVVsBdbvC7BiwEBE5oAXbo2zlfi2r6VoX0qN35S2H6l0RDFiIiBxMXFIa/t5/TpWD/b3QLqyi3lWisnRkFRC1RStXbqqtveIAGLAQETmYP3dFIzUjU5V7R1SFm6v5/7qmIsieu9JxFODqGF/1jvFTEBFRrrOD+rXkzsxO5dg64Pg6rVyxPtC4LxwFAxYiIgdyJi4Z/x6NVeU6lXzV7szkRNZknxk0GnB1nGRrBixERA5kUeRplXMp+jSvChcHSLakQjq1BTi8UiuXrwU0HeBQTWeKgOXYsWN45JFHUKdOHZQrVw5169bFxIkTkZqqreBotWPHDnTs2BHe3t6oUaMG3norW5Z0Hk6cOIFevXrBx8cHISEhGDNmDNLTtamARERmsyDSPjuobwsOBzmV1dPs5Y4jATd3OBJT/DT79u1DZmYmPv/8c9SrVw+7du3CsGHDcPnyZbz9ttb9FR8fj9tuuw3du3fHZ599hp07d+Lhhx9G+fLlMXz48FxfNyMjQwUroaGhWL9+PaKjozFkyBB4eHjg9ddfL+Ofkojo+hyKScSuqHhVjqgeiLBgPzaps4jeARz4UysHVAOa3w9HY4qApWfPnupmFRYWhv379+PTTz+1BSyzZ89WPS5ff/01PD090aRJE2zfvh3vvvtungHLX3/9hT179mD58uWoXLkyWrRogSlTpmDs2LF45ZVX1OsQEZnFwmxrr8hwEDmRNe/Yyzc9C7h7wdGYImDJTVxcHIKC7Fulb9iwAZ06dcoRZPTo0QNTp07FxYsXUaHCtbuUynOaNWumgpXsz3niiSewe/dutGzZMtf3TklJUTcr6d0R0gskNz3I+1osFt3e30jYFmwHZ7we5Ge0zg6StJU7m4Xm+JmdpR0K4pDtcG4/XPYsgGQrWXxDYGkxWNv40CTtUNg6mDJgOXToED766CNb74o4c+aMynHJzhqIyH25BSxyPnuwcvVz8vLGG29g0qRJ15w/d+4ckpOTodcHLkGcXICuDjLnvrjYFmwHZ7wedkVfxvELSarcuro/kByPmGTtjylnaoeCOGI7BK58A+WgZVonNBuKpIsJUjJNOyQk5F9XQwQs48aNUz0g+dm7dy/Cw8Ntx1FRUWp4aODAgSqPRQ/jx4/HyJEjc/SwSJJvcHAwAgICdKmTXHwyG0DqoPfFpze2BdvBGa+HtRv32MoDbqilJhE4YzsUxOHa4cJRuBxcrIqWchXg13kE/Dz9TNUOMlHG8AHLqFGjMHTo0HwfI/kqVqdPn0aXLl3QoUMHfPHFFzkeJ4mzZ8+ezXHOeiz35UbOb9q0qUjPEV5eXup2NfnQ9fzg5eLTuw5GwbZgOzjT9ZCekYnFO7VeYU83V9zerGquP6ujt0NhOVQ7rH8fsGSooku7p+DiHWC6dijs++sasEhkJ7fCkJ4VCVZat26NmTNnXvMDtm/fHi+++CLS0tLULB+xbNkyNGzYMNfhIOtzXnvtNcTExNj+GpHnSC9J48aNr/vnIyIqCxuOxOJ8opZX1zU8BIHltN+B5OAunQS2/6iVvQKAG/UZdSgrpggvJVjp3LkzatasqfJWJFdEckyy55kMGjRIJdzKei2SMPvzzz/jgw8+yDF089tvv+UYXpJp0BKYPPjgg4iMjMTSpUsxYcIEPPXUU7n2oBARGdH8bfal+Ln2ihNZ/yGQmaaV2z4GlCsPR2aKpFvp9ZBEW7lVr149x32SMCQCAwPVNGUJNqQXplKlSnj55ZdzTGmWBCOZDm3l5uaGxYsXq1lB0tvi6+uLhx56CJMnTy7Dn46IqPiS0zKwdLf2x5u/lzu6hOfMXSEHlXAW2PKtVvbwBdo+AUdnioBF8lwKynURERERWLNmTZFep1atWvjjjz9KpJ5ERGVt5b4YJKZoq3P3bBoKbw/H2TuGCuhdychaXuOGhwHfig7fXKYYEiIiotzN35Z9Kf5qbCZncDkW2Py1VnbzAtqPgDNgwEJEZFJxSWn4e/85VQ7290L7uo7/VzYB+Hc6kKatuYPWDwH+OdcTc1QMWIiITGrJ7mikZmirhPaOqAo3V+7M7PCuXAI2ZS3r4eqhLcPvJBiwEBGZFGcHOaFNXwIpWSsYtxgEBOaciOLIGLAQEZnQmbhk/Hs0VpVrV/RRuzOTg0tJAP79RCu7uAE3Pw9nwoCFiMiEFu84jaxVHVSyraxaSg5u89fAlYtaudlAICjn/nmOjgELEZEJzd9unx3Up0VVXetCZSDtCrD+o6wDF6CjfVFUZ8GAhYjIZA7FJGJXlJbH0KxaIOoGF7zZHZnc1u+Ay9qMMDTuCwQ3hLNhwEJEZDILI7kUv1NJTwHWfWA/7jQGzogBCxGRich2JAuyhoMkbaV3cw4HObztPwDxWUOADe8AQpvCGTFgISIykchTcTgeqy0a1j6sIioHeOtdJSpNGWnA2nftxx1HO217M2AhIjIRa++K6Mel+B3fzrnApRNauW5XoHprOCsGLEREJpGekYlFkdGq7Onmih5NQ/WuEpWmzAxgzTtw9twVKwYsREQmseFILM4najv0dgkPRmA5D72rRKVpzwIg9qBWrnUzUKuDU7c3AxYiIpNYsN0+O4jDQQ4uMxNY/bb9uJPz5q5YMWAhIjKB5LQMLNl1RpX9vdzRJTxE7ypRaTrwJxCzWytXawOEdXb69mbAQkRkAiv3xSAxJV2VJXfF28NN7ypRaZE9F1ZPy5m74sKtFxiwEBGZAGcHOZHDK4DT27Ry5WZAgx5618gQGLAQERlc3JU0rNqnLcteyc8L7etW1LtKVJq9K/9k710Zzd6VLAxYiIgMbsmuaKRmZKpy7+ZV4ObK4QGHdXwdcPJfrVypIdCoj941MgwGLEREBsfZQU7kn7dy9q648mvaii1BRGRgZ+OT1foronZFH0RUD9S7SlRaTm4Cjv6jlSvUAZrczbbOhgELEZGBLYo8rdIaRJ8W1eDC2SKOK/u6Kx1HAm7uetbGcBiwEBGZZDiobwvuzOywoiOBg0u1cmANIOI+vWtkOAxYiIgM6vC5ROyMilPlZtUCUTfYT+8qUVn0rtz0LODuyba+CgMWIiKDYu+Kk4jZC+xdqJX9KgMtH9S7RobEgIWIyIAsFgsWbo9SZUlbuTOCw0EOK/uOzB2eATy89ayNYTFgISIyoB2n4nAsNkmV24dVRGggv8QcUuxhYNevWrlcENDmf3rXyLAYsBARGdD8rN4VwWRbB7b2XcCiLQqI9k8Bnr5618iwGLAQERlMRqYFiyKjVdnTzRU9m1bRu0pUGi6dACJ/0sregcCNw9nO+WDAQkRkMBsOx+J8YooqdwkPRmA5D72rRKVh7ftAprYDN9o+DngHsJ3zwYCFiMjQw0HVdK0LlZL4aGDb91rZ008LWChfDFiIiAwkOS0DS3adUWU/L3d0DQ/Ru0pUGtZ/BGSkauUbHgF8gtjOBWDAQkRkIKv2xSAxRRsm6Nk0FN4ebnpXiUra5fPA5q+1srs30P5ptnEhMGAhIjIQzg5yAhs+AdKvaOXWQwE/9qIVBgMWIiKDiLuShlX7zqlyJT8vtf4KOZikC8CmL7Wym6e2UBwVCgMWIiKDWLrrDFIztDU5ejevAnc3/op2OJu+AFITtHKLB4BAJlUXFv81EBEZBGcHObjkeODfT7Wyixtw83N618hUGLAQERnA2fhkbDgSq8q1KvqgefVAvatEJW3zDCD5klaOuBeoUJttXAQMWIiIDGBR5GlYLPa1V1xkx0NyHKlJwPqPsw5cgI4jda6Q+TBgISIygIWRp23lPs25M7PD2fotkHReKze9G6hUX+8amY673hUgInL2fYMWbI9SuzOLJlX9US/ET+9qUUnJzACO/A38/ab9XMdRbF9H7mF57bXX0KFDB/j4+KB8+fLX3P/NN9+oLtTcbjExMXm+bu3ata95/JtvZruwiIhKyZJd0bh56kqMnBNpO3fiwhV1nhzAnoXA+02BWXfbc1dkobjYw3rXzJRME7CkpqZi4MCBeOKJJ3K9/95770V0dHSOW48ePXDLLbcgJCT/RXkmT56c43kjRowopZ+CiEgjQckTs7YiOi45R5MkJKer8wxaHCBYmTMEiLcP9Snpydp5uZ8cc0ho0qRJtp6U3JQrV07drM6dO4eVK1dixowZBb62v78/QkNDS7C2RET5DwNNWrQHWTm2uZL7b20cCjdXJt+achhoyVggv094yTggvBfgyq0XHC5gKarvvvtODR8NGDCgwMfKENCUKVNQs2ZNDBo0CM8//zzc3fNumpSUFHWzio+PV//PzMxUNz3I+1osFt3e30jYFmwHo18PG4/EXtOzkp18zcn9G4+cR7sSWu3WiO2ghzJph2Pr4Hp1z0oOFiA+CpnH1gG1b4YeMg10PRS2Dg4bsEjPigQf2XtdcvPMM8+gVatWCAoKwvr16zF+/Hg1LPTuu+/m+Zw33njD1uOTnfTqJCfn/UuotD/wuLg4dQG6uppmpK9UsC3YDka/Hv47eLZQjzt06hzC/DIcth30UBbtUO7wvyjMKjrxUQeQ7NMAesg00PWQkJC18q+RA5Zx48Zh6tSp+T5m7969CA8PL9LrbtiwQT3v+++/L/CxI0fa58JHRETA09MTjz32mApKvLy8cn2OBDXZnyc9LDVq1EBwcDACAgKg18UnCcNSB70vPr2xLdgORr0eMjMtmLXxBKavy++vb7t61YMRElLR4dpBT6XeDnvmw2XTe4V6aEC1BggoIMeytGQa6Hrw9vY2fsAyatQoDB06NN/HhIWFFfl1v/rqK7Ro0QKtW7cu8nPbtm2L9PR0HDt2DA0bNsz1MRLI5BbMyIeu5wcvF5/edTAKtgXbwWjXw/HYy3hh7g5sPHqhwMdK1kpooDfahlWCawnmsBihHYygVNoh9TLw5wvAtlmFqQEQUBWutW+SLw7oxcUg10Nh31/XgEUiO7mVpMTERMyZM0f1kBTH9u3bVeMVNLOIiKiwvSrfrD+GaUv340qafXinY/1KWHtQW0gse2qmNTyZ2LsxE27N4vR24NdHgNhD9nM12gEnN2Yd5PIJ93yTCbdFZJoclhMnTuDChQvq/xkZGSqwEPXq1YOfn32RpZ9//ln1kAwePPia19i0aROGDBmCFStWoFq1amroaOPGjejSpYuaKSTHknArz61QoUKZ/nxE5HiOnEtUvSqbj1+0nateoRze6h+BDvUqqanLMhsoewKu9KxIsNKzaRWdak2FJsmi/04Hlr8CZKZp5zx8gTumAS0GAXsXabOFsifgBlTVgpXGfdjQjhqwvPzyy/j2229txy1btlT/X7VqFTp37pwj2fbuu+/OdXG5pKQk7N+/H2lp2oUlwzo//fQTXnnlFTXrp06dOipgyZ6fQkRUnGnLX689irf/2o+UdPsMiIfa18ILPcPh66X96pWgRKYubzp6ATEJyQjx98aNdYLYs2IGCWeB+U8Ah1fYz1VpAQz4GqhYVzuWoESmLh9fDySeBfwqA7U6sGelmFwskiJM10WSbgMDA1XGtZ5Jt7Kirwxl6T0eqTe2BdtBz+vhUEwCxszdgW0nslY2zdp9eWr/iBKbolwc/HdRgu1wcJkWrFw+Zz/X4Rmg60uAuyfMINNA3xmF/Q41TQ8LEZGRpWdk4ss1R/He8gNIzepVkQ2X/9ehDkb3aAAfT/66Nb30FGDZRGDjp/Zz0mty12dA3a561swp8F8QEdF12n8mAS/MjURk1gaGok4lX0wbEIE2tYPYvo7g3H5g7iPA2Z32c/V7AP2mA76V9KyZ02DAQkRUTGkZmfj8n8P4YMVBpGVoo+syC/nRjmEYeWsDeHtw2XXTk6yJrd8Cf44D0q9o59y8gNumADcO17rRqEwwYCEiKoa90fEY/Uskdp/WtuYQdYN9MW1gc7SqyVmGDiHpArDoWWBvto0Kg8OB/jOA0KZ61swpMWAhIioCyU+Z/vchfLzyENIz7b0qj91SF892q89eFUch+/zMG6b2/LFp8zBw22uAp4+eNXNaDFiIiAppV1Sc6lXZd8a+90mDyn6YNqA5mte4dikFMqGMdOCfN4E17wCWrCnp5SoAfT4GGt2pd+2cGgMWIqICpKRn4KMVh/DpP4fVGivCzdUFT3aui6e71oOXO3NVHMLFY8Cvw4BTm+znancE7vocCKymZ82IAQsRUf4iT17CmLmROHA20XauUZUANQOoabXC7MlLprBzLrD4eSAlKyfJxQ3o8n/Azc9zoTeDYA8LEVEuktMy8P7yg/hi9WFkdarA3dVF9ag82bkePN2de4FGh5GSAPw5Ftg+236ufC1txdrqbfSsGV2FAQsR0VW2nriIMb9E4vC5y7ZzTapKr0pzNK6qz2rWVAqitmqbFl44Yj/X7B6g1zuANz9no2HAQkSUrVflnb/2Y8bao7ZeFQ83FzX7R2YBebixV8UhSDLtug+AVa8CmenaOU8/oNe7QPN79a4d5YEBCxERgM3HLqidlY+ct/eqRFQPVL0qDUP92UaOIuEMKix+BK5R6+3nqrUG+n8FBIXpWTMqAAMWInJqSanpmLZ0P75Zf0wtaiokP+X57g0wrGMduLNXxXHs/xMuC56CV1Js1gkXLalWkmvdPHSuHBWEAQsROa1/j8Ri7K87cDw2yXauZc3yagZQvRD2qjiMtGRg2UvApi8kRFEs/lXgItOVw27RuXJUWAxYiMjpXE5Jx9Ql+/DdhuO2c17urhh9W0M8fHMdtcYKOYiYvdqmhTG7baeSa3eF54Av4OIXrGvVqGgYsBCRU1l36LzqVTl1MWsjOwA31K6Aqf0jEBbsp2vdqATJ+N7mGcDSF4H0ZO2cuzcyb3sVl2rciRCfimxuk2HAQkROISE5DVOXHsAPG0/Yznl7uGJsz3A81L42XNmr4libFi4cAexbbD8X0ljbtFA2L4yJ0bN2VEwMWIjI4W08Ho83V+5GdFzWX9oA2tYJwlsDIlCroq+udaMSdnQ1MG84kBBtP3fjcODWyYBHOSAza38gMh0GLETksOKT0/Dq4j2Ys/mU7ZyPpxvG3x6OB9rWYq+KI8lIA1a9Dqx9T8aDtHPlgoB+04GGt+tdOyoBDFiIyCGt2heD8fN24ky8vVelQ92KKlelRpCPrnWjEiYr1f76KBC1xX6uzi3apoUBVdjcDoIBCxE5lLikNExevAe/bs3eq+KKF+9ohEFta8HFhTOAHErkz8Dvo4DUBO3Y1R3o+hLQ4RnAlSsTOxIGLETkMJbtOYsXf9uJmIQU27mO9SthVMcqiKhXncGKI0mOB/4YDez42X5OVqqVFWtl5VpyOAxYiMj0Ll5OxaRFuzF/+2nbOX8vd7x0Z2P0b1UV586d07V+VMJObdY2Lbx4zH6u+SDgjrcALy7456gYsBCRqS3ZFY0J83fjfKK9V6VLw2C8fnczVAksh0zOCnEcmRnAuve15FrrpoVeAcCd7wHNBuhdOyplDFiIyJRiE1MwceFuLN5hn74a4O2Oib2b4O5W1Tj842jiT2vTlY+tsZ+rfiPQ/0ugQm09a0ZlhAELEZmKxWLB7zujMXHBbsReTrWd796oMl67qykqB3jrWj8qBft+BxY8BVy5mHXCBeg0GrhlHODGrzFnwU+aiEzjXEIKXpq/C0t2n7GdK+/jgUl9mqBP86rsVXE0aVe0pfVliX2rgGrA3V8AtW/Ws2akAwYsRGSKXpWFkafVENClpDTb+Z5NQjGlX1ME+3vpWj8qBWd3A3MfBs7ts59r1Bvo/SHgE8Qmd0IMWIjI0GLik/F/v+3C8r1nbeeCfD0xuW8T9GpWhb0qjrhp4aYvgb8mABlZidTu5YDb3wRaPQRwHR2nxYCFiAzbqzJva5SarhyfnDUjBECviCqY3KcJKvqxV8XhXD6v5aocWGI/V7kZMEA2LWyoZ83IABiwEJHhnIlLxvh5O7Bqv339lEp+nni1X1P0bMql1h3S4VXAb48Difb8JLR7Eug2EfBgIjUxYCEig/Wq/LL5FKb8vgcJ2XpV+raoild6N0EFX09d60elID0VWDkFWP+h/ZxPJaDfp0CD29jkZMMeFiIyhKhLV9RmhasP2HtVJJn29bua4dbGlXWtG5WS2MPairWnt9nP1e0K9PsM8OdnTjkxYCEi3XtVftx0Eq//sReJKfZeFVn87eU7G6O8D3tVHDKxNvJH4PfRQNpl7ZyrB9D9FW0YiJsWUi4YsBCRbk5eSMK4eTuw7lCs7VxogDdev7spuobzL2yHlBwHLH4e2PWr/VzFekD/GUDVFnrWjAyOAQsRlbnMTAtmbTyON//ch6TUDNv5e9vUwP/1aoTAch78VBzRyU3aENClE/ZzLR8Eer4JePnpWTMyAQYsRFSmjsdexgtzd2Dj0Qu2c1UDvfFG/wjc0iCYn4ajblq45h3g7zcBS1aA6hUI9H4faHq33rUjk2DAQkRl1qvyzfpjmLZ0P66k2XtVBrWtifG3h8Pfm70qDinulLZp4fF19nM12mmbFpavqWfNyGQYsBBRqTt6XnpVIvHfMevmdUD1CuUwtX8EbqpXiZ+Ao9qzEFg4Aki+pB27uAK3jAU6juamhVRkDFiIqNRkZFrw9dqjePuv/UhJz7SdH9K+Fsb2DIevF38FOaTUy8CS8cDWb+3nAmsAd38J1GqvZ83IxPjbgohKxaGYRIyZG4ltJ7L+ugZQM8hH9aq0r1uRre6oondoibXnD9jPNe6n5auUq6BnzcjkXGESr732Gjp06AAfHx+UL18+18e4uLhcc/vpp5/yfd0LFy7ggQceQEBAgHrdRx55BImJiaX0UxA5vvSMTHz692Hc8eEaW7Ai+9X976baWPJcRwYrjry2yobpwFfd7MGKhw/Q52Ng4DcMVsh5elhSU1MxcOBAtG/fHjNmzMjzcTNnzkTPnj1tx3kFN1YSrERHR2PZsmVIS0vD//73PwwfPhw//PBDidafyBkcOJuAMb9EIvJUnO1cnUq+eGtABG6oHaRr3agUJZ4D5j8BHFpmP1eluba2SqX6bHpyroBl0qRJ6v/ffPNNvo+TACU0NLRQr7l3714sWbIE//33H9q0aaPOffTRR7jjjjvw9ttvo2rVqiVQcyLHl5aRic//OYwPVxxCakamrVfl0ZvrYOStDVHO003vKlJpObQc+O0J4HKM/Vz7p4FuLwPu3FGbnDBgKaynnnoKjz76KMLCwvD444+rHhMZGsrNhg0bVIBjDVZE9+7d4erqio0bN+Kuu+7K9XkpKSnqZhUfH6/+n5mZqW56kPeVJc71en8jYVuUbTvsjY7H2F93Ytdp7d+BqBvsi6n9m6FVTS1nQc/rktdDKbVDegpcVk6By7+f2E5ZfENg6TsdqNfN+qYwGl4PxmuHwtbBoQKWyZMno2vXrirP5a+//sKTTz6p8lGeeeaZXB9/5swZhISE5Djn7u6OoKAgdV9e3njjDVuPT3bnzp1DcnIy9PrA4+Li1AUoAZczY1uUTTtIr8q3/53BzE3RyOpUgasL8EDryni0XVV4uachJibbX9064fVQ8u3gdukIyi8fBY/ze2znUmp0QlzXN5FZriJggM89L7wejNcOCQkJxg9Yxo0bh6lTpxY4bBMeHl6o13vppZds5ZYtW+Ly5cuYNm1angFLcY0fPx4jR47M0cNSo0YNBAcHq+RdvS4+6UmSOuh98emNbVH67bD7dBzGzN2JfWfsv2gahPhh6oBmaF49/7yxssbroQTbQRJrt8+Cy5JxcElL0k65ecLSfRI8bnwMlfLozTYSXg/Gawdvb2/jByyjRo3C0KFD832MDO0UV9u2bTFlyhQ1fOPlde1YquS6XP0XYHp6upo5lF8ejLxWbq8nH7qeH7xcfHrXwSjYFqXTDinpGfh45SFM//uwWmNFuLm64MnOdfF013rwcjdmrgqvhxJohysXgUXPAXvm289VagCXAV/DJbQZzITXg7HaobDvr2vAIpGd3ErL9u3bUaFChVyDCyEzji5duoQtW7agdevW6tzKlStV5CnBDhHZ7Th1CaN/icSBs/Zp/+Gh/nh7YHM0rRbIpnJkxzcA84YBcSft51oPBXq8AXj66FkzciKmyWE5ceKE6vmQ/2dkZKhgRNSrVw9+fn5YtGgRzp49i3bt2qnuJZmm/Prrr2P06NG219i0aROGDBmCFStWoFq1amjUqJGaAj1s2DB89tlnalrz008/jfvuu48zhIiyJKdl4IMVB/HF6iO2XhV3VxfVo/Jk53rwdGePnsPKSAdWTwNWvwVYshKVvMsDfT4CGvfRu3bkZEwTsLz88sv49ttvc+SoiFWrVqFz587w8PDAJ598gueff14lEUkg8+6776pgxCopKQn79+9XgYnV7NmzVZDSrVs31S3Vv39/fPjhh2X80xEZ09YTF9XOyrJqrVWTqgGYNqA5GlfVJ1+LysilE8Cvw4CT/9rP1boJuPsLILA6PwYqcy4W+Xan6yJJt4GBgSrjWs+kW8nHkVlPeo9H6o1tcf3tIL0q7y47gK/WHEFWpwo83FzwTNf6eLxzXXi4meca4/VQjHbYNU/LV0nJWgDQxQ3oPB7oOBJwNWaeUmHxejBeOxT2O9Q0PSxEVDY2H7ugelWOnL9sOxdRPVD1qjQM9efH4MhSEoElY4Fts+znytfUVqytcaOeNSNiwEJEmiupGZi2dD9mrj+qZq8KTzdXPHdrfQzvGAZ3E/WqUDGc3q5tWhh7yH6u6QDgzncBbyZVk/7Yw0JE+PdILMb+ugPHY7W1NUSLGuXx9sAI1Athr4pDk1VGZbXa5ZOAzKz8Pk8/4I63geb3aXssEBkAAxYiJ3Y5JR1Tl+zDdxuO2855ubti1G0N8MjNYWqNFXJgCWeB+Y8Dh1faz1VtqQ0BVayrZ82IrsGAhchJrT90Hi/8ugOnLl6xnWtTq4LaWTks2E/XulEZOPCXtsNy0vmsEy7ATc8CXV4E3D35EZDhMGAhcjIJyWl44899+GHjCds5bw9XvNAjHA91qM1eFUeXngysmAxs/NR+zi8UuPtzIKyznjUjyhcDFiInsvrAOYyftxNRl+y9KjfWCcJb/SNQu5KvrnWj0ud28TBcfnsBOLvLfrLB7UDfjwHfSvwIyNAYsBA5gfjkNLy2eC9+3mxfWt3H0w3jbg/H4La14MpcFccm0762fINKS8fDRXpYhJsX0OM14IZHmVhLpsCAhcjB/b3/HF6cvwvRcVlfVAA61K2Iqf0jUCOI+8A4vKQLwKJn4Lp3kf1ccCNgwAygchM9a0ZUJAxYiBxU3JU0TF56DH/sjbWd8/V0w//1aoRBN9ZUO7WSgzu2Fpg3HIiPsp2ytHkELtKz4lFO16oRFRUDFiIHtHzPWfzfbzsRk5BiO9exfiW8cXczVK/AXhWHl5EG/DMVWP22hCjqlKVcBVzq9CoC2w6Ci5Nv30HmxICFyIFcvJyKSYt2Y/7207Zzfl7ueOnORrinTQ32qjiDi8eAXx8FTv1nP1e7Iyz9PkNKMn/lk3nx6iVyEEt2ncGE+btwPtHeq9K+dgDevrcVqlXgDCCnsOMX4PeRQEq8duzqrq2rIuuryDoryTF615CobAKWvXv34qeffsKaNWtw/PhxJCUlITg4GC1btkSPHj3Qv39/eHl5Fb82RFRksYkpmLhwNxbviLadC/DWelVuruaByoHMVXB4KQnAH2OAyB/t5yrUBvp/DVRvbV+Cn8jRA5atW7fihRdewNq1a3HTTTehbdu2uOuuu1CuXDlcuHABu3btwosvvogRI0aoxz333HMMXIjKwO87ovHygl2IvZxqO9e9UQheu6sZgv081fbx5OCitmhDQBeO2M9F3AfcMQ3wDtCzZkRlH7BIz8mYMWMwd+5clC9fPs/HbdiwAR988AHeeecd/N///V9J1pOIsjmXkKIClT93nbGdK+/jgUl9mqBP86oqVyWTf1E7Nvl8138IrJwCZKZr5zz9td2VI+7Ru3ZE+gQsBw4cgIeHR4GPa9++vbqlpWXt+ElEJcpisWBh5Gm8snA3LibZ/531bBKKyf2aIMTfmy3uDOKjgd8eA47+Yz9XrQ3Q/ysgqI6eNSPSN2ApTLByPY8nooLFxCerBeCW7TlrOxfk64nJfZugV7MqnAHkLPb/Ccx/ErhyIeuEC9BxJNB5PODG373kuIo1S+i///7DqlWr1Pj41d3O7777bknVjYiyelV+2xaFSYv2qMXgrHpFVMHkPk1Q0Y+J7k4h7Qrw10vAf1/az/lX1TYtrNNJz5oRGTNgef311zFhwgQ0bNgQlStXzvFXHVfOJCpZZ+KS1QJwK/fZk2cr+XliSt+muL1ZFTa3szi7B/j1ESBmj/1c+J1An48AnyA9a0Zk3IBFkmq//vprDB06tHRqRESqV+WXLacwZfEeJCRnJVQC6NuiKib2bqKGgshJNi387yvgrwmAddNCd2+gx+tAm4e5aSE5lSIHLK6urmpqMxGVjtOXrmDcvJ1YfeCc7Vywvxde69cUtzUJZbM7i8uxwMIRwP7f7edCmgADvgZCwvWsGZEuiryhxPPPP49PPvmkdGpD5OS9Kj9sPIHb3ludI1i5u1U1LHu+E4MVZ3LkH+Czm3IGK20fB4atZLBCTqvIPSyjR49Gr169ULduXTRu3PiaGUHz5s0ryfoROYWTF5Iwbt4OrDtk31m5coCX2qywa3hlXetGZbxp4arXgLXv2zYthE9FoN+nQIMe/CjIqRU5YHnmmWfUDKEuXbqgYsWKTLQlug6ZmRbM3ngcb/y5D0mpGbbz97Spjhd7NUZgOU5TdRqyUu3cR4DTW+3nwjoDd30O+HMokKjIAcu3336LX3/9VfWyEFHxHY+9jLG/7sC/R6zraQBVA73xRv8I3NIgmE3rTCJ/An4fBaQm2jct7PYy0H6EJA7qXTsicwYsQUFBajiIiIrfq/LthmN4a8l+XEmz96rcf2NN/N8d4fD3Zq+K00iO1wKVnXPs54LqaivWVmulZ82IzB+wvPLKK5g4cSJmzpwJHx+f0qkVkYM6ev4yXpgbif+OXbSdq1a+HN4aEIGb6lXStW5Uxk7+p62tcum4/VyLwcDtUwEvP34cRNcbsHz44Yc4fPiwWjSudu3a1yTdys7ORJRTRqYFM9cdxbSl+5GSbl8d+sF2tTD29nD4eRVr0Wkyo8wMYO17wKrXAUtWD5tXAHDne0CzAXrXjsiwivxbsl+/fqVTEyIHdSgmUfWqbD1xyXauZpAPpvaPQPu6FXWtG5WxuCht08Jja+znarQF7v4SqFCLHwdRSQYsMhxERAVLz8jEV2uP4t1lB5Ca1asiO1k81L42XujZED6e7FVxKnsXAwufBq5kDQe6uAKdxgCdXgDceC0QFcS9sAtacZ8gosI7cDYBY36JROSpONu5OpV8Va7KDbW594tTSU0C/noR2Py1/VxAdeDuL4DaXDWcqLAKNV+uSZMm+Omnn5Camprv4w4ePIgnnngCb775ZqErQORI0jIy8fHKg7jzw7W2YEV6VR69uQ7+eKYjgxVnc2YX8GWXnMFKoz7AE2sZrBCVRg/LRx99hLFjx+LJJ5/ErbfeijZt2qBq1arw9vbGxYsXsWfPHqxduxa7d+/G008/rYIWImezNzoeY+ZGYldUvO1cWLAvpg1ojta1KuhaN9Jh08JNXwB/vQRkpGjnPHyAnm8CrYZw00Ki0gpYunXrhs2bN6ug5Oeff8bs2bNx/PhxXLlyBZUqVULLli0xZMgQPPDAA6hQgb+YyblIfsr0vw/hk1WHkJahLafu6gIM6xSG57s3gLeHm95VpLJ0+Tww/0ng4FL7udBmQP+vgeAG/CyIiqlImV4333yzuhGRZldUHMbM3aF6V6zqh/hh2sDmaFGjPJvJ2RxeCfz2OJB41n6u3VNA94mAu5eeNSMyPaamExWzV0VyVab/fRjpmVqvipurC564pS5GdKsHL3f2qjiV9FRg5WRg/Uf2c77BQL/PgPrd9awZkcNgwEJURDtOXcKYX3Zg/9kE27nwUH+Vq9KseiDb09nEHgbmPgxEb7efq9sNuOszwC9Ez5oRORQGLESFlJyWgQ9XHMTnq4+olWvVPyBXFzzVpZ66ebpzkzqnS6zd/gPwxxgg7bJ2ztUDuHUS0PYJblpIVMIYsBAVwrYTF1Wuiqxaa9W4SgCmDYxAk6rsVXE6Vy4Bv48Edv1qP1exPjBgBlCluZ41I3JYDFiICuhVeW/ZAXy55giyOlXg4eaCZ7rWx+Od68LDjb0qTufERuDXR4G4E/ZzMlVZpix7+upZMyKHVqyAJTMzE4cOHUJMTIwqZ9epU6eSqhtRmZEhnk1HLyAmIRkh/t64sU4Qtp+8qHJVjpzP6u4H0KxaoOpVCQ8N4KfjDJsUHl+vzfjxq6zt+SObFv4z1b5poXcg0PtDoAn3WCMyXMDy77//YtCgQWodFlmyPztZvj8jI+sfcgl77bXX8Pvvv2P79u3w9PTEpUv2jeREZGSkWmFX1oo5f/682kn68ccfx7PPPpvv68rj5GfJ7o033sC4ceNK5ecg41myKxqTFu1BdFyy7ZyPpxuSUu3XsqebK567tT6GdwyDO3tVHN+ehcCSsUD8afs5N08gI9tq3zU7aMvrl6+hSxWJnE2RAxYJAmSlWwkeqlSpUmZ7DMm2AAMHDkT79u0xY8aMa+7fsmULQkJCMGvWLNSoUQPr16/H8OHD4ebmplbfzc/kyZMxbNgw27G/v3+p/AxkzGDliVlbkTP0Ro5gRdZTmTYgAvUr87pwmmBlzhDJqs153hasuACdxwMdR3HTQiIjByyyX9DcuXNRr149lKVJkyap/3/zzTe53v/www/nOA4LC8OGDRswb968AgMWCVBCQ0NLsLZklmEg6Vm5OljJLsDbHXMea88ZQM40DCQ9K/ldFb6VgE6jAVeutUNk6IClbdu2Kn+lrAOW4oiLi0NQUME748pQ0pQpU1CzZk013PX888/D3T3vpklJSVE3q/h4bZVTyee5OqenrMj7yhCdXu9vJIVti41HYnMMA+UmPjkdm4/Fol1YRZgNr4litMOxdXDNPgyUm8vnkHlsHVDbXKt+83pgOxj1eihsHQoVsOzYscNWHjFiBEaNGoUzZ86gWbNm8PDwyPHYiIgIGIEMCcm+RzJ0lZ9nnnkGrVq1UoGNPGf8+PGIjo7Gu+++m+dzJMfF2uOT3blz55CcnP8XYGl+4BKgyQXo6urcM1cK2xaHTl0o1OsdOnUOYX6lk5tVmnhNFL0dvKMOoDAbKsRHHUCyj7n2BeL1wHYw6vWQkGBfhPO6A5YWLVqoXJXsSbbZh2Cs9xU16VYSW6dOnZrvY/bu3Yvw8HAUxa5du9C3b19MnDgRt912W76PHTlyZI5gSxJ6H3vsMRWUeHnlvveHBDXZnyc9LJI3ExwcjICAAN0uPml/qYPeF5/eCtsW9RKlS/9oga9Xr3owQkLM2cPCa6KI7ZBUuCAkoFoDBISYaxVbXg9sB6NeD97e3iUXsBw9WvAv9eKQnpqhQ4fm+xjJRSmKPXv2qN2lJeF2woQJKM6QV3p6Oo4dO4aGDRvm+hgJZHILZuRD1/ODl4tP7zoYRWHaom6Iv9pV2bq+yjWvIZvsBnqjbVgluMoDTYjXRBHbofZNgKcfkJqYV4sCAVXhKo8z4b8zXg9sByNeD4V9/0IFLLVq1bKVV69ejQ4dOlyT4yFf8jKkkv2xBZHITm4lZffu3ejatSseeughNQ26OGTatDSezDgix5WWkYlnftyWb7AiJvZurDY1JCch+wGlJuVxZ9Z1IAvEMeGWqMwVOazq0qULLly4duxfxsLkvtJy4sQJFUzI/2XYScpyS0xMtA0DyfvLEJAM10iOjdwkr8Rq06ZNangpKipKHcssovfff1+t4XLkyBHMnj1bJdwOHjwYFSpUKLWfhfT36uI92Hj0gm0mUIh/zh4z6Vn5dHAr9GxaRacaUplLSwbmPymd5dqx11XT2AOqAvd8BzTuww+HyAyzhKy5KleLjY2Fr2/pLUv98ssv49tvv7Udt2zZUv1/1apV6Ny5s5pqLcGJrMMiNyvp8ZHhHZGUlIT9+/cjLS1NHcuwzk8//YRXXnlFzfqpU6eOCliy56eQ45nz30l8u+G4bUG4bx6+Ec2rl79mpVv2rDiZv18Hzu3TylVaAA8vBU79Z1/ptlYH9qwQ6cjFcvVytXm4++671f8XLFiAnj175sjhkB4PmUkkOR9LliyBs5Gk28DAQNXLpGfSrWyVIENZeo9H6i2/tth64iLu+/xfpGZof0W/1T8C99zgmCuV8pooQjuc3AR83QOwZGor2g7/B6jcGI6E1wPbwajXQ2G/QwvdwyIvJiS+kYXWypUrZ7tPZta0a9cux2qxREYTE5+Mx7/fYgtWhrSv5bDBChWB5Kz89rgWrAhZxdbBghUiR1DogGXmzJm2vXdGjx5dqsM/RCUtJT0Dj83agpgEbcE/GfJ56U5+KRGAlVOAC4e1pqjWBujwDJuFyBFyWGRtEyFdSZIPImQoiLNqyKikV/Dl+bux7YS2YWbVQG9Mf6AVPLiJIcmKtf9+qrWDuzfQ71PuD0RkUK7FWZHuwQcfRLVq1XDLLbeom5RlZo2MPxEZzax/j+PnzSdV2cvdFV8MaYNKfrkvCkhOJCURWCCzgrLS+Lq+BASba/VaImdS5IDl0UcfxcaNG7F48WJcunRJ3aS8efNmtUIskZHIfkGywaHVWwMi0LSalo9FTm75K8BFbQYharYH2j2hd42IqCSHhCQ4Wbp0KW6+2b7xV48ePfDll1+q2UNERnH60hU8OXsr0rNWhxveKQx9W1TTu1pkBEf+Bv77Uit7+AB9P+GUZSJHC1gqVqxomzGUnZzjYmtkFMnpmXh6zlbEXk5Vxx3rV8ILPXLfaoGcTHI8sOBp+3H3SUDFunrWiIhKY0hI9uexriRrJeUxY8bgpZdeKurLEZVKku2by49j1+l4dVwzyAcf3d8S7kyyJfHXBCBOy2lC7Y7ADY+yXYgcsYfl008/xaFDh1CzZk11E7JcviwkJyvNfv7557bHbt26tWRrS1QIM9Ydw5J92rL7Pp5u+GJIa5T38WTbEXBwObA1a8Vs2eRQDQU590KLRA4bsPTr1690akJUAtYePI83/8xaXh3AOwObIzxUn9WHyWCS44CFI+zHt00BKhR+s1YiMuk6LERGcyI2CU//uNW2A/PTXeri9mbcvJA0LkvHAwmntYO6XYHW/2PTEJlIsfpCZSrzV199hfHjx9t2bpbhH+suyERl7XJKOoZ/vxmXkrSNLW+qE4jnutXnB0GK17EVcIn8MesgAOjzEZDLJq5E5EA9LLLJYffu3dWsINkFWfYPCgoKwrx581Quy3fffVc6NSXKJ8l2zNxI7DuToI7DKvliUs86cHXlFxLJNu0XELA6W89wzzeAwOpsGiJH72GRGUJDhw7FwYMH4e3tbTt/xx13YPXq1SVdP6ICTf/7MP7Yqc1a8/dyxxcPtoKflxtbjhSXJS/ALemcdlC/B9DiAbYMkTMELP/991+uK9rK8vzZpzoTlYWV+87i7b+0Pa2kh/+D+1sgLNiPjU+aPQvgsutXVbR4lwd6f8ChICJnCVhk+nJ8vLa+RXYHDhxAcHBwSdWLqECHzyXi2R+3w5KVZDvq1gboGl6ZLUeay+eBxSNtrWG5fSoQwCRsIqcJWPr06YPJkycjLU1LbnRxcVG5K2PHjkX//v1Lo45E14hPTsOw7zYjISVdHd/RLBRPdanHliKNRLGLnweSzqvD5Dq3Ak0HsnWInClgeeedd5CYmIiQkBBcuXJF7dZcr149+Pv747XXXiudWhJlk5lpwcift+PIucvquGFlf0wb0FwFz0SKDAPtXaiKlnJBiO/4CoeCiJxtlpDMDlq2bBnWrl2rZgxJ8NKqVSs1c4ioLLy//ACW743RrsdyHmolW1+vIl/K5KgSzgJ/jLYdWnq9g0yfSrpWiYiuX7F/y8tuzdl3bCYqC0t2RePDlYdUWWYtfzyoJWpV9GXjk30oaNGzwJWL2nGTu4DG/YAYLcAlIicJWDIzM/HNN9+oNVdkDRbpgq9Tpw4GDBiABx98kF3yVKr2n0nAyDmRtuPxtzdCx/pM9KZsIn8CDvyplX2DgTveYfMQOVsOiyzOJQm3jz76qFrRtlmzZmjSpAmOHz+u1mW56667Srem5NQuJaWqJNuk1Ax13K9FVTzasY7e1SIjiT8N/DnWfixTmH0r6lkjItKjh0V6VmRhuBUrVqBLly457lu5cqXaFFFWuR0yZEhJ1o8I6RmZGPHjNpy4kKRao2m1ALzZP4I9epRzKEg2NkyJ044j7gXCe7GFiJyxh+XHH3/E//3f/10TrIiuXbti3LhxmD17dknXjwjTlu7HmoPa9NSKvp74/ME28PbgSraUzdbvgEPLtbJfKCBrrhCRcwYsMiOoZ8+eed5/++23IzLSnl9AVBIWbI/C56uPqLK7qwumP9AK1cqXY+OS3aUTwNIX7ceysWG5CmwhImcNWGRX5sqV815FVO67eDErM5+oBOyKisMLc3fYjif2boy2YcxJoGwyM4EFTwOp2saXaDkYaHAbm4jImQOWjIwMuLvnnfLi5uaG9HRt1VGi63U+MQWPfb8FKemZ6vjeNjUwuF0tNizltOVr4Og/WjmgOtDjdbYQkbMn3cosIZkNJHsJ5SYlJaUk60VOLC0jE0/N3oqoS1fUccua5TG5XxMm2VJOF44Cf71sP+77EeAdyFYicvaA5aGHHirwMZwhRCXh1cV7sPHoBVUO8ffCZ4Nbw8udSbZ09VDQU0Catj0D2jwM1O3KJiJyYIUOWGbOnFm6NSECMOe/k/h2w3HVFp5urvjswdaoHODNtqGcNn0OHF+nlcvXBG6dzBYicnBF3vyQqLRsPXERE+bvsh2/2q8pWtXkbA+6yvlDwPJJ9uO+0wEvfzYTkYNjwEKGEBOfjMe/34LUDC3Jdkj7Wrjnhhp6V4uMJjMDmP8EkK7lN+HGx4A6HfWuFRGVAQYspLuU9Aw8PmsLYhK0xO0b6wThpTsb610tMqINHwOnNmnloDCg+0S9a0REZYQBC+lKZp9NXLAbW09cUsdVA73V4nAebrw06Sox+4CVr2UduAD9PgU8uVM3kbPgtwLpatbGE/jpv5Oq7OXuii+GtEElv9ynzpMTy0jXhoIyspZPaP8UULOd3rUiojLEgIV0s/FILCYt3G07fmtABJpW4zoalIt17wOnt2rlSg2ArhPYTEROhgEL6eL0pSt4cvZWpGda1PHwTmHo26IaPw261pldwN9vamUXV20oyIP7SRE5GwYsVOaS0zIw/PvNiL2cqo471q+EF3o05CdB10pPBeY/DmSmacc3PQdUb8OWInJCDFiozJNsx8/biV1R8eq4ZpAPPrq/JdyZZEu5WfMOcGanVg5pDHQex3YiclIMWKhMzVh7FL9ti1JlH083fDGkNcr7ePJToGud3g6seVsru7prQ0HuTMgmclYMWKjMrD14Hq//sdd2/M7A5ggPDeAnQNdKT9FmBWVm7QDfcTRQtQVbisiJmSZgee2119ChQwf4+PigfPnyuT7mxIkT6NWrl3pMSEgIxowZg/T0rF94ebhw4QIeeOABBAQEqNd95JFHkJiYWEo/hfM6EZuEp3/ciqwcW4zoWg+3N6uid7XIqCTJNmaPVg5tBnQcpXeNiEhnpglYUlNTMXDgQDzxxBO53p+RkaGCFXnc+vXr8e233+Kbb77Byy9n234+FxKs7N69G8uWLcPixYuxevVqDB8+vJR+Cud0OSVdJdleStISJ7uFh+D57g30rhYZ1anN2jRm4eoB9PsMcOewIZGzK/RuzXqbNEnb7EyCkNz89ddf2LNnD5YvX47KlSujRYsWmDJlCsaOHYtXXnkFnp7X/sLbu3cvlixZgv/++w9t2mgzDz766CPccccdePvtt1G1atVS/qmcI8l2zNxI7DuToI7Dgn3x3n0t4OrqonfVyIjSrmhDQRZtTyl0HguENtW7VkRkAKYJWAqyYcMGNGvWTAUrVj169FA9MtKD0rJly1yfI8NA1mBFdO/eHa6urti4cSPuuuuuXN8rJSVF3azi47UZL5mZmeqmB3lfCQ70ev+8fPL3Yfyx84wq+3m544vBreDn6Vaq9TRqW5Q1M7aDy4pX4XL+gCpbqraCpcOz8oM4XTuUBrYD28Go10Nh6+AwAcuZM2dyBCvCeiz35fUcyXXJzt3dHUFBQXk+R7zxxhu2Hp/szp07h+TkZOj1gcfFxakLUAIuI1h3NA7v/nVIlaU/ZVLP2vCzJCEmJsnp2kIPZmsHj+gtCPr3E1W2uHni/M1TkHH+gtO1Q2lhO7AdjHo9JCRoPfCGDljGjRuHqVOn5vsYGbYJDw+HkYwfPx4jR47M0cNSo0YNBAcHq+RdvS4+FxcXVQe9Lz5x5FwiJi6JRFaOLUbeWh93ta3nlG2hF1O1Q+pluMyZAJesK8bS5UVUDO/gfO1QitgObAejXg/e3t7GD1hGjRqFoUOH5vuYsLCwQr1WaGgoNm3K2nY+y9mzZ2335fWcmJiYHOdkVpHMHMrrOcLLy0vdriYfup4fvFx8etdBxCenYfisrUhM0WZo3dEsFE93ra/q52xtoTfTtMOqV4ELR7Ry9Rvh2mGE/INyvnYoZWwHtoMRr4fCvr+uAYtEdnIrCe3bt1dTnyUAsQ7zyMwf6fFo3Lhxns+5dOkStmzZgtatW6tzK1euVJFn27ZtS6ReziYz04KRP2/HkXOX1XHDyv6YNqB5mQYrZDJH1wAbP9PK7uW0BeJc3fSuFREZjGn+3JA1VrZv367+L1OYpSw365opt912mwpMHnzwQURGRmLp0qWYMGECnnrqKVtviPTAyPBSVJS20mqjRo3Qs2dPDBs2TN23bt06PP3007jvvvs4Q6iY3l9+AMv3ar1WgeU81Eq2vl4OkypFJS0lAVjwpP24+0SgUtkMHRKRuZjmm0TWU5G1Vayss35WrVqFzp07w83NTa2jIrOCpOfE19cXDz30ECZPnmx7TlJSEvbv34+0tKyN1ADMnj1bBSndunVT3VL9+/fHhx9+WMY/nWNYsisaH67Ukmxl1vLHg1qiVkVfvatFRrbsZeDSCa1c6ybgxsf0rhERGZRpAhZZfyWvNVisatWqhT/++CPP+yWwkYzo7GRG0A8//FBi9XRW+88kYOScSNvx+NsboWP9khnuIwd1eCWw+Wut7OEL9P2kRPNWiMix8LcDXbdLSakY9t1mJKVmqON+Lari0Y512LKUt+Q4YMEI+/Ftk4EgXjNElDcGLHRd0jMyMeLHbThxQVtbpWm1ALzZP4JJtpS/pf8HxJ/SynVuAVo/zBYjonwxYKHrMm3pfqw5eF6VK/p64vMH28DbgzM8KB8HlgLbZmllT3+g78ccCiKiAjFgoWJbsD0Kn6/W1s5wd3XB9AdaoVr5cmxRytuVi8DCZ+zHPV4DytdkixFRgRiwULHsiorDC3N32I4n9m6MtmEV2ZqUvz/HAolZ217UuxVoNYQtRkSFwoCFiux8Ygoe+34LUtK1DavubVMDg9vVYktS/vYuBnb8rJW9AoE+H8pSm2w1IioUBixUJGkZmXhq9lZEXbqijlvWLI/J/ZowyZbydzkWWPyc/fj2qUBAVbYaERUaAxYqklcX78HGo9oOuiH+XvhscGt4uTPJlgrwx2jg8jmt3PAOoPl9bDIiKhIGLFRoc/47iW83HFdlTzdXfPZga1QOKNwum+TEdv8G7J6nlctVAO58n0NBRFRkDFioULaduIgJ83fZjl/t1xStalZg61H+EmOAxSPtx3e8DfhXZqsRUZExYKECxcQn4/FZW5CaoSXZDmlfC/fcUIMtR/mTbTAWPw9c0YYQ0agP0LQ/W42IioUBC+UrJT1DBStn41PU8Y11gvDSnY3ZalSwnb8A+xZrZZ9KwJ3vcSiIiIqNAQvlSTaKnLhgN7aeuKSOqwZ6q8XhPNx42VAB4qOBP8bYj+98F/CtxGYjomLjNw/ladbGE/jpv5Oq7OXuii+GtEElPy+2GBU8FLToWSBZC3TRdADQuC9bjYiuCwMWytXGI7GYtHC37fitARFoWi2QrUUF2z4bOLhUK/tVBu6YxlYjouvGgIWucfrSFTw5eyvSMy3qeHinMPRtUY0tRQWLOwUsGW8/7v0B4BPEliOi68aAhXJITsvA8O83I/ZyqjruWL8SXujRkK1EhRsKWjgCSInXjpsPAhrezpYjohLBgIVyJNmOn7cTu6K0L5yaQT746P6WcGeSLRXGlm+Awyu1sn9VoOcbbDciKjEMWMhmxtqj+G1blCr7eLrhiyGtUd7Hky1EBbt4HPhrgv24z0dAufJsOSIqMQxYSFl78Dxe/2OvrTXeGdgc4aEBbB0qWGYmsOApIDVRO271EFC/O1uOiEoUAxbCidgkPP3jVmTl2GJE13q4vVkVtgwVzn9fAcfWaOXAGsBtr7LliKjEMWBxcpdT0lWS7aWkNHXcLTwEz3dvoHe1yCxiDwPLJ9qP+34MeLNnjohKHgMWJ0+yHTM3EvvOJKjjsGBfvHdfC7i6uuhdNTKDzAxg/pNAWpJ2fMOjQFhnvWtFRA6KAYsTm/73Yfyx84wq+3u548shbRDg7aF3tcgs/v0UOPmvVq5QG+g+Se8aEZEDY8DipFbuO4u3/9qvyi4uwAf3t0DdYD+9q0Vmce4AsHJK1oEL0O9TwIvXDxGVHgYsTujwuUQ8++N2tc6XGHVrA3QNr6x3tcgsMtKB+U8A6cnacbsngFod9K4VETk4BixOJj45DcO+24yElHR1fHvTUDzVpZ7e1SIz2fARELVZK1esB3R9Se8aEZETYMDiRDIzLRj583YcOXdZHTes7I+3BzaHi4wJERXG2T3Aqte1sourNhTk6cO2I6JSx4DFiby//ACW741R5cByHmolW18vd72rRWaRkQbMfxzI0PaZQocRQI0b9a4VETkJBixOYsmuaHy48pAqy6zljwe1RK2KvnpXi8xk7XtAdKRWDg4HOv+f3jUiIifCgMUJ7D+TgJFzsr5oAIy/vRE61g/WtU5kMtE7gH+mamUXN20oyMNb71oRkRNhwOLgLiWlqiTbpNQMddyvRVU82rGO3tUiM0lP1WYFZWqJ2ug4EqjWSu9aEZGTYcDiwNIzMjHix204cUFbibRptQC82T+CSbZUNKvfAs7u0sqVmwKdXmALElGZY8DiwKYt3Y81B8+rckVfT3z+YBt4e7jpXS0yk6gtwJp3tbKruzYU5O6pd62IyAkxYHFQC7ZH4fPVR1TZ3dUF0x9ohWrly+ldLTKTtGRtryCLNpyoelaqROhdKyJyUgxYHNCuqDi8MHeH7Xhi78ZoG1ZR1zqRCf39OnBun1au0kLLXSEi0gkDFgdzPjEFj32/BSnpmer43jY1MLhdLb2rRWZzchOw/iOt7OapDQW5cWNMItIPAxYHkp5hwYgftyPq0hV13LJmeUzu14RJtlQ0qUnarCCLFvSi83igcmO2IhHpigGLA/lgzUlsPHpBlUP8vfDZ4NbwcmeSLRXRyleBWG2RQVRrA3R4hk1IRLpjwOIgftlyCr9sP6fKnm6u+OzB1qgcwIW9qIiOrwf+na6V3b2zhoK4fQMR6Y8BiwPYduIiXpqftU4GgFf7NUWrmhV0rROZUEqiNhQEi3YsuzAHN9C7VkRECv90MqGMTAs2Hb2AmIRk1ZsyceEupGZoXzIPtquJe26ooXcVySwyM7RelcSzwK55wMVj2vma7YF2ErwQERmDwwQsf//9N7p06ZLrfZs2bcINN9yQ632dO3fGP//8k+PcY489hs8++wxG3cRw0qI9iI5Lvua+ltX8MKFXI13qRSa0ZyGwZCwQfzrneZkV1PcTwJX5T0RkHA4TsHTo0AHR0dE5zr300ktYsWIF2rRpk+9zhw0bhsmTJ9uOfXx8YNRg5YlZW60d9te4o3FFeLhxlI8KGazMGWIf/skuIxU4uxuoWJdNSUSG4TABi6enJ0JDQ23HaWlpWLBgAUaMGFHgtF4JULI/16jDQNKzklewIr7acBpDb2kEV8YsVNAwkPSs5Hk1uQBLxgHhvdjLQkSG4TABy9UWLlyI2NhY/O9//yvwsbNnz8asWbNU0NK7d2/VM5NfL0tKSoq6WcXHx6v/Z2Zmqltp2HgkNtdhoOzOJqapx3WoVwnOTD4Di8VSap+F6dvh2Dq4Xj0MlIMFiI9C5rF1QO2bYXa8HtgOvB6M/e+isHVw2IBlxowZ6NGjB6pXr57v4wYNGoRatWqhatWq2LFjB8aOHYv9+/dj3rx5eT7njTfewKRJk645f+7cOSQn5x9UFNehUxcK97ioGNQL0P8C1Pvij4uLU/8YXZ24uymvdvCOOoDyhXh+fNQBJPuYf5YQrwe2A68HY/+7SEhIcIyAZdy4cZg6dWq+j9m7dy/Cw8Ntx6dOncLSpUsxZ86cAl9/+PDhtnKzZs1QpUoVdOvWDYcPH0bdurmP4Y8fPx4jR47M0cNSo0YNBAcHIyAgAKWhXqIkQB4t+HHVQhASwh4WGQaUz0Pvf4iGbIekwgUhAdUaICAkBA7bDk6G7cB2MOr14O3t7RgBy6hRozB06NB8HxMWFpbjeObMmahYsSL69OlT5Pdr27at+v+hQ4fyDFi8vLzU7WryoZfWB982rBKqBHrjTFxyrpkHkqUT4uehNjnU++IzAvmHWJqfh6nbofZNQEDVa2cH2Z+l7neVxzlI+/F6YDvwejDuv4vCvr/hAxaJ/uRWWNK9JQHLkCFD4OFR9M3atm/frv4vPS1G4ubqonZdlllCV7OmFD/XuYZ6HFG+ZLpyz6nAnAdzuTPr+un5JhNuichQHOPPp2xWrlyJo0eP4tFHH73mvqioKDV0JOuyCBn2mTJlCrZs2YJjx46pRF0JdDp16oSIiAgYTc+mVfDp4FYI8M4ZiIUGeuOTQS3RpR5Xt6VCCusMuHpee156Xu75Dmhc9N5JIqLSZPgeluIk28qaLNlzWrJPdZaE2qSkJNtU6OXLl+P999/H5cuXVR5K//79MWHCBBiVBC0Hzibg3WUH1fGoWxvgyS714AILYmJi9K4emcWeBUBmqlZucAfQrD/gVxmo1YE9K0RkSA4XsPzwww953le7dm01ZGQlAcrVq9yawcWkNFu5Q72KahgoMzO/FVqIrhL5o73caRRQPf/FFYmI9OZwQ0LO4MLlrL+MAQT5Xpv8S5Qv2S/o+DqtXKkBUK01G4yIDI8Bi+kDllzyEIjyE/mTvdz8fpkqwPYiIsNjwGJCsYlawOLh5oIAb4cb1aPSJCtKbrcOm7oAEfeyvYnIFBiwmLiHpYKPZ4H7JBHlcGIDcOm4faZQYDU2EBGZAgMWk5GkYWvAwuEgKrLIbEnpLQaxAYnINBiwmExiSjpSM7S9gir6MX+FiiA1Cdi9QCt7+gPhd7L5iMg0GLCYDGcIUbHtWwykZm0y1qQf4Jn3juREREbDgMVkYrPNEKrIGUJUFLZkWw4HEZH5MGAxmQtZM4QEc1io0OKigCN/a+UKtYGa7dl4RGQqDFhMhmuwULHskLVXslZD5torRGRCDFhMhkNCVGSyHcX2bEvxN7+PjUhEpsOAxWQuXE6xlSswh4UKI2oLEKttlolaN2lDQkREJsOAxWTYw0JF5bIje+/K/WxAIjIlBiwmwxwWKpKMVGDXPK3s4aNNZyYiMiEGLCYNWGRF/vI+XDiO8ud1bCVcki9pB416A17+bDIiMiUGLCbd+FD2EXJz5T5ClL9y+3+zH3A4iIhMjAGLyXAfISq0xBh4nVyjlQOqAXU6sfGIyLQYsJjIldQMXEnLUGUuGkcF2vkLXCza9YKIewFXNzYaEZkWAxYTic02pZnL8lORZgdxZ2YiMjkGLCbCGUJUaNE74HJ2typaqt0AVKrPxiMiU2PAYiJcg4UKLdLeu2LhyrZE5AAYsJgINz6kQslIA3bMUUWLmyfQ5G42HBGZHgMWsw4J+XnpWhcysIPLgKTzqphcqytQrrzeNSIium4MWEyEQ0JUKJE/2IrJDe9ioxGRQ2DAYiIXs/ewcONDyk3SBWD/ElW0+IYgpcbNbCcicggMWEyEPSxUoF2/AplpWrnZPYCrOxuNiBwCAxYTuZBtHZYK7GGh3Gy3DwdxdhARORIGLCZMug3wdoeHGz86ukrMPuD0Vq0cGgFUbsImIiKHwW89Ew4JVeQMISog2ZYr2xKRo2HAYhKp6ZlISE5XZSbc0jUyM2xrr6i8lWYD2UhE5FAYsJjExSTOEKJ8HFkFJERr5fq3Ab6V2FxE5FAYsJhEbKI9YOHGh3SN7dzokIgcGwMWk+DGh5Sn5Dhg32KtXC4IqN+DjUVEDocBi0nEZpvSzBwWymH3b0B6slZuNgBw92QDEZHDYcBiwh6Win78QqI8hoOa38+mISKHxIDFlENC3PiQssQeBk7+q5WDw4GqLdk0ROSQGLCYBJflp1xF/pSzd8XFhQ1FRA6JAYtJXMg2S4g5LKRkZtoDFhdXIOJeNgwROSwGLCbBWUJ0jePrgLgTWrluVyCgChuJiBwWAxaTzRLy9XSDt4eb3tUhg210yGRbInJ0DFhM1sMSxBlCJFISgT0LtLbwCgTCe7FdiMihOVTAUrt2bbi4uOS4vfnmm/k+Jzk5GU899RQqVqwIPz8/9O/fH2fPnoWRZGRacOlKmipzhhApexcBaZe1cpN+gEc5NgwROTSHCljE5MmTER0dbbuNGDEi38c///zzWLRoEX755Rf8888/OH36NO6++24YbR8hi0Urc1l+UrgzMxE5GXc4GH9/f4SGhhbqsXFxcZgxYwZ++OEHdO3aVZ2bOXMmGjVqhH///Rft2rWDETDhlnK4dBI4ukYrB4UBNdqygYjI4TlcwCJDQFOmTEHNmjUxaNAg1YPi7p77j7llyxakpaWhe/futnPh4eHquRs2bMgzYElJSVE3q/j4ePX/zMxMdStp5xOyll2X7ycfj1zfQ85ZLJZSeX+zcfi2iPwRrtC63DIj7ofqfrN2wTlTOxQS24HtwOvB2P8uClsHhwpYnnnmGbRq1QpBQUFYv349xo8fr4aF3n333Vwff+bMGXh6eqJ8+fI5zleuXFndl5c33ngDkyZNuub8uXPnVE5MSTt6+qKt7GFJRUxMTK4fuPQYyQXo6upwI31F4tBtYbGg0tbZtrHc89W6ITOX68Hh26EI2A5sB14Pxv53kZCQ4BgBy7hx4zB16tR8H7N3717VMzJy5EjbuYiICBWMPPbYYyrA8PIqueXsJRDK/l7Sw1KjRg0EBwcjICAAJS39yBVbuWblIISEhOR68UmSsdRB74tPbw7dFic3wjXumCpaandEpbotnbMdioDtwHbg9WDsfxfe3t6OEbCMGjUKQ4cOzfcxYWFhuZ5v27Yt0tPTcezYMTRs2PCa+yXXJTU1FZcuXcrRyyKzhPLLg5HgJ7cASD700vjgLyZpM4REJT+vPN9DLr7SqoPZOGxb7LAvxe/SYhBcCvj5HLYdiojtwHbg9WDcfxeFfX/DBywS/cmtOLZv364aIrceCdG6dWt4eHhgxYoVajqz2L9/P06cOIH27dvDKJh0S0raFWDXb1rZwxdo1IcNQ0ROw/ABS2FJkuzGjRvRpUsXNVNIjiXhdvDgwahQoYJ6TFRUFLp164bvvvsON954IwIDA/HII4+o4R3Je5HhHJkGLcGKUWYIXbvxIXdqdlr7/wBS4rRy4z6Al5/eNSIiKjMOE7DIEM1PP/2EV155Rc3gqVOnjgpYsueayIwg6UFJSkqynXvvvfdUL4z0sMjzevTogenTp8NILmYLWLjSrRPb/qO93GKQnjUhIipzDhOwyOwgWTuloJVwJSP66mSfTz75RN2Myjok5OnuqvYSIieUcAY4vEIrB9YEat2sd42IiMqUc2fimYR1SEhWuZUkKXJCO34GLFlrFTS/V7LU9K4REVGZ4m89g5MeIeuQUJCvp97VIT1Ir2D24aDm9/NzICKnw4DF4OKvpCM9UxvGYsDipKK3A+f2amVZhr9iXb1rRERU5hiwGFzsZfsWANz40Emxd4WIiAGL0eVcg4VTmp1Oeiqw8xet7OYFNLlL7xoREemCPSwGl2MNFj/msDidg38BVy5o5UZ3AuVy7ntFROQsGLAYHFe5dXLbf7CXm3PtFSJyXgxYDI4BixO7fB44uFQr+4UCdbvoXSMiIt0wYDG42MTsy/JzSMip7JwLZKZr5Yh7AFcuGkhEzosBi8FdyDZLiNOanUxktuEgLsVPRE6OAYvBceNDJ3V2DxAdqZWrtABCGuldIyIiXTFgMUkOi7urCwLKOczWT1Sk3pUH2F5E5PQYsJgkYKnAfYScR0Y6EPmzVnb1AJoN0LtGRES6Y8Bi8H2Esm98SE7i8ErgcoxWbtAD8AnSu0ZERLpjwGJgl1MzkJqu7dDLhFsnwmRbIqJrMGAxsAvZpjQzYHESVy4C+/7Qyj4VgXq36l0jIiJDYMBiYNz40Ant/g3IyJrK3mwg4M6hQCIiwYDFwLjxoZPvzMy1V4iIbBiwmGQNliBufOj4zh8ETm3SyiFNgNAIvWtERGQYDFhM0sPCWUJOIDJ778r9gIuLnrUhIjIUBiwGxo0PnUhmpn3tFRc3oNk9eteIiMhQGLAYGDc+dCLHVgPxp7RyvW6Af2W9a0REZCgMWAyMGx86abJt8/v1rAkRkSExYDHBkJCkMpT34fRWh5WSAOxdqJW9A4GGd+hdIyIiw2HAYmAXkrL2EfLxhJsrEzAd1p4FQFqSVm7aH/Dw1rtGRESGw4DFBCvdcpVbZxoOGqRnTYiIDIsBi0Elp2WovYQEAxYHdvEYcHytVq5YD6jeRu8aEREZEgMWM0xpZv6K47JOZbYm23LtFSKiXDFgMUPAwlVuHZPFkm2xOBeg+X06V4iIyLgYsJhgWX6ucuugTvwLXDyqlet0AgKr610jIiLDYsBiUFyDxQlsn20vt3hAz5oQERkeAxYTrHLLpFsHlJoE7J6vlT39gEZ36l0jIiJDY8Biio0PvXStC5WCfb8DqQlauXE/wNOXzUxElA8GLAbFjQ8dXOQPOXdmJiKifDFgMUPSLWcJOZb408CRv7Vy+ZpAzQ5614iIyPAYsJigh0WW5icHsuNnwJJpX3vFlf8MiYgKwt+UBg9Y/L3d4enOj8mh1l7Znm04iGuvEBEVCr8JDSo2MUX9n2uwOJiorcD5A1pZhoKCwvSuERGRKTBgMaC0jEzEJ6erMqc0Oxgm2xIRFQsDFgO6mH1Zfk5pdhzpKcDOuVrZvZw2nZmIiAqFAYsBcVl+B3VgCZB8SSvLQnHeAXrXiIjINBiwGND5BC1/RVxJy0BGpkXX+tB1yswAjq4BVr9jPyezg4iIyPkClmPHjuGRRx5BnTp1UK5cOdStWxcTJ05Eaqp9eCU3nTt3houLS47b448/Dr0s2RWNET9usx0vjDyNm6euVOfJhPYsBN5vCnx7J3AmUjvn4gqkZK1yS0REheIOB7Fv3z5kZmbi888/R7169bBr1y4MGzYMly9fxttvv53vc+VxkydPth37+PhADxKUPDFrK67uTzkTl6zOfzq4FXo2raJL3aiYwcqcITKXOed5WYPll6GAy3dA4z5sWiIiZwpYevbsqW5WYWFh2L9/Pz799NMCAxYJUEJDQ8uglnmTYZ9Ji/ZcE6wIOecCqPtvbRwKN1c5IsMPAy0Ze22wkt2ScUB4L8DVrSxrRkRkSg4TsOQmLi4OQUFBBT5u9uzZmDVrlgpaevfujZdeeinfXpaUlBR1s4qPj1f/lx4euRXHxiOxiI5LzvN++dqT+zceOY92YRWvuV/e12KxFPv9HYkh2uLYOrjKEvx5sgDxUcg8tg6ofbPjtoMBsB3YDrwejP3vorB1cNiA5dChQ/joo48K7F0ZNGgQatWqhapVq2LHjh0YO3as6pmZN29ens954403MGnSpGvOnzt3DsnJycWr76kLhXzcOYT5ZeT6gUuAJhegq5Mv9W6EtvCOOoDyhXhcfNQBJPs0cNh2MAK2A9uB14Ox/10kJCQ4RsAybtw4TJ06Nd/H7N27F+Hh4bbjqKgoNTw0cOBAlZ+Sn+HDh9vKzZo1Q5UqVdCtWzccPnxYJe7mZvz48Rg5cmSOHpYaNWogODgYAQHFm6paL1GGBY4W/LjqwQgJyb2HRRKGpQ56X3x6M0RbJBUuCAmo1gABISGO2w4GwHZgO/B6MPa/C29vb8cIWEaNGoWhQ4fm+xjJV7E6ffo0unTpgg4dOuCLL74o8vu1bdvW1kOTV8Di5eWlbleTD724H3zbsEqoEuitEmxzy3qQrJXQQG/1ONc8cljk4rueOjgS3dui9k1AQFUgXmZ35fGJBlSFqzyuFOuoezsYBNuB7cDrwbj/Lgr7/oYPWCT6k1thSM+KBCutW7fGzJkzi/UhbN++Xf1felrKkiTSTuzdWM0GcrnqK84ansj9TLg1CUmk7Tk1a5ZQHp9ozzeZcEtEVEgO82eXBCuypkrNmjVV3orkk5w5c0bdsj9Gho42bdqkjmXYZ8qUKdiyZYtax2XhwoUYMmQIOnXqhIiIiDL/GWTKskxdlp6U7OSYU5pNSKYs3/MdEHBV8Cs9L3KeU5qJiArN8D0shbVs2TI1jCO36tWr57hPkopEWlqaSqhNSkpSx56enli+fDnef/99tV6L5KH0798fEyZMgF4kaJGpy5uOXkBMQjJC/L1xY50g9qyYlQQlMnX5+Hog8SzgVxmo1YE9K0REzhqwSJ5LQbkutWvXtgUvQgKUf/75B0Yjwz7t616bWEsmHh6q01HvWhARmZrDDAkRERGR42LAQkRERIbHgIWIiIgMjwELERERGR4DFiIiIjI8BixERERkeAxYiIiIyPAYsBAREZHhMWAhIiIiw2PAQkRERIbHgIWIiIgMjwELERERGR4DFiIiIjI8h9mtWU/WHaDj4+N1q0NmZiYSEhLg7e0NV1fnjkPZFmwHXg/8d8HfD+b5PWn97rR+l+aFAUsJkA9d1KhRoyRejoiIyCm/SwMDA/O838VSUEhDhYpUT58+DX9/f7i4uOgWoUrAdPLkSQQEBMCZsS3YDrwe+O+Cvx/M83tSwhAJVqpWrZpvbw97WEqANHD16tVhBHLh6X3xGQXbgu3A64H/Lvj7wRy/J/PrWbFy7mQHIiIiMgUGLERERGR4DFgchJeXFyZOnKj+7+zYFmwHXg/8d8HfD473e5JJt0RERGR47GEhIiIiw2PAQkRERIbHgIWIiIgMjwELERERGR4DFpOIiorC4MGDUbFiRZQrVw7NmjXD5s2b833O33//jVatWqks8Hr16uGbb76Bs7VDdHQ0Bg0ahAYNGqgF/p577jk4iqK2xbx583DrrbciODhYLRTVvn17LF26FM7WDmvXrsVNN91ke3x4eDjee+89OOPvCKt169bB3d0dLVq0gLO1g/yelBXKr76dOXMGznY9pKSk4MUXX0StWrXU90bt2rXx9ddfwyi40q0JXLx4Uf2C7dKlC/7880/1hXPw4EFUqFAhz+ccPXoUvXr1wuOPP47Zs2djxYoVePTRR1GlShX06NEDztIO8g9QHjdhwgSH+FK6nrZYvXq1Clhef/11lC9fHjNnzkTv3r2xceNGtGzZEs7SDr6+vnj66acRERGhyhLAPPbYY6o8fPhwOEs7WF26dAlDhgxBt27dcPbsWZjZ9bTD/v37c6z4GhISAmdrh3vuuUddAzNmzFB/5MoffLL1jGHIXkJkbGPHjrXcfPPNRXrOCy+8YGnSpEmOc/fee6+lR48eFmdqh+xuueUWy7PPPmtxBNfbFlaNGze2TJo0yeLs7XDXXXdZBg8ebHHGdpDfCxMmTLBMnDjR0rx5c4uZFacdVq1aJfvpWS5evGhxFGOL0Q5//vmnJTAw0BIbG2sxKg4JmcDChQvRpk0bDBw4UEX98tfwl19+me9zNmzYgO7du+c4Jz0rct6Z2sFRlURbWLeXDwoKgjO3w7Zt27B+/XrccsstcLZ2kF62I0eOqAXEHMH1XA8yHCY90NILKUNkztYOC7Oe89Zbb6FatWpqGH306NG4cuUKDEPviIkK5uXlpW7jx4+3bN261fL5559bvL29Ld98802ez6lfv77l9ddfz3Hu999/V39JJCUlOU07OGoPy/W2hZg6daqlQoUKlrNnz1qcsR2qVatm8fT0tLi6ulomT55sMbPitMOBAwcsISEhlv3796tjR+hhKU477Nu3z/LZZ59ZNm/ebFm3bp3lf//7n8Xd3d2yZcsWizO1Q48ePdRzevXqZdm4caP6vqhVq5Zl6NChFqNgwGICHh4elvbt2+c4N2LECEu7du2cKmApTjs4asByvW0xe/Zsi4+Pj2XZsmUWZ22HI0eOWHbs2GH54osvLEFBQZYffvjB4iztkJ6ebmnTpo3l008/tZ1zhIDlev9dWHXq1MnUQ4QexWiHW2+9VQU1ly5dsp379ddfLS4uLob5zuCQkAlIN2Xjxo1znGvUqBFOnDiR53NCQ0OvSaCTY0kqk4xxZ2kHR3U9bfHTTz+pBOw5c+ZcM2zoTO1Qp04dNXNi2LBheP755/HKK6/AWdpBhgJlxogkH8vsILlNnjwZkZGRqrxy5Uo48++IG2+8EYcOHYJZVSlGO8hzZCgoMDAwx3OkY+PUqVMwAs4SMgHJ9pYM9uwOHDigpp7lRaas/vHHHznOLVu2TJ13pnZwVMVtix9//BEPP/ywClpkFpnZldQ1Ifk8MqPMWdpB/nDZuXNnjnPTp09XgcrcuXNVMOfM18P27dvVF7hZ3VSMdpDn/PLLL0hMTISfn5/tObIcRPXq1WEIenfxUME2bdqkxlRfe+01y8GDB23d+bNmzbI9Zty4cZYHH3wwR3e3PGbMmDGWvXv3Wj755BOLm5ubZcmSJU7VDmLbtm3q1rp1a8ugQYNUeffu3RYzK05byGPkOXItREdH227Zu4CdoR0+/vhjy8KFC1UOh9y++uori7+/v+XFF1+0ONu/jewcYUioOO3w3nvvWebPn68ev3PnTjVsLHlNy5cvtzhTOyQkJFiqV69uGTBggPr9+M8//6jUgkcffdRiFAxYTGLRokWWpk2bqqSo8PBwNe6e3UMPPaRyNK6erteiRQuVWBgWFmaZOXOmxRnbQeLyq2+STOZsbSHl3NpCHudM7fDhhx+qKf/yCzwgIMDSsmVLy/Tp0y0ZGRkWZ/u34WgBS3HaQZLP69atq/I3JJepc+fOlpUrV1rMblExrgf547Z79+6WcuXKqeBl5MiRhslfES7yH717eYiIiIjyw6RbIiIiMjwGLERERGR4DFiIiIjI8BiwEBERkeExYCEiIiLDY8BCREREhseAhYiIiAyPAQsREREZHgMWInJYnTt3xnPPPad3NYioBDBgISJD6t27N3r27JnrfWvWrIGLiwt27NhR5vUiIn0wYCEiQ3rkkUfUDuO5bW0/c+ZMtGnTBhEREbrUjYjKHgMWIjKkO++8E8HBwfjmm29ynE9MTMQvv/yCfv364f7770e1atXg4+ODZs2a4ccff8z3NaVXZv78+TnOlS9fPsd7nDx5Evfcc486HxQUhL59++LYsWMl/NMRUVExYCEiQ3J3d8eQIUNUMJF9j1YJVjIyMjB48GC0bt0av//+O3bt2oXhw4fjwQcfxKZNm4r9nmlpaejRowf8/f3VsNO6devg5+enhqZSU1NL6CcjouJgwEJEhvXwww/j8OHD+Oeff3IMB/Xv3x+1atXC6NGj0aJFC4SFhWHEiBEqsJgzZ06x3+/nn39GZmYmvvrqK9Vj06hRI/V+J06cwN9//11CPxURFQcDFiIyrPDwcHTo0AFff/21Oj506JDq+ZD8FullmTJligosZOhGekKWLl2qgoviioyMVO8hPSzyenKT105OTlaBExHpx13H9yYiKpAEJ9J78sknn6jejrp16+KWW27B1KlT8cEHH+D9999XQYuvr6+awpzf0I3ksGQfXrIOA2XPj5FhptmzZ1/zXMmnISL9MGAhIkOTBNhnn30WP/zwA7777js88cQTKvCQ/BJJiJVcFiFDOQcOHEDjxo3zfC0JOqKjo23HBw8eRFJSku24VatWalgoJCQEAQEBpfyTEVFRcEiIiAxNhmXuvfdejB8/XgUbQ4cOVefr16+vpj2vX78ee/fuxWOPPYazZ8/m+1pdu3bFxx9/jG3btmHz5s14/PHH4eHhYbv/gQceQKVKlVQgJENPR48eVbkrzzzzTK7Tq4mo7DBgISJTDAtdvHhRzeCpWrWqOjdhwgTVIyLnZEXb0NBQNdU5P++88w5q1KiBjh07YtCgQSppV6ZEW0l59erVqFmzJu6++26VdCvvLTks7HEh0peL5eoBXSIiIiKDYQ8LERERGR4DFiIiIjI8BixERERkeAxYiIiIyPAYsBAREZHhMWAhIiIiw2PAQkRERIbHgIWIiIgMjwELERERGR4DFiIiIjI8BixEREQEo/t/jgDvSqrQQpEAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#input\n",
+    "depth_bins = sorted([0, -5, -10, -15, -20, -25])\n",
+    "depth_labels = ['0-5m', '5-10m', '10-15m', '15-20m', '>20m']\n",
+    "df = cmp.data.to_dataframe()\n",
+    "df['depth_bin'] = pd.cut(df['z'], bins=depth_bins, labels=depth_labels)\n",
+    "\n",
+    "df_av = (\n",
+    "    df.groupby(\"depth_bin\", observed=True)\n",
+    "        .agg(\n",
+    "          model_mean=(cmp.mod_names[0], \"mean\"),\n",
+    "          obs_mean=(\"Observation\", \"mean\"),\n",
+    "      )\n",
+    "      .round(3)\n",
+    ")\n",
+    "\n",
+    "# Plot vertical profile\n",
+    "fig, ax = plt.subplots(1, 1, figsize=(6, 6), sharey=True)\n",
+    "\n",
+    "# Get bin centers for plotting\n",
+    "bin_centers = [(depth_bins[i] + depth_bins[i+1])/2 for i in range(len(depth_labels))]\n",
+    "\n",
+    "ax.plot(df_av['model_mean'], bin_centers[:len(df_av)], 'o-', linewidth=2, label='Model Mean')\n",
+    "ax.plot(df_av['obs_mean'], bin_centers[:len(df_av)], 'o-', linewidth=2, label='Observation Mean')\n",
+    "ax.set_xlabel('Value')\n",
+    "ax.set_ylabel('Depth (m)')\n",
+    "ax.invert_yaxis()\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "ax.set_title('Average Profile')\n",
+    "ax.legend(fontsize=8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "611e8db6",
+   "metadata": {},
+   "source": [
+    "**Profile statistics plots**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cf90dce1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "depth_bin",
+         "rawType": "category",
+         "type": "unknown"
+        },
+        {
+         "name": "bias",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "std",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "rmse",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "mae",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "n",
+         "rawType": "int64",
+         "type": "integer"
+        }
+       ],
+       "ref": "4049c2cd-9492-46c8-98a9-763a69564821",
+       "rows": [
+        [
+         "0-5m",
+         "-0.202",
+         "0.071",
+         "0.214",
+         "0.202",
+         "27"
+        ],
+        [
+         "5-10m",
+         "-0.181",
+         "0.068",
+         "0.193",
+         "0.181",
+         "18"
+        ],
+        [
+         "10-15m",
+         "-0.201",
+         "0.075",
+         "0.214",
+         "0.201",
+         "27"
+        ],
+        [
+         "15-20m",
+         "-0.222",
+         "0.068",
+         "0.232",
+         "0.222",
+         "18"
+        ],
+        [
+         ">20m",
+         "-0.175",
+         "0.066",
+         "0.187",
+         "0.175",
+         "18"
+        ]
+       ],
+       "shape": {
+        "columns": 5,
+        "rows": 5
+       }
+      },
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>bias</th>\n",
+       "      <th>std</th>\n",
+       "      <th>rmse</th>\n",
+       "      <th>mae</th>\n",
+       "      <th>n</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>depth_bin</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0-5m</th>\n",
+       "      <td>-0.202</td>\n",
+       "      <td>0.071</td>\n",
+       "      <td>0.214</td>\n",
+       "      <td>0.202</td>\n",
+       "      <td>27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5-10m</th>\n",
+       "      <td>-0.181</td>\n",
+       "      <td>0.068</td>\n",
+       "      <td>0.193</td>\n",
+       "      <td>0.181</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10-15m</th>\n",
+       "      <td>-0.201</td>\n",
+       "      <td>0.075</td>\n",
+       "      <td>0.214</td>\n",
+       "      <td>0.201</td>\n",
+       "      <td>27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15-20m</th>\n",
+       "      <td>-0.222</td>\n",
+       "      <td>0.068</td>\n",
+       "      <td>0.232</td>\n",
+       "      <td>0.222</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>&gt;20m</th>\n",
+       "      <td>-0.175</td>\n",
+       "      <td>0.066</td>\n",
+       "      <td>0.187</td>\n",
+       "      <td>0.175</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            bias    std   rmse    mae   n\n",
+       "depth_bin                                \n",
+       "0-5m      -0.202  0.071  0.214  0.202  27\n",
+       "5-10m     -0.181  0.068  0.193  0.181  18\n",
+       "10-15m    -0.201  0.075  0.214  0.201  27\n",
+       "15-20m    -0.222  0.068  0.232  0.222  18\n",
+       ">20m      -0.175  0.066  0.187  0.175  18"
+      ]
+     },
+     "execution_count": 113,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#input\n",
+    "depth_bins = sorted([0, -5, -10, -15, -20, -25])\n",
+    "depth_labels = ['0-5m', '5-10m', '10-15m', '15-20m', '>20m']\n",
+    "\n",
+    "df = cmp.data.to_dataframe()\n",
+    "df['depth_bin'] = pd.cut(df['z'], bins=depth_bins, labels=depth_labels)\n",
+    "\n",
+    "df[\"error\"] = df[cmp.mod_names[0]] - df[\"Observation\"]\n",
+    "df[\"abs_error\"] = df[\"error\"].abs()\n",
+    "\n",
+    "# Calculate statistics by depth bin\n",
+    "profile_stats = (\n",
+    "    df.groupby(\"depth_bin\", observed=True)\n",
+    "      .agg(\n",
+    "          bias=(\"error\", \"mean\"),\n",
+    "          std=(\"error\", \"std\"),\n",
+    "          rmse=(\"error\", lambda x: np.sqrt((x**2).mean())),\n",
+    "          mae=(\"abs_error\", \"mean\"),\n",
+    "          n=(\"z\", \"count\"),\n",
+    "      )\n",
+    "      .round(3)\n",
+    ")\n",
+    "profile_stats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c17a7ea1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\lajo\\AppData\\Local\\Temp\\ipykernel_29000\\352810279.py:14: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
+      "  axes[0].legend(fontsize=8)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot vertical profile\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(16, 6), sharey=True)\n",
+    "\n",
+    "# Get bin centers for plotting\n",
+    "bin_centers = [(depth_bins[i] + depth_bins[i+1])/2 for i in range(len(depth_labels))]\n",
+    "\n",
+    "axes[0].plot(profile_stats['bias'], bin_centers[:len(profile_stats)], 'o-', linewidth=2)\n",
+    "axes[0].axvline(0, color='gray', linestyle='--', alpha=0.5)\n",
+    "axes[0].set_xlabel('Bias (psu)')\n",
+    "axes[0].set_ylabel('Depth (m)')\n",
+    "axes[0].invert_yaxis()\n",
+    "axes[0].grid(True, alpha=0.3)\n",
+    "axes[0].set_title('Bias Profile')\n",
+    "axes[0].legend(fontsize=8)\n",
+    "\n",
+    "axes[1].plot(profile_stats['rmse'], bin_centers[:len(profile_stats)], 'o-', linewidth=2, color='orange')\n",
+    "axes[1].set_xlabel('RMSE (psu)')\n",
+    "axes[1].grid(True, alpha=0.3)\n",
+    "axes[1].set_title('RMSE Profile')\n",
+    "\n",
+    "# Boxplot of errors by depth bin\n",
+    "error_by_depth = [df[df['depth_bin'] == label]['error'].values \n",
+    "                  for label in depth_labels if label in df['depth_bin'].values]\n",
+    "positions = [bin_centers[i] for i in range(len(error_by_depth))]\n",
+    "\n",
+    "bp = axes[2].boxplot(error_by_depth, positions=positions, vert=False, widths=2.5,\n",
+    "                      patch_artist=True, showfliers=True)\n",
+    "for patch in bp['boxes']:\n",
+    "    patch.set_facecolor('lightblue')\n",
+    "    patch.set_alpha(0.6)\n",
+    "\n",
+    "axes[2].axvline(0, color='gray', linestyle='--', alpha=0.5)\n",
+    "axes[2].set_xlabel('Error (psu)')\n",
+    "axes[2].grid(True, alpha=0.3, axis='x')\n",
+    "axes[2].set_title('Error Distribution')\n",
+    "\n",
+    "# Add N observations as text on the right side\n",
+    "for i, (label, pos) in enumerate(zip(profile_stats.index, positions)):\n",
+    "    n_obs = profile_stats.loc[label, 'n']\n",
+    "    axes[2].text(axes[2].get_xlim()[1] * 0.95, pos, f'n={n_obs}', \n",
+    "                 va='center', ha='right', fontsize=9, bbox=dict(boxstyle='round,pad=0.3', \n",
+    "                 facecolor='white', alpha=0.7))\n",
+    "\n",
+    "plt.suptitle('Vertical Error Profile (Both Stations Combined)')\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f83c3d95",
+   "metadata": {},
+   "source": [
+    "### Considerations and ideas for working with 3D model and vertical observations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70e8aed9",
+   "metadata": {},
+   "source": [
+    "#### Summary of qustionnaire ~40 answers\n",
+    "\n",
+    "- People use sigma-layers (some use Mike Wave but I guess it also work with sigma)\n",
+    "- Most use fixed profile location in time\n",
+    "- Some use Matlab and excel but most use python and Mike Zero Probably P or M in combination with Mike Zero\n",
+    "- Most people interpolate model depth to exact obs pos. However, ~1/3 binned the obs to model cells (with or without aggregate)\n",
+    "- Clear, ready to use visualizations are highly valued: observed vs modelled vertical profiles\n",
+    "- Vertical profile analysis is a key gap: e.g. depth range stats, and easier profile to profile comparisons.  \n",
+    "- Advanced diagnostic plots are requested: e.g. Hovmöller diagrams\n",
+    "- Users have example plots and scripts\n",
+    "\n",
+    "**What does this mean for Modelskill dev**:\n",
+    "\n",
+    "1. Focus on sigma-z layers\n",
+    "2. Start with x,y fixed profiles\n",
+    "3. Options to interpolate or bin observations\n",
+    "4. Need dedicated vertical plots\n",
+    "5. Vertical profiles stats, e.g. vertical_grid_stats\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d63e8d63",
+   "metadata": {},
+   "source": [
+    "#### Features needed for minimal version\n",
+    "\n",
+    "- Support vertical observations with fixed position and depth coordinates. Time coordinate should support non-equidistant timsteps.\n",
+    "- Match 3D sigma model to these observations with binning and interpolation. Get a comparerer.\n",
+    "- Calculate statistics on vertical grids (e.g. rmse with depth)\n",
+    "- Slice Comparer in vertical (results similar as comaprer with pointObs)\n",
+    "- Aggregate Comparerer in vertical (results similar as comaprer with pointObs)\n",
+    "- Plots"
+   ]
+  },
+  {
+   "attachments": {
+    "image-2.png": {
+     "image/png": 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"
+    },
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"
+    },
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"
+    },
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "373ffb85",
+   "metadata": {},
+   "source": [
+    "#### Usefull plots\n",
+    "\n",
+    "**Timeseries of all depth**\n",
+    "\n",
+    "As below but raw data not only mod-obs pairs. As timeseries plots today but all depth\n",
+    "![image.png](attachment:image.png)\n",
+    "\n",
+    "\n",
+    "**Profiles of all times**\n",
+    "\n",
+    "So, this is a selected time, could include several profiles  \n",
+    "![image-2.png](attachment:image-2.png)\n",
+    "\n",
+    "**Havmöller plots**\n",
+    "\n",
+    "![image-3.png](attachment:image-3.png)\n",
+    "\n",
+    "**Vertical statistics**\n",
+    "![image-4.png](attachment:image-4.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8214a066",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6eabb43",
+   "metadata": {},
+   "source": [
+    "#### Implementation ideas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dc4649a0",
+   "metadata": {},
+   "source": [
+    "**Proposal**\n",
+    "\n",
+    "1. VerticalObservation class  \n",
+    "    - Stores z coordinate as dimension, x,y as attributes and time\n",
+    "    - Distinguishable from track (x,y coordinates) or point (single x,y,z coordinate)\n",
+    "    - Format for dataframe and dfs\n",
+    "    - Create vertical grid out of all available z\n",
+    "2. Support sigma-levels\n",
+    "    - Need to consider time when matching mod-obs in depth\n",
+    "    - Need layer boundaries in mikeio calc_zf()\n",
+    "\n",
+    "3. Matching for verticalObservation needs implementation\n",
+    "    - interpolate mod to obs \n",
+    "    - or bin obs in model cells\n",
+    "4. from_matched()\n",
+    "    - Consider dataframe structure for creating vertical obs. Is it enough with a column z? Create vertical grid containing all z.\n",
+    "\n",
+    "5. Same comparer as today but add support for VerticalObservation\n",
+    "    - plots same as today for all depth (ignoring z coordinate)\n",
+    "    - to_dataframe() include z\n",
+    "    - sel/slice on vertical coordinate\n",
+    "    - vertical_skill(binds, metric) > SkillProfile (1D in z)\n",
+    "6. SkillProfile \n",
+    "    - xr.Dataset with dim z, with .plot()  or a \n",
+    "        - producing bias/RMSE vs depth\n",
+    "        - Havmöller? or depth/time (would require raw model)\n",
+    "        - model and obs on depth (Profiles) (would require raw model)\n",
+    "\n",
+    "Pros: \n",
+    "- Reduced code modification/additions. \n",
+    "- Use same plots etc. as today. \n",
+    "- Need to add vertical_skill and SkillProfile  \n",
+    "\n",
+    "Cons: \n",
+    "- The comparer plots we have today does not consider vertical. \n",
+    "- Most plots above will not be supported (maybe only plots in SkillProfile)\n",
+    "- No easy way to aggregate depth\n",
+    "\n",
+    "Questions: Do we need new 3dModel class?\n",
+    "\n",
+    "\n",
+    "Example:\n",
+    "```Python\n",
+    "\n",
+    "# ==============================================\n",
+    "# Match mod and obs\n",
+    "# ==============================================\n",
+    "\n",
+    "# Create vertical observations\n",
+    "vobs1 = VerticalObservation(df_1, x=, y=)  \n",
+    "vobs2 = VerticalObservation(df_2, x=, y=) #or dfs1?\n",
+    "\n",
+    "# Probably need sigma layers support...current model have support for Dfsu3D, Dfsu2H...check this**  \n",
+    "mr = DfsuModelResult(ds, name=\"HD3D Salinity\")   \n",
+    "\n",
+    "# Match observations and model results (detects that we use vertical observations)\n",
+    "cmp = match([vobs1, vobs2], mr) \n",
+    "\n",
+    "# ==============================================\n",
+    "# Slicing comparer\n",
+    "# ==============================================\n",
+    "\n",
+    "# Select depth levels from comparer (notice that we have z-coordinates in observations)\n",
+    "cmp_sfr = cmp.sel(zmin=0, zmax=15)\n",
+    "cmp_z30_40 = cmp.sel(z=[30, 40])\n",
+    "\n",
+    "# or select from area as with other comparers\n",
+    "cmp_sfr_area = cmp.sel(area=(650000, 6.52e6, 668000, 6.54e6))\n",
+    "\n",
+    "# or with time slicing\n",
+    "cmp_sfr_time = vcmp.sel(time=slice(\"2022-07-01\", \"2022-08-01\"))\n",
+    "\n",
+    "# ==============================================\n",
+    "# Comparer plots\n",
+    "# ==============================================\n",
+    "# All plots we have today in comparer can be applied to vertical data\n",
+    "# However, no vertical information without slicing the comparer.\n",
+    "# NOTE: most plots today deal with time, no way of plotting time,depth (e.g. havmöller)\n",
+    "v.plot.timeseres()\n",
+    "cmp.plot.box()\n",
+    "...\n",
+    "\n",
+    "# Vertical info\n",
+    "cmp_20_30 = cmp.sel(z=[30, 40])\n",
+    "cmp_30_40 = cmp.sel(z=[30, 40])\n",
+    "cmp_30_40.plot.bar()\n",
+    "cmp_20_30.plot.bar()\n",
+    "\n",
+    "# ==============================================\n",
+    "# Skill\n",
+    "# ==============================================\n",
+    "# Same as today and same problem as above.\n",
+    "# Comaprer needs to be sliced to get vertical info\n",
+    "cmp_20_30.skill()\n",
+    "cmp.skill()\n",
+    "\n",
+    "# ==============================================\n",
+    "# Vertical skill\n",
+    "# ==============================================\n",
+    "sp = cmp.vertical_skill() > SkillProfile\n",
+    "\n",
+    "sp.metrics\n",
+    "['n', 'bias', 'rmse', 'urmse', 'mae', 'cc', 'si', 'r2']\n",
+    "\n",
+    "#selection\n",
+    "sp.sel(z = [50:100])\n",
+    "\n",
+    "# plots\n",
+    "sp.plot.profile() # bias, rmse... plots as seen above (y-axis = depth, x-axis=value)\n",
+    "...\n",
+    "\n",
+    "```\n",
+    "**Option:**   \n",
+    "Add VerticalAccessor on Comparer to allow vertical aggregation and dedicated profile plots** \n",
+    "\n",
+    "Reason:\n",
+    "- Supports more plots\n",
+    "- Supports aggregation \n",
+    "- no need for vertical_skill() on comparer. It is now on vertical\n",
+    "\n",
+    "VerticalAccessor:\n",
+    "- vertical.mean(zmin, zmax) > returns collapsed comparer. Ordinary workflow after that\n",
+    "- vertical.slice(depth) > returns collapsed comparer. Ordinary workflow after that\n",
+    "    - Equivalent to creating per‑depth DataFrames manually today.\n",
+    "- vertical.skill(mertrics, ) > SkillProfile  similar to SkillGrid with xarray dataset and z coord and .plot() for lines \n",
+    "    - Analogous to Comparer.skill() → SkillTable, Comparer.gridded_skill() → SkillGrid (x–y grid)\n",
+    "- vertical.skill(bins|bin_size) (as gridded_skill() but for z)\n",
+    "    - Analogous to spatial binning (gridded_skill) but in 1D (depth).\n",
+    "- vertical.plot.profile() - value vs depth for mod and obs\n",
+    "    - Just like plot.timeseries() but operating in depth space.\n",
+    "\n",
+    "Example:\n",
+    "```Python\n",
+    "# Examples:\n",
+    "cmp0_15 = cmp.vertical.mean(zmin=0, zmax=15) #(max, min as well)\n",
+    "cmp0_15.plot.timeseries() #no depth coordinate anymore\n",
+    "cmp0_15.skill()\n",
+    "\n",
+    "cmp5m = cmp.vertical.slice(depth=5)\n",
+    "cmp5m.plot.timeseries() \n",
+    "\n",
+    "sp = cmp.vertical.skill(metrics=[\"rmse\", \"bias\"], bin_size=) #options to bin data\n",
+    "sp.rmse.plot()        # RMSE vs depth\n",
+    "sp.bias.plot()\n",
+    "\n",
+    "cmp.vertical.plot.profile(time=\"2023-07-15T12:00\")\n",
+    "\n",
+    "cmp.vertical.plot.skill(metric=\"rmse\")\n",
+    "\n",
+    "\n",
+    "class Comparer:\n",
+    "    ...\n",
+    "    @property\n",
+    "    def vertical(self):\n",
+    "        return VerticalAccessor(self)\n",
+    "\n",
+    "\n",
+    "class VerticalAccessor:\n",
+    "    def __init__(self, cmp):\n",
+    "        self._cmp = cmp\n",
+    "\n",
+    "    def mean(self, zmin, zmax):\n",
+    "        ds = self._cmp.data\n",
+    "        ds2 = _vertical_mean(ds, zmin, zmax)  # returns time-series dataset\n",
+    "        return Comparer.from_matched_dataset(ds2)  # uses existing API\n",
+    "\n",
+    "    def slice(self, depth):\n",
+    "        ds = self._cmp.data\n",
+    "        ds2 = _vertical_interpolate(ds, depth)\n",
+    "        return Comparer.from_matched_dataset(ds2)\n",
+    "\n",
+    "    def skill(self, metrics=None, bins=None):\n",
+    "        ds = self._cmp.data\n",
+    "        if bins:\n",
+    "            dsb = _vertical_bin(ds, bins)\n",
+    "        else:\n",
+    "            dsb = ds\n",
+    "        sk = _compute_skill_per_depth(dsb, metrics)\n",
+    "        return SkillProfile(sk)\n",
+    "\n",
+    "    @property\n",
+    "    def plot(self):\n",
+    "        return VerticalPlotAccessor(self._cmp)\n",
+    "\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "94922ec0",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "modelskill",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.14.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/modelskill/__init__.py b/src/modelskill/__init__.py
index 0b3ce52f2..2e70004de 100644
--- a/src/modelskill/__init__.py
+++ b/src/modelskill/__init__.py
@@ -36,6 +36,7 @@
 from .model import (
     PointModelResult,
     TrackModelResult,
+    VerticalModelResult,
     GridModelResult,
     DfsuModelResult,
     DummyModelResult,
@@ -44,6 +45,7 @@
     observation,
     PointObservation,
     TrackObservation,
+    VerticalObservation,
 )
 from .matching import from_matched, match
 from .configuration import from_config
@@ -91,12 +93,14 @@ def load(filename: Union[str, Path]) -> Comparer | ComparerCollection:
     "model_result",
     "PointModelResult",
     "TrackModelResult",
+    "VerticalModelResult",
     "GridModelResult",
     "DfsuModelResult",
     "DummyModelResult",
     "observation",
     "PointObservation",
     "TrackObservation",
+    "VerticalObservation",
     "TimeSeries",
     "match",
     "from_matched",
diff --git a/src/modelskill/comparison/_comparer_plotter.py b/src/modelskill/comparison/_comparer_plotter.py
index 5467eafc2..0f548dfc8 100644
--- a/src/modelskill/comparison/_comparer_plotter.py
+++ b/src/modelskill/comparison/_comparer_plotter.py
@@ -746,7 +746,14 @@ def taylor(
         df = df.rename(columns={"_std_obs": "obs_std", "_std_mod": "std"})
 
         pts = [
-            TaylorPoint(name=r.model, obs_std=r.obs_std, std=r.std, cc=r.cc, marker=marker, marker_size=marker_size)
+            TaylorPoint(
+                name=r.model,
+                obs_std=r.obs_std,
+                std=r.std,
+                cc=r.cc,
+                marker=marker,
+                marker_size=marker_size,
+            )
             for r in df.itertuples()
         ]
 
diff --git a/src/modelskill/comparison/_comparison.py b/src/modelskill/comparison/_comparison.py
index f8fedef75..94c961163 100644
--- a/src/modelskill/comparison/_comparison.py
+++ b/src/modelskill/comparison/_comparison.py
@@ -25,7 +25,7 @@
 from .. import Quantity
 from ..types import GeometryType
 from ..obs import PointObservation, TrackObservation
-from ..model import PointModelResult, TrackModelResult
+from ..model import PointModelResult, TrackModelResult, VerticalModelResult
 from ..timeseries._timeseries import _validate_data_var_name
 from ._comparer_plotter import ComparerPlotter
 from ..metrics import _parse_metric
@@ -175,6 +175,7 @@ class ItemSelection:
     aux: Sequence[str]
     x: Optional[str] = None
     y: Optional[str] = None
+    z: Optional[str] = None
 
     def __post_init__(self) -> None:
         # check that obs, model and aux are unique, and that they are not overlapping
@@ -189,6 +190,8 @@ def all(self) -> Sequence[str]:
             res.append(self.x)
         if self.y is not None:
             res.append(self.y)
+        if self.z is not None:
+            res.append(self.z)
         return res
 
     @staticmethod
@@ -199,6 +202,7 @@ def parse(
         aux_items: Optional[Iterable[str | int]] = None,
         x_item: str | int | None = None,
         y_item: str | int | None = None,
+        z_item: str | int | None = None,
     ) -> ItemSelection:
         """Parse item selection.
 
@@ -214,26 +218,30 @@ def parse(
             raise ValueError("data must contain at least two items")
         obs_name = _get_name(obs_item, items) if obs_item else items[0]
 
-        # Check existance of items and convert to names
-
+        # Check existence of items and convert to names
         if aux_items is not None:
             if isinstance(aux_items, (str, int)):
                 aux_items = [aux_items]
             aux_names = [_get_name(a, items) for a in aux_items]
         else:
             aux_names = []
+
+        x_name = _get_name(x_item, items) if x_item is not None else None
+        y_name = _get_name(y_item, items) if y_item is not None else None
+        z_name = _get_name(z_item, items) if z_item is not None else None
+
         if mod_items is not None:
             if isinstance(mod_items, (str, int)):
                 mod_items = [mod_items]
             mod_names = [_get_name(m, items) for m in mod_items]
         else:
+            # Add remaining items as model items
             mod_names = [
-                item for item in items if item not in aux_names and item != obs_name
+                item
+                for item in items
+                if item not in [x_name, y_name, z_name] + aux_names + [obs_name]
             ]
 
-        x_name = _get_name(x_item, items) if x_item is not None else None
-        y_name = _get_name(y_item, items) if y_item is not None else None
-
         if len(mod_names) == 0:
             raise ValueError("no model items were found! Must be at least one")
         if obs_name in mod_names:
@@ -242,7 +250,7 @@ def parse(
             raise ValueError("observation item must not be an auxiliary item")
 
         return ItemSelection(
-            obs=obs_name, model=mod_names, aux=aux_names, x=x_name, y=y_name
+            obs=obs_name, model=mod_names, aux=aux_names, x=x_name, y=y_name, z=z_name
         )
 
 
@@ -302,18 +310,23 @@ def _matched_data_to_xarray(
     z: Optional[float] = None,
     x_item: str | int | None = None,
     y_item: str | int | None = None,
+    z_item: str | int | None = None,
     quantity: Optional[Quantity] = None,
 ) -> xr.Dataset:
     """Convert matched data to accepted xarray.Dataset format"""
     assert isinstance(df, pd.DataFrame)
-    cols = list(df.columns)
-    items = ItemSelection.parse(cols, obs_item, mod_items, aux_items, x_item, y_item)
 
+    cols = list(df.columns)
+    items = ItemSelection.parse(
+        cols, obs_item, mod_items, aux_items, x_item, y_item, z_item
+    )
     # check that x and x_item is not both specified (same for y and y_item)
     if (x is not None) and (x_item is not None):
         raise ValueError("x and x_item cannot both be specified")
     if (y is not None) and (y_item is not None):
         raise ValueError("y and y_item cannot both be specified")
+    if (z is not None) and (z_item is not None):
+        raise ValueError("z and z_item cannot both be specified")
 
     if x is not None:
         try:
@@ -329,7 +342,6 @@ def _matched_data_to_xarray(
             raise TypeError(
                 f"y must be scalar, not {type(y)}; if y is a coordinate variable, use y_item"
             )
-
     # check that items.obs and items.model are numeric
     if not np.issubdtype(df[items.obs].dtype, np.number):
         raise ValueError(
@@ -370,16 +382,21 @@ def _matched_data_to_xarray(
     else:
         ds.coords["y"] = np.nan
 
-    # No z-item so far (relevant for ProfileObservation)
-    if z is not None:
+    if z_item is not None:
+        ds = ds.rename({items.z: "z"}).set_coords("z")
+    elif z is not None:
         ds.coords["z"] = z
+    elif "z" in ds.data_vars:
+        ds = ds.set_coords("z")
+    else:
+        pass  # z is optional, if not provided, we don't add it as a coordinate
 
-    if ds.coords["x"].size == 1:
+    if "z" in ds.coords:
+        ds.attrs["gtype"] = str(GeometryType.VERTICAL)
+    elif ds.coords["x"].size == 1:
         ds.attrs["gtype"] = str(GeometryType.POINT)
     else:
         ds.attrs["gtype"] = str(GeometryType.TRACK)
-    # TODO
-    # ds.attrs["gtype"] = str(GeometryType.PROFILE)
 
     if quantity is None:
         q = Quantity.undefined()
@@ -444,7 +461,10 @@ class Comparer:
     def __init__(
         self,
         matched_data: xr.Dataset,
-        raw_mod_data: dict[str, PointModelResult | TrackModelResult] | None = None,
+        raw_mod_data: dict[
+            str, PointModelResult | TrackModelResult | VerticalModelResult
+        ]
+        | None = None,
     ) -> None:
         self.data = _parse_dataset(matched_data)
         self.raw_mod_data = (
@@ -464,7 +484,9 @@ def __init__(
     @staticmethod
     def from_matched_data(
         data: xr.Dataset | pd.DataFrame,
-        raw_mod_data: Optional[Dict[str, PointModelResult | TrackModelResult]] = None,
+        raw_mod_data: Optional[
+            Dict[str, PointModelResult | TrackModelResult | VerticalModelResult]
+        ] = None,
         obs_item: str | int | None = None,
         mod_items: Optional[Iterable[str | int]] = None,
         aux_items: Optional[Iterable[str | int]] = None,
@@ -475,6 +497,7 @@ def from_matched_data(
         z: Optional[float] = None,
         x_item: str | int | None = None,
         y_item: str | int | None = None,
+        z_item: str | int | None = None,
         quantity: Optional[Quantity] = None,
     ) -> "Comparer":
         """Initialize from compared data"""
@@ -491,9 +514,11 @@ def from_matched_data(
                 z=z,
                 x_item=x_item,
                 y_item=y_item,
+                z_item=z_item,
                 quantity=quantity,
             )
             data.attrs["weight"] = weight
+            # FIXME: Consider changes needed for vertical
         return Comparer(matched_data=data, raw_mod_data=raw_mod_data)
 
     def __repr__(self):
@@ -734,7 +759,9 @@ def _to_observation(self) -> PointObservation | TrackObservation:
         else:
             raise NotImplementedError(f"Unknown gtype: {self.gtype}")
 
-    def _to_model(self) -> list[PointModelResult | TrackModelResult]:
+    def _to_model(
+        self,
+    ) -> list[PointModelResult | TrackModelResult | VerticalModelResult]:
         mods = list(self.raw_mod_data.values())
         return mods
 
@@ -1193,6 +1220,7 @@ def to_dataframe(self) -> pd.DataFrame:
         """Convert matched data to pandas DataFrame
 
         Include x, y coordinates only if gtype=track
+        Include z coordinate only if gtype=vertical
 
         Returns
         -------
@@ -1208,6 +1236,16 @@ def to_dataframe(self) -> pd.DataFrame:
             # make sure that x, y cols are first
             cols = ["x", "y"] + [c for c in df.columns if c not in ["x", "y"]]
             return df[cols]
+        elif self.gtype == str(GeometryType.VERTICAL):
+            df = self.data.drop_vars(["x", "y"]).to_dataframe()
+            # make sure that Observations is first and z is last
+            cols = (
+                ["Observation"]
+                + [c for c in df.columns if c not in ["Observation", "z"]]
+                + ["z"]
+            )
+            return df[cols]
+
         else:
             raise NotImplementedError(f"Unknown gtype: {self.gtype}")
 
@@ -1221,6 +1259,8 @@ def save(self, filename: Union[str, Path]) -> None:
         """
         ds = self.data
 
+        # FIXME: Consider changes needed for vertical
+
         # add self.raw_mod_data to ds with prefix 'raw_' to avoid name conflicts
         # an alternative strategy would be to use NetCDF groups
         # https://docs.xarray.dev/en/stable/user-guide/io.html#groups
@@ -1257,8 +1297,14 @@ def load(filename: Union[str, Path]) -> "Comparer":
         if data.gtype == "track":
             return Comparer(matched_data=data)
 
+        if data.gtype == "vertical":
+            # FIXME: consider during Phase3
+            return Comparer(matched_data=data)
+
         if data.gtype == "point":
-            raw_mod_data: Dict[str, PointModelResult | TrackModelResult] = {}
+            raw_mod_data: Dict[
+                str, PointModelResult | TrackModelResult | VerticalModelResult
+            ] = {}
 
             for var in data.data_vars:
                 var_name = str(var)
diff --git a/src/modelskill/matching.py b/src/modelskill/matching.py
index b182f0ad7..ce4eee573 100644
--- a/src/modelskill/matching.py
+++ b/src/modelskill/matching.py
@@ -28,7 +28,8 @@
 from .model.dummy import DummyModelResult
 from .model.grid import GridModelResult
 from .model.track import TrackModelResult
-from .obs import Observation, PointObservation, TrackObservation
+from .model.vertical import VerticalModelResult
+from .obs import Observation, PointObservation, TrackObservation, VerticalObservation
 from .timeseries import TimeSeries
 from .types import Period
 
@@ -40,6 +41,7 @@
     GridModelResult,
     DfsuModelResult,
     TrackModelResult,
+    VerticalModelResult,
     DummyModelResult,
 ]
 MRInputType = Union[
@@ -56,7 +58,7 @@
     TimeSeries,
     MRTypes,
 ]
-ObsTypes = Union[PointObservation, TrackObservation]
+ObsTypes = Union[PointObservation, TrackObservation, VerticalObservation]
 ObsInputType = Union[
     str,
     Path,
@@ -85,6 +87,7 @@ def from_matched(
     z: Optional[float] = None,
     x_item: str | int | None = None,
     y_item: str | int | None = None,
+    z_item: str | int | None = None,
 ) -> Comparer:
     """Create a Comparer from data that is already matched (aligned).
 
@@ -113,6 +116,8 @@ def from_matched(
         Name of x item, only relevant for track data
     y_item: [str, int], optional
         Name of y item, only relevant for track data
+    z_item: [str, int], optional
+        Name of z item, only relevant for vertical data for which it must be provided
 
     Returns
     -------
@@ -165,6 +170,7 @@ def from_matched(
         z=z,
         x_item=x_item,
         y_item=y_item,
+        z_item=z_item,
         quantity=quantity,
     )
 
@@ -351,7 +357,9 @@ def _get_global_start_end(idxs: Iterable[pd.DatetimeIndex]) -> Period:
 
 def _match_space_time(
     observation: Observation,
-    raw_mod_data: Mapping[str, PointModelResult | TrackModelResult],
+    raw_mod_data: Mapping[
+        str, PointModelResult | TrackModelResult | VerticalModelResult
+    ],
     max_model_gap: float | None,
     spatial_tolerance: float,
     obs_no_overlap: Literal["ignore", "error", "warn"],
@@ -375,6 +383,8 @@ def _match_space_time(
                 )
             case PointModelResult() as pmr, PointObservation():
                 aligned = pmr.align(observation, max_gap=max_model_gap)
+            case VerticalModelResult() as vmr, VerticalObservation():
+                aligned = vmr.align(observation)
             case _:
                 raise TypeError(
                     f"Matching not implemented for model type {type(mr)} and observation type {type(observation)}"
@@ -386,7 +396,6 @@ def _match_space_time(
             raise ValueError(
                 f"Aux variables are not allowed to have identical names. Choose either aux from obs or model. Overlapping: {overlapping}"
             )
-
         for dv in aligned:
             data[dv] = aligned[dv]
 
diff --git a/src/modelskill/model/__init__.py b/src/modelskill/model/__init__.py
index b0ba468ae..64c9ab171 100644
--- a/src/modelskill/model/__init__.py
+++ b/src/modelskill/model/__init__.py
@@ -18,6 +18,7 @@
 from .factory import model_result
 from .point import PointModelResult
 from .track import TrackModelResult
+from .vertical import VerticalModelResult
 from .dfsu import DfsuModelResult
 from .grid import GridModelResult
 from .dummy import DummyModelResult
@@ -25,6 +26,7 @@
 __all__ = [
     "PointModelResult",
     "TrackModelResult",
+    "VerticalModelResult",
     "DfsuModelResult",
     "GridModelResult",
     "model_result",
diff --git a/src/modelskill/model/_base.py b/src/modelskill/model/_base.py
index 250dcc30d..229720c66 100644
--- a/src/modelskill/model/_base.py
+++ b/src/modelskill/model/_base.py
@@ -10,9 +10,10 @@
 if TYPE_CHECKING:
     from .point import PointModelResult
     from .track import TrackModelResult
+    from .vertical import VerticalModelResult
 
 from ..utils import _get_name
-from ..obs import Observation, PointObservation, TrackObservation
+from ..obs import Observation, PointObservation, TrackObservation, VerticalObservation
 
 
 @dataclass
@@ -75,9 +76,9 @@ def _validate_overlap_in_time(time: pd.DatetimeIndex, observation: Observation)
 class SpatialField(Protocol):
     def extract(
         self,
-        observation: PointObservation | TrackObservation,
+        observation: PointObservation | TrackObservation | VerticalObservation,
         spatial_method: Optional[str] = None,
-    ) -> PointModelResult | TrackModelResult: ...
+    ) -> PointModelResult | TrackModelResult | VerticalModelResult: ...
 
     def _extract_point(
         self, observation: PointObservation, spatial_method: Optional[str] = None
@@ -86,3 +87,7 @@ def _extract_point(
     def _extract_track(
         self, observation: TrackObservation, spatial_method: Optional[str] = None
     ) -> TrackModelResult: ...
+
+    def _extract_vertical(
+        self, observation: VerticalObservation, spatial_method: Optional[str] = None
+    ) -> VerticalModelResult: ...
diff --git a/src/modelskill/model/dfsu.py b/src/modelskill/model/dfsu.py
index 511e829d6..022ef454b 100644
--- a/src/modelskill/model/dfsu.py
+++ b/src/modelskill/model/dfsu.py
@@ -13,7 +13,8 @@
 from ..utils import _get_idx
 from .point import PointModelResult
 from .track import TrackModelResult
-from ..obs import Observation, PointObservation, TrackObservation
+from .vertical import VerticalModelResult
+from ..obs import Observation, PointObservation, TrackObservation, VerticalObservation
 
 
 class DfsuModelResult(SpatialField):
@@ -73,7 +74,9 @@ def __init__(
             item = data.name
             self.sel_items = SelectedItems(values=data.name, aux=[])
             data = mikeio.Dataset({data.name: data})
-        elif isinstance(data, (mikeio.dfsu.Dfsu2DH, mikeio.dfsu.Dfsu3D)):
+        elif isinstance(
+            data, (mikeio.dfsu.Dfsu2DH, mikeio.dfsu.Dfsu3D, mikeio.Dataset)
+        ):
             item_names = [i.name for i in data.items]
             idx = _get_idx(x=item, valid_names=item_names)
             item_info = data.items[idx]
@@ -113,14 +116,14 @@ def _in_domain(self, x: float, y: float) -> bool:
 
     def extract(
         self, observation: Observation, spatial_method: Optional[str] = None
-    ) -> PointModelResult | TrackModelResult:
+    ) -> PointModelResult | TrackModelResult | VerticalModelResult:
         """Extract ModelResult at observation positions
 
         Note: this method is typically not called directly, but by the match() method.
 
         Parameters
         ----------
-        observation : <PointObservation> or <TrackObservation>
+        observation : <PointObservation>, <TrackObservation>, or <VerticalObservation>
             positions (and times) at which modelresult should be extracted
         spatial_method : Optional[str], optional
             spatial selection/interpolation method, 'contained' (=isel),
@@ -129,7 +132,7 @@ def extract(
 
         Returns
         -------
-        PointModelResult or TrackModelResult
+        PointModelResult, TrackModelResult, or VerticalModelResult
             extracted modelresult with the same geometry as the observation
         """
         method = self._parse_spatial_method(spatial_method)
@@ -139,6 +142,8 @@ def extract(
             return self._extract_point(observation, spatial_method=method)
         elif isinstance(observation, TrackObservation):
             return self._extract_track(observation, spatial_method=method)
+        elif isinstance(observation, VerticalObservation):
+            return self._extract_vertical(observation, spatial_method=method)
         else:
             raise NotImplementedError(
                 f"Extraction from {type(self.data)} to {type(observation)} is not implemented."
@@ -162,6 +167,76 @@ def _parse_spatial_method(method: str | None) -> str | None:
         else:
             return METHOD_MAP[method]
 
+    def _extract_vertical(
+        self,
+        observation: VerticalObservation,
+        spatial_method: Optional[str] = None,
+    ) -> VerticalModelResult:
+        x, y = observation.x, observation.y
+
+        if not self._in_domain(x, y):
+            raise ValueError(
+                f"PointObservation '{observation.name}' (x={x}, y={y}) outside model domain!"
+            )
+
+        elemids = self.data.geometry.find_index(x=x, y=y)
+        if len(elemids) == 0:
+            raise ValueError(f"No elements found at observation location ({x}, {y})")
+
+        method = spatial_method or "contained"
+
+        # Check if 3D for dfsu and dataset
+        if not hasattr(self.data, "n_layers") and not hasattr(
+            self.data.geometry, "n_layers"
+        ):
+            raise ValueError("Cannot extract vertical profile from 2D dfsu file.")
+
+        if method == "contained":
+            if isinstance(self.data, mikeio.Dataset):
+                ds_column = self.data.sel(x=x, y=y)
+            elif isinstance(self.data, mikeio.dfsu.Dfsu3D):
+                # FIXME: open with specific element instead...make sure mikeio fix is in place before
+                # ds_column = self.data.read(elements=elemids) # when bug is fixed in mikeio
+                ds_column = self.data.read(items=self.sel_items.all).sel(x=x, y=y)
+            else:
+                raise ValueError(
+                    "Unsupported data type for vertical profile extraction."
+                )
+        else:
+            raise NotImplementedError(
+                "Only spatial_method='contained' is currently implemented for vertical profile extraction from DfsuModelResult. "
+            )
+
+        # get layer depth info
+        layer_boundaries = ds_column.geometry.calc_ze(ds_column._zn)
+
+        item_name = self.sel_items.values
+
+        # Flatten ds_column (time, layers) to 1D format for ProfileModelResult
+        # Need to repeat time for each layer and flatten z and values
+        n_layers = ds_column.geometry.n_layers
+
+        # Create flattened arrays # Repeat each timestamp n_layers times
+        time_flat = np.repeat(ds_column.time, n_layers)
+        z_flat = layer_boundaries.flatten()  # Flatten z-coordinates
+        item_values_1d = ds_column[item_name].to_numpy().flatten()  # Flatten item?
+        # aux_items_flat = {aux_item: ds_column[aux_item].to_numpy().flatten() for aux_item in self.sel_items.aux}
+
+        # Create DataFrame in long format
+        df = pd.DataFrame({"time": time_flat, "z": z_flat, self.name: item_values_1d})
+        # Set time as index for ProfileModelResult parsing
+        df = df.set_index("time")
+        return VerticalModelResult(
+            data=df,
+            item=self.name,
+            z_item="z",
+            x=ds_column.geometry.element_coordinates[0, 0],
+            y=ds_column.geometry.element_coordinates[0, 1],
+            name=self.name,
+            quantity=self.quantity,
+            aux_items=self.sel_items.aux,
+        )
+
     def _extract_point(
         self, observation: PointObservation, spatial_method: Optional[str] = None
     ) -> PointModelResult:
diff --git a/src/modelskill/model/dummy.py b/src/modelskill/model/dummy.py
index 6a1efea0d..cff1629f5 100644
--- a/src/modelskill/model/dummy.py
+++ b/src/modelskill/model/dummy.py
@@ -6,7 +6,7 @@
 
 from modelskill.model.point import PointModelResult
 from modelskill.model.track import TrackModelResult
-from modelskill.obs import PointObservation, TrackObservation
+from modelskill.obs import PointObservation, TrackObservation, VerticalObservation
 
 
 @dataclass
@@ -49,7 +49,7 @@ def __post_init__(self):
 
     def extract(
         self,
-        observation: PointObservation | TrackObservation,
+        observation: PointObservation | TrackObservation | VerticalObservation,
         spatial_method: Optional[str] = None,
     ) -> PointModelResult | TrackModelResult:
         if spatial_method is not None:
@@ -57,6 +57,10 @@ def extract(
                 "spatial interpolation not possible when matching point model results with point observations"
             )
 
+        if isinstance(observation, VerticalObservation):
+            raise NotImplementedError(
+                "DummyModelResult does not support VerticalObservation yet"
+            )
         da = observation.data[observation.name].copy()
         if self.strategy == "mean":
             da[:] = da.mean()
diff --git a/src/modelskill/model/factory.py b/src/modelskill/model/factory.py
index 4ee2cb7f3..649faec39 100644
--- a/src/modelskill/model/factory.py
+++ b/src/modelskill/model/factory.py
@@ -7,6 +7,7 @@
 
 from .point import PointModelResult
 from .track import TrackModelResult
+from .vertical import VerticalModelResult
 from .dfsu import DfsuModelResult
 from .grid import GridModelResult
 
@@ -16,6 +17,7 @@
 _modelresult_lookup = {
     GeometryType.POINT: PointModelResult,
     GeometryType.TRACK: TrackModelResult,
+    GeometryType.VERTICAL: VerticalModelResult,
     GeometryType.UNSTRUCTURED: DfsuModelResult,
     GeometryType.GRID: GridModelResult,
 }
@@ -25,9 +27,17 @@ def model_result(
     data: DataInputType,
     *,
     aux_items: Optional[list[int | str]] = None,
-    gtype: Optional[Literal["point", "track", "unstructured", "grid"]] = None,
+    gtype: Optional[
+        Literal["point", "track", "vertical", "unstructured", "grid"]
+    ] = None,
     **kwargs: Any,
-) -> PointModelResult | TrackModelResult | DfsuModelResult | GridModelResult:
+) -> (
+    PointModelResult
+    | TrackModelResult
+    | VerticalModelResult
+    | DfsuModelResult
+    | GridModelResult
+):
     """A factory function for creating an appropriate object based on the data input.
 
     Parameters
@@ -36,7 +46,7 @@ def model_result(
         The data to be used for creating the ModelResult object.
     aux_items : Optional[list[int | str]]
         Auxiliary items, by default None
-    gtype : Optional[Literal["point", "track", "unstructured", "grid"]]
+    gtype : Optional[Literal["point", "track", "vertical", "unstructured", "grid"]]
         The geometry type of the data. If not specified, it will be guessed from the data.
     **kwargs
         Additional keyword arguments to be passed to the ModelResult constructor.
@@ -48,6 +58,8 @@ def model_result(
     <DfsuModelResult> 'Oresund2D'
     >>> ms.model_result("ERA5_DutchCoast.nc", item="swh", name="ERA5")
     <GridModelResult> 'ERA5'
+    >>> ms.model_result("VerticalProfile_obs1.dfs0", z_item="z", item="Salinity", name="vmod", gtype="vertical")
+    <VerticalModelResult> 'vmod'
     """
     if gtype is None:
         geometry = _guess_gtype(data)
diff --git a/src/modelskill/model/grid.py b/src/modelskill/model/grid.py
index 67b88b35e..dd7518fe9 100644
--- a/src/modelskill/model/grid.py
+++ b/src/modelskill/model/grid.py
@@ -12,7 +12,7 @@
 from ..quantity import Quantity
 from .point import PointModelResult
 from .track import TrackModelResult
-from ..obs import PointObservation, TrackObservation
+from ..obs import PointObservation, TrackObservation, VerticalObservation
 
 
 class GridModelResult(SpatialField):
@@ -124,7 +124,7 @@ def _in_domain(self, x: float, y: float) -> bool:
 
     def extract(
         self,
-        observation: PointObservation | TrackObservation,
+        observation: PointObservation | TrackObservation | VerticalObservation,
         spatial_method: Optional[str] = None,
     ) -> PointModelResult | TrackModelResult:
         """Extract ModelResult at observation positions
@@ -144,6 +144,10 @@ def extract(
         PointModelResult or TrackModelResult
             extracted modelresult
         """
+        if isinstance(observation, VerticalObservation):
+            raise NotImplementedError(
+                "Extraction of VerticalObservation from GridModelResult is not implemented yet."
+            )
         _validate_overlap_in_time(self.time, observation)
         if isinstance(observation, PointObservation):
             return self._extract_point(observation, spatial_method)
diff --git a/src/modelskill/model/vertical.py b/src/modelskill/model/vertical.py
new file mode 100644
index 000000000..c9064cbd1
--- /dev/null
+++ b/src/modelskill/model/vertical.py
@@ -0,0 +1,180 @@
+from __future__ import annotations
+from typing import Any, Literal, Sequence
+
+import xarray as xr
+import pandas as pd
+import numpy as np
+
+from ..types import VerticalType
+from ..quantity import Quantity
+from ..timeseries import TimeSeries, _parse_vertical_input
+from ..obs import VerticalObservation
+
+
+class VerticalModelResult(TimeSeries):
+    """Model result for a vertical column.
+
+    Construct a VerticalColumnModelResult from a dfs0 file,
+    mikeio.Dataset, pandas.DataFrame or a xarray.Datasets
+
+    Parameters
+    ----------
+    data : str, Path, pd.DataFrame, mikeio.Dfs0, mikeio.Dfs0, xr.Dataset
+        The input data or file path
+    name : str | None, optional
+        The name of the model result,
+        by default None (will be set to file name or item name)
+    item : str | int | None, optional
+        If multiple items/arrays are present in the input an item
+        must be given (as either an index or a string), by default None
+    z_item : str | int | None, optional
+        Item of the first coordinate of positions, by default None
+    x : float, optional
+        lateral coordinate of point position, inferred from data if not given, else None
+    y : float, optional
+        zonal coordinate of point position, inferred from data if not given, else None
+    quantity : Quantity, optional
+        Model quantity, for MIKE files this is inferred from the EUM information
+    keep_duplicates : (str, bool), optional
+        Strategy for handling duplicate timestamps (wraps xarray.Dataset.drop_duplicates)
+        "first" to keep first occurrence, "last" to keep last occurrence,
+        False to drop all duplicates, "offset" to add milliseconds to
+        consecutive duplicates, by default "first"
+    aux_items : list[int | str] | None, optional
+        Auxiliary items, by default None
+    """
+
+    def __init__(
+        self,
+        data: VerticalType,
+        *,
+        name: str | None = None,
+        item: str | int | None = None,
+        quantity: Quantity | None = None,
+        z_item: str | int = 0,
+        x: float | None = None,
+        y: float | None = None,
+        keep_duplicates: Literal["first", "last", False] = "first",
+        aux_items: Sequence[int | str] | None = None,
+    ) -> None:
+        if not self._is_input_validated(data):
+            data = _parse_vertical_input(
+                data=data,
+                name=name,
+                item=item,
+                quantity=quantity,
+                z_item=z_item,
+                x=x,
+                y=y,
+                keep_duplicates=keep_duplicates,
+                aux_items=aux_items,
+            )
+        assert isinstance(data, xr.Dataset)
+        data_var = str(list(data.data_vars)[0])
+        data[data_var].attrs["kind"] = "model"
+        super().__init__(data=data)
+
+    @property
+    def z(self) -> Any:
+        """z-coordinate"""
+        return self._coordinate_values("z")
+
+    def _match_to_nearest_times(
+        self, obs_df: pd.DataFrame, t_tol: pd.Timedelta | None = None
+    ) -> pd.DataFrame:
+        """Match model times to nearest observation times within a specified tolerance."""
+        mod_df = self.data[["z"]].to_dataframe()
+        obs_times = obs_df.index.unique().sort_values()
+        mod_times_unique = mod_df.index.unique().sort_values()
+
+        # get_indexer requires a unique, monotonic index - work on unique times first
+        idx = mod_times_unique.get_indexer(obs_times, method="nearest", tolerance=t_tol)
+        valid = idx != -1
+
+        matched_mod_times = mod_times_unique[idx[valid]]
+        obs_times_valid = obs_times[valid]
+
+        return pd.DataFrame(
+            {"obs_time": obs_times_valid, "mod_time": matched_mod_times}
+        )
+
+    def _interpolate_to_obs_depths(
+        self,
+        obs_df: pd.DataFrame,
+        obs_times_valid: pd.Series,
+        matched_mod_times: pd.Series,
+        *,
+        mod_value_col: str,
+    ) -> pd.DataFrame:
+        """Interpolate model values to observation depths for matched times."""
+        records = []
+
+        mod_df = self.data.to_dataframe()
+
+        for obs_t, mod_t in zip(obs_times_valid, matched_mod_times):
+            obs_at_t = obs_df.loc[[obs_t]]
+            mod_at_t = mod_df.loc[[mod_t]]
+
+            obs_z = obs_at_t["z"].to_numpy(dtype=float)
+
+            # Sort model depths for np.interp
+            mod_at_t_sorted = mod_at_t.sort_values("z")
+            m_z = mod_at_t_sorted["z"].to_numpy(dtype=float)
+            m_v = mod_at_t_sorted[mod_value_col].to_numpy(dtype=float)
+
+            # make sure we have at least 2 points to interpolate, otherwise skip this time step
+            if m_z.size < 2:
+                continue
+
+            mod_interp = np.interp(obs_z, m_z, m_v, left=np.nan, right=np.nan)
+            for z, mod_v in zip(obs_z, mod_interp):
+                records.append({"time": obs_t, "z": z, self.name: mod_v})
+
+        if not records:
+            return pd.DataFrame(
+                columns=["z", self.name], index=pd.Index([], name="time")
+            )
+
+        return pd.DataFrame(records).set_index("time")
+
+    def align(
+        self, vo: VerticalObservation, temporal_tolerance: pd.Timedelta | None = None
+    ) -> xr.Dataset:
+        """Align model result to observation by matching nearest times and interpolating to observation depths.
+
+        Model depths outside the range of observation depths are extrapolated using nearest model values.
+
+        Parameters
+        ----------
+        vo : VerticalObservation
+            Vertical observation to align with
+        temporal_tolerance : pd.Timedelta, optional
+            Maximum allowed time difference for matching, by default None
+
+        Returns
+        -------
+        xr.Dataset
+            Aligned model result
+
+        """
+        # if temporal_tolerance is not given. Estimate on half the median time step of the model data.
+        if temporal_tolerance is None:
+            median_dt = self.time.unique().to_series().diff().median()
+            temporal_tolerance = median_dt / 2
+
+        matched_times = self._match_to_nearest_times(
+            vo.data[["z"]].to_dataframe(),
+            t_tol=temporal_tolerance,
+        )
+
+        pairs = self._interpolate_to_obs_depths(
+            vo.data.to_dataframe(),
+            matched_times["obs_time"],
+            matched_times["mod_time"],
+            mod_value_col=self.name,
+        )
+        # Convert to xarray Dataset and set kind attribute
+        xarr = pairs.reset_index().set_index(["time"]).to_xarray()
+        xarr[self.name].attrs["kind"] = "model"
+
+        return xarr
diff --git a/src/modelskill/obs.py b/src/modelskill/obs.py
index 5828b2b03..e76562b54 100644
--- a/src/modelskill/obs.py
+++ b/src/modelskill/obs.py
@@ -1,10 +1,11 @@
 """
 # Observations
 
-ModelSkill supports two types of observations:
+ModelSkill supports three types of observations:
 
 * [`PointObservation`](`modelskill.PointObservation`) - a point timeseries from a dfs0/nc file or a DataFrame
 * [`TrackObservation`](`modelskill.TrackObservation`) - a track (moving point) timeseries from a dfs0/nc file or a DataFrame
+* [`VerticalObservation`](`modelskill.VerticalObservation`) - a vertical profile from a dfs0/nc file or a DataFrame
 
 An observation can be created by explicitly invoking one of the above classes or using the [`observation()`](`modelskill.observation`) function which will return the appropriate type based on the input data (if possible).
 """
@@ -16,12 +17,13 @@
 import pandas as pd
 import xarray as xr
 
-from .types import PointType, TrackType, GeometryType, DataInputType
+from .types import PointType, TrackType, VerticalType, GeometryType, DataInputType
 from . import Quantity
 from .timeseries import (
     TimeSeries,
     _parse_xyz_point_input,
     _parse_track_input,
+    _parse_vertical_input,
 )
 
 # NetCDF attributes can only be str, int, float https://unidata.github.io/netcdf4-python/#attributes-in-a-netcdf-file
@@ -31,9 +33,9 @@
 def observation(
     data: DataInputType,
     *,
-    gtype: Optional[Literal["point", "track"]] = None,
+    gtype: Optional[Literal["point", "track", "vertical"]] = None,
     **kwargs,
-) -> PointObservation | TrackObservation:
+) -> PointObservation | TrackObservation | VerticalObservation:
     """Create an appropriate observation object.
 
     A factory function for creating an appropriate observation object
@@ -41,19 +43,21 @@ def observation(
 
     If 'x' or 'y' is given, a PointObservation is created.
     If 'x_item' or 'y_item' is given, a TrackObservation is created.
+    If 'z_item' is given, a VerticalObservation is created.
+    If gtype is explicitly given, it will be used to determine the type of observation.
 
     Parameters
     ----------
     data : DataInputType
         The data to be used for creating the Observation object.
-    gtype : Optional[Literal["point", "track"]]
+    gtype : Optional[Literal["point", "track", "vertical"]], optional
         The geometry type of the data. If not specified, it will be guessed from the data.
     **kwargs
         Additional keyword arguments to be passed to the Observation constructor.
 
     Returns
     -------
-    PointObservation or TrackObservation
+    PointObservation or TrackObservation or VerticalObservation
         An observation object of the appropriate type
 
     Examples
@@ -76,13 +80,16 @@ def observation(
 def _guess_gtype(**kwargs) -> GeometryType:
     """Guess geometry type from data"""
 
-    if "x" in kwargs and "y" in kwargs:
+    if "z_item" in kwargs:
+        return GeometryType.VERTICAL
+    elif "x" in kwargs and "y" in kwargs:
         return GeometryType.POINT
     elif "x_item" in kwargs or "y_item" in kwargs:
         return GeometryType.TRACK
     else:
         warnings.warn(
-            "Could not guess geometry type from data or args, assuming POINT geometry. Use PointObservation or TrackObservation to be explicit."
+            "Could not guess geometry type from data or args, assuming POINT geometry. "
+            "Use PointObservation, TrackObservation, or VerticalObservation to be explicit."
         )
         return GeometryType.POINT
 
@@ -340,6 +347,106 @@ def __init__(
         super().__init__(data=data, weight=weight, attrs=attrs)
 
 
+class VerticalObservation(Observation):
+    """Class for observations of vertical profiles.
+
+    Create a VerticalObservation from a dfs0/nc file or tabular data
+    containing time, vertical coordinate, and observed values.
+
+    Parameters
+    ----------
+    data : (str, Path, pd.DataFrame, mikeio.Dfs0, mikeio.Dataset, xr.Dataset)
+        Input data with vertical profile observations.
+    item : int or str, optional
+        Index or name of the primary observation item.
+        If the input contains more than one candidate value item,
+        this argument must be provided.
+    x : float, optional
+        x-coordinate of the observation location. If not provided,
+        it is inferred from data when possible.
+    y : float, optional
+        y-coordinate of the observation location. If not provided,
+        it is inferred from data when possible.
+    z_item : int or str, optional
+        Index or name of the vertical coordinate item, by default 0.
+    name : str, optional
+        User-defined name for identification in plots and summaries.
+    weight : float, optional
+        Weighting factor for skill scores, by default 1.0.
+    keep_duplicates : {"first", "last", False}, optional
+        Strategy for handling duplicate timestamps/z pairs.
+    quantity : Quantity, optional
+        Physical quantity metadata used for validation against model results.
+    aux_items : list[int | str], optional
+        List of auxiliary item names or indices to keep in the dataset.
+    attrs : dict, optional
+        Additional attributes to be added to the underlying dataset.
+
+    Examples
+    --------
+    >>> import modelskill as ms
+    >>> import pandas as pd
+    >>> df = pd.DataFrame(
+    ...     {
+    ...         "z": [0.0, -5.0, -10.0, 0.0, -5.0, -10.0],
+    ...         "value": [0.1, 0.3, 0.4, 0.5, 0.3, 0.3],
+    ...     },
+    ...     index=pd.to_datetime(
+    ...         [
+    ...             "2010-01-01 01:00:00",
+    ...             "2010-01-01 01:00:00",
+    ...             "2010-01-01 01:00:00",
+    ...             "2010-01-01 02:00:00",
+    ...             "2010-01-01 02:00:00",
+    ...             "2010-01-01 02:00:00",
+    ...         ]
+    ...     ),
+    ... )
+    >>> df.index.name = "t"
+    >>> print(df.to_string())
+                           z  value
+    t
+    2010-01-01 01:00:00   0.0    0.1
+    2010-01-01 01:00:00  -5.0    0.3
+    2010-01-01 01:00:00 -10.0    0.4
+    2010-01-01 02:00:00   0.0    0.5
+    2010-01-01 02:00:00  -5.0    0.3
+    2010-01-01 02:00:00 -10.0    0.3
+
+    >>> o = ms.VerticalObservation(df, item="value", z_item="z", x=12.0, y=55.0)
+    """
+
+    def __init__(
+        self,
+        data: VerticalType,
+        *,
+        item: Optional[int | str] = None,
+        x: Optional[float] = None,
+        y: Optional[float] = None,
+        z_item: Optional[int | str] = 0,
+        name: Optional[str] = None,
+        weight: float = 1.0,
+        keep_duplicates: Literal["first", "last", False] = "first",
+        quantity: Optional[Quantity] = None,
+        aux_items: Optional[list[int | str]] = None,
+        attrs: Optional[dict] = None,
+    ) -> None:
+        if not self._is_input_validated(data):
+            data = _parse_vertical_input(
+                data,
+                name=name,
+                item=item,
+                quantity=quantity,
+                aux_items=aux_items,
+                z_item=z_item,
+                x=x,
+                y=y,
+                keep_duplicates=keep_duplicates,
+            )
+        assert isinstance(data, xr.Dataset)
+        super().__init__(data=data, weight=weight, attrs=attrs)
+
+
 def unit_display_name(name: str) -> str:
     """Display name
 
@@ -364,4 +471,5 @@ def unit_display_name(name: str) -> str:
 _obs_class_lookup = {
     GeometryType.POINT: PointObservation,
     GeometryType.TRACK: TrackObservation,
+    GeometryType.VERTICAL: VerticalObservation,
 }
diff --git a/src/modelskill/plotting/_spatial_overview.py b/src/modelskill/plotting/_spatial_overview.py
index 156af04c1..25a04ee74 100644
--- a/src/modelskill/plotting/_spatial_overview.py
+++ b/src/modelskill/plotting/_spatial_overview.py
@@ -8,7 +8,8 @@
 
 from ..model.point import PointModelResult
 from ..model.track import TrackModelResult
-from ..obs import Observation, PointObservation, TrackObservation
+from ..model.vertical import VerticalModelResult
+from ..obs import Observation, PointObservation, TrackObservation, VerticalObservation
 from ._misc import _get_ax
 
 
@@ -68,7 +69,7 @@ def spatial_overview(
 
     # TODO: support Gridded ModelResults
     for m in mods:
-        if isinstance(m, (PointModelResult, TrackModelResult)):
+        if isinstance(m, (PointModelResult, TrackModelResult, VerticalModelResult)):
             raise ValueError(
                 f"Model type {type(m)} not supported. Only DfsuModelResult and mikeio.GeometryFM supported!"
             )
@@ -82,7 +83,11 @@ def spatial_overview(
         g.plot.outline(ax=ax)  # type: ignore
 
     for o in obs:
-        if isinstance(o, PointObservation):
+        if (
+            isinstance(o, PointObservation)
+            or isinstance(o, TrackObservation)
+            or isinstance(o, VerticalObservation)
+        ):
             ax.scatter(x=o.x, y=o.y, marker="x")
         elif isinstance(o, TrackObservation):
             if o.n_points < 10000:
diff --git a/src/modelskill/timeseries/__init__.py b/src/modelskill/timeseries/__init__.py
index cb1ca3ffc..0a660b0c4 100644
--- a/src/modelskill/timeseries/__init__.py
+++ b/src/modelskill/timeseries/__init__.py
@@ -1,9 +1,11 @@
 from ._timeseries import TimeSeries
 from ._point import _parse_xyz_point_input
 from ._track import _parse_track_input
+from ._vertical import _parse_vertical_input
 
 __all__ = [
     "TimeSeries",
     "_parse_xyz_point_input",
     "_parse_track_input",
+    "_parse_vertical_input",
 ]
diff --git a/src/modelskill/timeseries/_timeseries.py b/src/modelskill/timeseries/_timeseries.py
index 52ff26ca4..2150c3ca2 100644
--- a/src/modelskill/timeseries/_timeseries.py
+++ b/src/modelskill/timeseries/_timeseries.py
@@ -62,14 +62,30 @@ def _validate_dataset(ds: xr.Dataset) -> xr.Dataset:
         ds.time.to_index().is_monotonic_increasing
     ), "time must be increasing (please check for duplicate times))"
 
+    if "gtype" not in ds.attrs:
+        raise ValueError("data must have a gtype attribute")
+
+    # Validate gtype
+    gtype = ds.attrs["gtype"]
+    valid_gtypes = {
+        str(GeometryType.POINT),
+        str(GeometryType.TRACK),
+        str(GeometryType.VERTICAL),
+    }
+    if gtype not in valid_gtypes:
+        raise ValueError(
+            f"data attribute 'gtype' must be one of {GeometryType.POINT}, {GeometryType.TRACK} or {GeometryType.VERTICAL}"
+        )
+
     # Validate coordinates
     if "x" not in ds.coords:
         raise ValueError("data must have an x-coordinate")
     if "y" not in ds.coords:
         raise ValueError("data must have a y-coordinate")
     if "z" not in ds.coords:
+        if gtype == str(GeometryType.VERTICAL):
+            raise ValueError("data with gtype 'vertical' must have a z-coordinate")
         ds.coords["z"] = np.nan
-    # assert "z" in ds.coords, "data must have a z-coordinate"
 
     # Validate data
     vars = [v for v in ds.data_vars]
@@ -103,15 +119,6 @@ def _validate_dataset(ds: xr.Dataset) -> xr.Dataset:
     da = ds[name]
 
     # Validate attrs
-    if "gtype" not in ds.attrs:
-        raise ValueError("data must have a gtype attribute")
-    if ds.attrs["gtype"] not in [
-        str(GeometryType.POINT),
-        str(GeometryType.TRACK),
-    ]:
-        raise ValueError(
-            f"data attribute 'gtype' must be one of {GeometryType.POINT} or {GeometryType.TRACK}"
-        )
     if "long_name" not in da.attrs:
         da.attrs["long_name"] = Quantity.undefined().name
     if "units" not in da.attrs:
@@ -304,6 +311,8 @@ def to_dataframe(self) -> pd.DataFrame:
             # make sure that x, y cols are first
             cols = ["x", "y"] + [c for c in df.columns if c not in ["x", "y"]]
             return df[cols]
+        elif self.gtype == str(GeometryType.VERTICAL):
+            return self.data.drop_vars(["x", "y"]).to_dataframe()
         else:
             raise NotImplementedError(f"Unknown gtype: {self.gtype}")
 
diff --git a/src/modelskill/timeseries/_vertical.py b/src/modelskill/timeseries/_vertical.py
new file mode 100644
index 000000000..57226ad58
--- /dev/null
+++ b/src/modelskill/timeseries/_vertical.py
@@ -0,0 +1,179 @@
+from __future__ import annotations
+from collections.abc import Hashable
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Literal, get_args, Optional, List, Sequence
+import warnings
+import pandas as pd
+import xarray as xr
+from ._coords import XYZCoords
+import numpy as np
+
+import mikeio
+
+from ..types import GeometryType, VerticalType
+from ..quantity import Quantity
+from ..utils import _get_name
+from ._timeseries import _validate_data_var_name
+
+
+@dataclass
+class VerticalItem:
+    z: str
+    values: str
+    aux: list[str]
+
+    @property
+    def all(self) -> List[str]:
+        return [self.z, self.values] + self.aux
+
+
+def _parse_vertical_items(
+    items: Sequence[Hashable],
+    z_item: int | str | None,
+    item: int | str | None,
+    aux_items: Optional[Sequence[int | str]] = None,
+) -> VerticalItem:
+    """If input has exactly 2 items we accept item=None"""
+    if len(items) < 2:
+        raise ValueError(
+            f"Input has only {len(items)} items. It should have at least 2."
+        )
+    if item is None:
+        if len(items) == 2:
+            item = 1
+        elif len(items) > 2:
+            raise ValueError(
+                f"Input has more than 2 items, but item was not given! Available items: {items}"
+            )
+
+    item = _get_name(item, valid_names=items)
+    z_item = _get_name(z_item, valid_names=items)
+    if isinstance(aux_items, (str, int)):
+        aux_items = [aux_items]
+    aux_items_str = [_get_name(i, valid_names=items) for i in aux_items or []]
+
+    # check that there are no duplicates
+    res = VerticalItem(z=z_item, values=item, aux=aux_items_str)
+    if len(set(res.all)) != len(res.all):
+        raise ValueError(f"Duplicate items! {res.all}")
+
+    return res
+
+
+def _include_location(
+    ds: xr.Dataset,
+    *,
+    coords: Optional[XYZCoords] = None,
+) -> xr.Dataset:
+    ds = ds.copy()
+    if coords is not None:
+        # ds might already have some coordinates set, so we will update
+        # only the ones that are NOT already present
+        coords_to_add = {}
+        for k, v in coords.as_dict.items():
+            # Add if coordinate doesn't exist, or if user provided a non-null value
+            if k not in ds.coords or (v is not None and not np.isnan(v)):
+                coords_to_add[k] = v
+        ds.coords.update(coords_to_add)
+
+    return ds
+
+
+def _parse_vertical_input(
+    data: VerticalType,
+    name: Optional[str],
+    item: str | int | None,
+    quantity: Optional[Quantity],
+    z_item: str | int | None,
+    x: float | None = None,
+    y: float | None = None,
+    keep_duplicates: Literal["first", "last", False] = "first",
+    aux_items: Optional[Sequence[int | str]] = None,
+) -> xr.Dataset:
+    assert isinstance(
+        data, get_args(VerticalType)
+    ), "Could not construct Vertical object from provided data."
+    if isinstance(data, (str, Path)):
+        if Path(data).suffix != ".dfs0":
+            raise ValueError(f"File must be a dfs0 file, not {Path(data).suffix}")
+        name = name or Path(data).stem
+        data = mikeio.read(data)  # now mikeio.Dataset
+    elif isinstance(data, mikeio.Dfs0):
+        data = data.read()  # now mikeio.Dataset
+
+    # parse items
+    valid_items: Sequence[Hashable]
+    if isinstance(data, mikeio.Dataset):
+        valid_items = [i.name for i in data.items]
+    elif isinstance(data, pd.DataFrame):
+        valid_items = list(data.columns)
+    elif isinstance(data, xr.Dataset):
+        valid_items = list(data.data_vars)
+    else:
+        raise ValueError("Could not construct Vertical object from provided data")
+
+    sel_items = _parse_vertical_items(
+        valid_items, z_item=z_item, item=item, aux_items=aux_items
+    )
+
+    name = name or sel_items.values
+    name = _validate_data_var_name(name)
+
+    # parse quantity
+    if isinstance(data, mikeio.Dataset):
+        if quantity is None:
+            quantity = Quantity.from_mikeio_iteminfo(data[sel_items.values].item)
+    model_quantity = Quantity.undefined() if quantity is None else quantity
+
+    # select relevant items and convert to xr.Dataset
+    assert isinstance(data, (mikeio.Dataset, pd.DataFrame, xr.Dataset))
+    data = data[sel_items.all]
+    if isinstance(data, mikeio.Dataset):
+        ds = data.to_xarray()
+    elif isinstance(data, pd.DataFrame):
+        data.index.name = "time"
+        ds = data.to_xarray()
+    else:
+        assert len(data.dims) == 1, "Only 0-dimensional data are supported"
+        if data.coords[list(data.coords)[0]].name != "time":
+            data = data.rename({list(data.coords)[0]: "time"})
+        ds = data
+
+    ds = ds.rename({sel_items.z: "z"})
+
+    # keep first, last or none of duplicate (time, z) pairs
+    idx_df = pd.DataFrame({"time": ds["time"].to_index(), "z": ds["z"].values})
+
+    keep_mask = ~idx_df.duplicated(subset=["time", "z"], keep=keep_duplicates)
+
+    n_removed = int((~keep_mask).sum())
+    ds = ds.isel(time=keep_mask.values)
+    if n_removed > 0:
+        warnings.warn(
+            f"Removed {n_removed} duplicate (time, z) entries with keep={keep_duplicates}"
+        )
+
+    ds = ds.dropna(dim="time", subset=["z"])  # remove times with z as nan
+
+    SPATIAL_DIMS = ["x", "y", "z"]
+    for dim in SPATIAL_DIMS:
+        if dim in ds:
+            ds = ds.set_coords(dim)
+
+    # Fixed x,y location
+    coords = XYZCoords(x=x, y=y, z=None)
+    ds = _include_location(ds, coords=coords)  # Add x,y to coords
+
+    assert len(ds.data_vars) == 1 + len(sel_items.aux)
+    data_var = str(list(ds.data_vars)[0])
+    ds = ds.rename({data_var: name})
+    ds[name].attrs["long_name"] = model_quantity.name
+    ds[name].attrs["units"] = model_quantity.unit
+
+    for aux_item in sel_items.aux:
+        ds[aux_item].attrs["kind"] = "aux"
+
+    ds.attrs["gtype"] = str(GeometryType.VERTICAL)
+    assert isinstance(ds, xr.Dataset)
+    return ds
diff --git a/src/modelskill/types.py b/src/modelskill/types.py
index 0ba3dcd63..2e2851f01 100644
--- a/src/modelskill/types.py
+++ b/src/modelskill/types.py
@@ -14,6 +14,7 @@ class GeometryType(Enum):
     TRACK = "track"
     UNSTRUCTURED = "unstructured"
     GRID = "grid"
+    VERTICAL = "vertical"
 
     def __str__(self) -> str:
         return self.name.lower()
@@ -37,6 +38,8 @@ def from_string(s: str) -> "GeometryType":
         <GeometryType.UNSTRUCTURED: 'unstructured'>
         >>> GeometryType.from_string("grid")
         <GeometryType.GRID: 'grid'>
+        >>> GeometryType.from_string("vertical")
+        <GeometryType.VERTICAL: 'vertical'>
         """
 
         try:
@@ -87,6 +90,7 @@ def from_string(s: str) -> "GeometryType":
     xr.DataArray,
 ]
 TrackType = Union[str, Path, pd.DataFrame, mikeio.Dfs0, mikeio.Dataset, xr.Dataset]
+VerticalType = Union[str, Path, pd.DataFrame, mikeio.Dfs0, mikeio.Dataset, xr.Dataset]
 
 
 @dataclass(frozen=True)
diff --git a/tests/model/test_vertical.py b/tests/model/test_vertical.py
new file mode 100644
index 000000000..792e29d4b
--- /dev/null
+++ b/tests/model/test_vertical.py
@@ -0,0 +1,311 @@
+import numpy as np
+import pandas as pd
+import pytest
+import modelskill as ms
+import mikeio
+
+
+# ================
+# Data fixtures
+# ================
+@pytest.fixture
+def vertical_model_df() -> pd.DataFrame:
+    return pd.DataFrame(
+        {
+            "z": [-5.0, -4.0, -3.0, -5.0, -4.0, -3.0],
+            "Salinity": [30.0, 31.0, 32.0, 30.5, 31.5, 32.5],
+        },
+        index=pd.to_datetime(
+            [
+                "2019-01-01 00:00:00",
+                "2019-01-01 00:00:00",
+                "2019-01-01 00:00:00",
+                "2019-01-01 01:00:00",
+                "2019-01-01 01:00:00",
+                "2019-01-01 01:00:00",
+            ]
+        ),
+    )
+
+
+@pytest.fixture
+def vertical_model_df_duplicates() -> pd.DataFrame:
+    return pd.DataFrame(
+        {
+            "z": [-5.0, -5.0, -4.0, -4.0, -3.0],
+            "Salinity": [30.0, 300.0, 31.0, 310.0, 32.0],
+        },
+        index=pd.to_datetime(
+            [
+                "2019-01-01 00:00:00",
+                "2019-01-01 00:00:00",
+                "2019-01-01 00:00:00",
+                "2019-01-01 00:00:00",
+                "2019-01-01 01:00:00",
+            ]
+        ),
+    )
+
+
+@pytest.fixture
+def vertical_model_df_aux() -> pd.DataFrame:
+    return pd.DataFrame(
+        {
+            "z": [-5.0, -4.0, -3.0],
+            "Salinity": [30.0, 31.0, 32.0],
+            "Temperature": [8.0, 8.2, 8.4],
+            "Density": [1025.0, 1025.1, 1025.2],
+        },
+        index=[pd.Timestamp("2019-01-01")] * 3,
+    )
+
+
+@pytest.fixture
+def dfs0_fpath() -> str:
+    return "tests/testdata/vertical/VerticalProfile_obs1.dfs0"
+
+
+@pytest.fixture
+def dfs0_ds(dfs0_fpath) -> mikeio.Dataset:
+    return mikeio.read(dfs0_fpath)
+
+
+@pytest.fixture
+def dfsu_fpath() -> str:
+    return "tests/testdata/vertical/sigma_z_coast.dfsu"
+
+
+@pytest.fixture
+def dfsu_ds(dfsu_fpath) -> mikeio.Dataset:
+    return mikeio.read(dfsu_fpath)
+
+
+class TestVerticalModelResult:
+    # ================
+    # Test basic open for different formats
+    # ================
+    @pytest.mark.parametrize(
+        "input_fixture", ["vertical_model_df", "dfs0_ds", "dfs0_fpath"]
+    )
+    def test_open_and_parse(self, request, input_fixture):
+        input = request.getfixturevalue(input_fixture)
+        mr = ms.VerticalModelResult(input, z_item="z", item="Salinity", name="test")
+
+        assert isinstance(mr, ms.VerticalModelResult)
+        assert mr.gtype == "vertical"
+        assert mr.name == "test"
+        assert mr.n_points > 0
+        assert not np.isnan(mr.z).any()
+        assert np.isnan(mr.x)
+        assert np.isnan(mr.y)
+
+        # with x, y
+        mr = ms.VerticalModelResult(
+            input, z_item="z", item="Salinity", name="test", x=12.0, y=55.0
+        )
+        assert isinstance(mr, ms.VerticalModelResult)
+        assert mr.gtype == "vertical"
+        assert mr.name == "test"
+        assert mr.n_points > 0
+        assert mr.x == pytest.approx(12.0)
+        assert mr.y == pytest.approx(55.0)
+
+    def test_open_with_factory(self, dfs0_fpath):
+        mr = ms.model_result(
+            dfs0_fpath, z_item="z", item="Salinity", name="test", gtype="vertical"
+        )
+        assert isinstance(mr, ms.VerticalModelResult)
+        assert mr.gtype == "vertical"
+        assert mr.name == "test"
+
+    # ================
+    # Test failing and optional args
+    # ================
+    # failing without z_item
+    def test_fail_with_3_items_no_item_arg(self, dfs0_ds):
+        ds_test = dfs0_ds.copy()
+        ds_test["extra_item"] = ds_test[1].copy()
+        with pytest.raises(ValueError, match="Input has more than 2 items, but"):
+            _ = ms.VerticalModelResult(ds_test)
+
+    # failing z wronge location
+    def test_item_named_z(self, dfs0_ds):
+        ds_test = mikeio.Dataset(
+            [dfs0_ds[1], dfs0_ds[0]],
+        )
+        with pytest.raises(ValueError, match="name 'z' is reserved "):
+            _ = ms.VerticalModelResult(ds_test)
+
+    # ===============
+    # test arguments options for handling duplicates
+    # ===============
+    @pytest.mark.parametrize(
+        "keep_duplicates,expected_removed,expected_z,expected_values",
+        [
+            ("first", 2, [-5.0, -4.0, -3.0], [30.0, 31.0, 32.0]),
+            ("last", 2, [-5.0, -4.0, -3.0], [300.0, 310.0, 32.0]),
+            (False, 4, [-3.0], [32.0]),
+        ],
+    )
+    def test_vertical_model_keep_duplicates_modes(
+        self,
+        vertical_model_df_duplicates,
+        keep_duplicates,
+        expected_removed,
+        expected_z,
+        expected_values,
+    ):
+        with pytest.warns(UserWarning, match=f"Removed {expected_removed} duplicate"):
+            mr = ms.VerticalModelResult(
+                vertical_model_df_duplicates,
+                item="Salinity",
+                z_item="z",
+                x=12.0,
+                y=55.0,
+                keep_duplicates=keep_duplicates,
+            )
+
+        assert list(mr.data["z"].values) == expected_z
+        assert list(mr.data[mr.name].values) == expected_values
+
+    # aux items
+    def test_vertical_model_aux_items_preserved_and_tagged(self, vertical_model_df_aux):
+        mr = ms.VerticalModelResult(
+            vertical_model_df_aux,
+            item="Salinity",
+            z_item="z",
+            x=12.0,
+            y=55.0,
+            aux_items=["Temperature", "Density"],
+        )
+
+        for aux_name in ["Temperature", "Density"]:
+            assert aux_name in mr.data.data_vars
+            assert mr.data[aux_name].attrs["kind"] == "aux"
+
+        df = mr.to_dataframe()
+        assert "z" in df.columns
+        assert "Salinity" in df.columns
+        assert "Temperature" in df.columns
+        assert "Density" in df.columns
+
+    # ================
+    # Test roundtrips and identical parsing
+    # ================
+    def test_vertical_model_roundtrip_from_dataset(self, vertical_model_df):
+        mr = ms.VerticalModelResult(
+            vertical_model_df,
+            item="Salinity",
+            z_item="z",
+            x=12.0,
+            y=55.0,
+            name="salt_model",
+        )
+        mr2 = ms.VerticalModelResult(mr.data)
+
+        assert mr.equals(mr2)
+        assert mr2.gtype == mr.gtype
+        assert mr2.name == mr.name
+
+        # Check that changing name of original does not change roundtripped
+        mr.name = "changed_name"
+        assert mr2.name == "salt_model"
+
+    # ================
+    # Test extract profile and align to obs profiles from dfsu
+    # ===============
+    def test_extract_from_dfsu(self, dfsu_ds):
+        dfsu_mr = ms.DfsuModelResult(dfsu_ds, item=0, name="test")
+        dummy_obs = pd.DataFrame(
+            {"z": [-5.0, -4.0, -3.0], "salt": [30.0, 31.0, 32.0]},
+            index=pd.to_datetime(["2022-06-14 00:00:00"] * 3),
+        )
+        XPOS = 6.575e5
+        YPOS = 6.55e6
+        vo = ms.VerticalObservation(dummy_obs, x=XPOS, y=YPOS, item="salt", z_item="z")
+        vmr = dfsu_mr.extract(vo, spatial_method="contained")
+
+        # approximate selected column coordinates from dfsu geometry
+        dfsu_col = dfsu_ds.sel(x=XPOS, y=YPOS)
+        x_mod_expected_close = dfsu_col.geometry.element_coordinates[0, 0]
+        y_mod_expected_close = dfsu_col.geometry.element_coordinates[0, 1]
+
+        assert isinstance(vmr, ms.VerticalModelResult)
+        assert vmr.gtype == "vertical"
+        assert vmr.name == "test"
+        assert vmr.n_points > 0
+        assert vmr.x == pytest.approx(x_mod_expected_close)
+        assert vmr.y == pytest.approx(y_mod_expected_close)
+
+    def test_extract_from_dfsu_correct_layers(self, dfsu_ds):
+        dfsu_mr = ms.DfsuModelResult(dfsu_ds, item=0, name="test")
+        dummy_obs = pd.DataFrame(
+            {"z": [-5.0, -4.0, -3.0], "salt": [30.0, 31.0, 32.0]},
+            index=pd.to_datetime(["2022-06-14 00:00:00"] * 3),
+        )
+        XPOS = 6.575e5
+        YPOS = 6.55e6
+        vo = ms.VerticalObservation(dummy_obs, x=XPOS, y=YPOS, item="salt", z_item="z")
+        vmr = dfsu_mr.extract(vo, spatial_method="contained")
+
+        # number layers at location from sel
+        dfsu_col = dfsu_ds.sel(x=XPOS, y=YPOS)
+        n_layers_expected = dfsu_col.geometry.n_layers
+
+        # number of layers in VerticalModelResult
+        ntimes = len(np.unique(vmr.data.time.values))
+        n_layers = int(len(vmr.data.z.values) / ntimes)
+
+        assert n_layers == n_layers_expected
+
+        # ===
+        # Also check values
+        # ===
+
+        # expected element depths from dfsu geometry
+        element_depths_expected = dfsu_col.geometry.calc_ze(dfsu_col._zn)
+
+        # element depths at first timestep from VerticalModelResult
+        element_depths_t0 = vmr.data.sel(time=vmr.data.time.values[0]).z.values
+        # element depths at last timestep from VerticalModelResult
+        element_depths_tend = vmr.data.sel(time=vmr.data.time.values[-1]).z.values
+
+        assert np.allclose(element_depths_t0, element_depths_expected[0, :])
+        assert np.allclose(element_depths_tend, element_depths_expected[-1, :])
+
+    @pytest.mark.parametrize("spatial_method", ["nearest", "inverse_distance"])
+    def test_extract_from_dfsu_unsupported_spatial_methods_raise(
+        self, dfsu_ds, spatial_method
+    ):
+        dfsu_mr = ms.DfsuModelResult(dfsu_ds, item=0, name="test")
+        dummy_obs = pd.DataFrame(
+            {"z": [-5.0, -4.0, -3.0], "salt": [30.0, 31.0, 32.0]},
+            index=pd.to_datetime(["2022-06-14 00:00:00"] * 3),
+        )
+        xpos = 6.575e5
+        ypos = 6.55e6
+        vo = ms.VerticalObservation(
+            dummy_obs,
+            x=xpos,
+            y=ypos,
+            item="salt",
+            z_item="z",
+        )
+
+        with pytest.raises(
+            NotImplementedError,
+            match="Only spatial_method='contained' is currently implemented",
+        ):
+            _ = dfsu_mr.extract(vo, spatial_method=spatial_method)
+
+    def test_extract_from_dfsu_obs_outside_domain(self, dfsu_ds):
+        dfsu_mr = ms.DfsuModelResult(dfsu_ds, item=0, name="test")
+        dummy_obs = pd.DataFrame(
+            {"z": [-5.0, -4.0, -3.0], "salt": [30.0, 31.0, 32.0]},
+            index=pd.to_datetime(["2022-06-14 00:00:00"] * 3),
+        )
+        XPOS = 1
+        YPOS = 1
+        vo = ms.VerticalObservation(dummy_obs, x=XPOS, y=YPOS, item="salt", z_item="z")
+        with pytest.raises(ValueError, match="outside model domain"):
+            _ = dfsu_mr.extract(vo, spatial_method="contained")
diff --git a/tests/observation/test_vertical_obs.py b/tests/observation/test_vertical_obs.py
new file mode 100644
index 000000000..45e24c3b6
--- /dev/null
+++ b/tests/observation/test_vertical_obs.py
@@ -0,0 +1,263 @@
+from pathlib import Path
+import pandas as pd
+import pytest
+import modelskill as ms
+
+
+@pytest.fixture
+def _vertical_df() -> pd.DataFrame:
+    return pd.DataFrame(
+        {
+            "z": [-5.0, -4.0, -3.0],
+            "value": [1.0, 1.1, 1.2],
+        },
+        index=[pd.Timestamp("2019-01-01")] * 3,
+    )
+
+
+@pytest.fixture
+def _vertical_df_duplicates() -> pd.DataFrame:
+    return pd.DataFrame(
+        {
+            "z": [-5.0, -5.0, -4.0, -4.0, -3.0],
+            "value": [1.0, 10.0, 2.0, 20.0, 7.0],
+        },
+        index=pd.to_datetime(
+            [
+                "2019-01-01 00:00:00",
+                "2019-01-01 00:00:00",
+                "2019-01-01 00:00:00",
+                "2019-01-01 00:00:00",
+                "2019-01-01 01:00:00",
+            ]
+        ),
+    )
+
+
+@pytest.fixture
+def _vertical_df_aux() -> pd.DataFrame:
+    return pd.DataFrame(
+        {
+            "z": [-5.0, -4.0, -3.0],
+            "value": [1.0, 1.1, 1.2],
+            "aux1": [10.0, 11.0, 12.0],
+            "aux2": [20.0, 21.0, 22.0],
+        },
+        index=[pd.Timestamp("2019-01-01")] * 3,
+    )
+
+
+class TestVerticalObservation:
+    def test_vertical_observation_factory_from_kwargs(self, _vertical_df):
+        obs = ms.observation(
+            _vertical_df,
+            item="value",
+            z_item="z",
+            x=12.0,
+            y=55.0,
+        )
+        assert isinstance(obs, ms.VerticalObservation)
+
+    def test_name_kwarg(self, _vertical_df):
+        obs = ms.observation(
+            _vertical_df,
+            item="value",
+            z_item="z",
+            x=12.0,
+            y=55.0,
+            name="argname",
+        )
+        assert obs.name == "argname"
+        # check xarray name
+        assert list(obs.data.data_vars.keys())[0] == "argname"
+
+    def test_factory_from_gtype(self, _vertical_df):
+        obs = ms.observation(
+            _vertical_df,
+            gtype="vertical",
+            item="value",
+            z_item="z",
+            x=12.0,
+            y=55.0,
+        )
+        assert isinstance(obs, ms.VerticalObservation)
+
+    def test_sel_by_z_scalar(self, _vertical_df):
+        obs = ms.VerticalObservation(
+            _vertical_df,
+            item="value",
+            z_item="z",
+            x=12.0,
+            y=55.0,
+        )
+
+        # out = obs.sel(z=-4.0)  # only works with xarray >= 2026.01.0
+        out = ms.VerticalObservation(obs.data.where(obs.data["z"] == -4.0, drop=True))
+
+        assert isinstance(out, ms.VerticalObservation)
+        assert len(out.data) == 1
+        assert out.data.value == 1.1
+
+    def test_open_dfs0_equal(self):
+        fn = Path("tests/testdata/vertical/VerticalProfile_obs2.dfs0")
+        obs = ms.observation(fn, z_item="z")
+        obs2 = ms.VerticalObservation(fn)
+        assert isinstance(obs, ms.VerticalObservation)
+        assert obs.equals(obs2)
+
+    def test_with_and_without_item_arg(self):
+        fn = Path("tests/testdata/vertical/VerticalProfile_ST.dfs0")
+        # no item specified, but multiple items in file
+        with pytest.raises(ValueError):
+            obs = ms.observation(fn, z_item="z")
+        # below should be fine...only one item
+        fn = Path("tests/testdata/vertical/VerticalProfile_obs1.dfs0")
+        assert isinstance(ms.observation(fn, z_item="z"), ms.VerticalObservation)
+
+    def test_duplicated_time_z_pairs(self):
+        df = pd.DataFrame(
+            {
+                "z": [-5.0, -4.0, -4.0],
+                "value": [1.0, 1.1, 1.2],
+            },
+            index=[pd.Timestamp("2019-01-01")] * 3,
+        )
+        with pytest.warns(UserWarning, match="Removed 1 duplicate"):
+            obs = ms.VerticalObservation(
+                df,
+                item="value",
+                z_item="z",
+                x=12.0,
+                y=55.0,
+            )
+
+    def test_keep_duplicates_last_is_applied(self, _vertical_df_duplicates):
+        with pytest.warns(UserWarning, match="Removed 2 duplicate"):
+            obs = ms.VerticalObservation(
+                _vertical_df_duplicates,
+                item="value",
+                z_item="z",
+                x=12.0,
+                y=55.0,
+                keep_duplicates="last",
+            )
+
+        assert list(obs.data["z"].values) == [-5.0, -4.0, -3.0]
+        assert list(obs.data["value"].values) == [10.0, 20.0, 7.0]
+
+    @pytest.mark.parametrize(
+        "keep_duplicates,expected_removed,expected_z,expected_values",
+        [
+            ("first", 2, [-5.0, -4.0, -3.0], [1.0, 2.0, 7.0]),
+            ("last", 2, [-5.0, -4.0, -3.0], [10.0, 20.0, 7.0]),
+            (False, 4, [-3.0], [7.0]),
+        ],
+    )
+    def test_keep_duplicates_modes(
+        self,
+        _vertical_df_duplicates,
+        keep_duplicates,
+        expected_removed,
+        expected_z,
+        expected_values,
+    ):
+        with pytest.warns(UserWarning, match=f"Removed {expected_removed} duplicate"):
+            obs = ms.VerticalObservation(
+                _vertical_df_duplicates,
+                item="value",
+                z_item="z",
+                x=12.0,
+                y=55.0,
+                keep_duplicates=keep_duplicates,
+            )
+
+        assert list(obs.data["z"].values) == expected_z
+        assert list(obs.data["value"].values) == expected_values
+
+    def test_single_item_input_raises(self):
+        df = pd.DataFrame(
+            {"value": [1.0, 1.1, 1.2]},
+            index=[pd.Timestamp("2019-01-01")] * 3,
+        )
+
+        with pytest.raises(ValueError, match="at least 2"):
+            ms.VerticalObservation(df, item="value", z_item="z", x=12.0, y=55.0)
+
+    def test_more_than_two_items_without_item_raises(self):
+        df = pd.DataFrame(
+            {
+                "z": [-5.0, -4.0, -3.0],
+                "value1": [1.0, 1.1, 1.2],
+                "value2": [2.0, 2.1, 2.2],
+            },
+            index=[pd.Timestamp("2019-01-01")] * 3,
+        )
+
+        with pytest.raises(ValueError, match="item was not given"):
+            ms.VerticalObservation(df, z_item="z", x=12.0, y=55.0)
+
+    def test_duplicate_item_specification_raises(self, _vertical_df_aux):
+        with pytest.raises(ValueError, match="Duplicate items"):
+            ms.VerticalObservation(
+                _vertical_df_aux,
+                item="value",
+                z_item="z",
+                x=12.0,
+                y=55.0,
+                aux_items=["value"],
+            )
+
+    @pytest.mark.parametrize(
+        "aux_items,expected_aux",
+        [
+            ("aux1", ["aux1"]),
+            (["aux1", "aux2"], ["aux1", "aux2"]),
+        ],
+    )
+    def test_aux_items_preserved_and_tagged(
+        self, _vertical_df_aux, aux_items, expected_aux
+    ):
+        obs = ms.VerticalObservation(
+            _vertical_df_aux,
+            item="value",
+            z_item="z",
+            x=12.0,
+            y=55.0,
+            aux_items=aux_items,
+        )
+
+        for aux_name in expected_aux:
+            assert aux_name in obs.data.data_vars
+            assert obs.data[aux_name].attrs["kind"] == "aux"
+
+        df = obs.to_dataframe()
+        for aux_name in expected_aux:
+            assert aux_name in df.columns
+
+    def test_roundtrip_from_dataset_preserves_vertical_observation(self, _vertical_df):
+        obs = ms.VerticalObservation(
+            _vertical_df,
+            item="value",
+            z_item="z",
+            x=12.0,
+            y=55.0,
+            attrs={"station": "A"},
+        )
+        obs2 = ms.VerticalObservation(obs.data)
+
+        assert obs.equals(obs2)
+        assert obs2.attrs["gtype"] == obs.attrs["gtype"]
+        assert obs2.attrs["station"] == "A"
+        assert obs2.name == obs.name
+
+    def test_to_dataframe(self, _vertical_df):
+        obs = ms.VerticalObservation(
+            _vertical_df,
+            item="value",
+            z_item="z",
+            x=12.0,
+            y=55.0,
+        )
+        df = obs.to_dataframe()
+        assert isinstance(df, pd.DataFrame)
+        assert list(df.columns) == ["z", "value"]
diff --git a/tests/test_match.py b/tests/test_match.py
index 54b8d7a23..0aab06c79 100644
--- a/tests/test_match.py
+++ b/tests/test_match.py
@@ -37,6 +37,12 @@ def o3():
     return ms.TrackObservation(fn, item=3, name="c2")
 
 
+@pytest.fixture
+def o4():
+    fn = "tests/testdata/vertical/VerticalProfile_obs1.dfs0"
+    return ms.VerticalObservation(fn, z_item="z", name="vobs", x=657500, y=6553600)
+
+
 @pytest.fixture
 def mr12_gaps():
     fn = "tests/testdata/SW/ts_storm_4.dfs0"
@@ -71,6 +77,226 @@ def mr3():
     return ms.model_result(fn, item=0, name="SW_3")
 
 
+@pytest.fixture
+def mr4():
+    fn = "tests/testdata/vertical/VerticalModel_at_obs.dfs0"
+    return ms.model_result(fn, item="Salinity", name="vmod", gtype="vertical")
+
+
+@pytest.fixture
+def mr5():
+    fn = "tests/testdata/vertical/sigma_z_coast.dfsu"
+    return ms.model_result(fn, item="Salinity", name="3dmod")
+
+
+@pytest.fixture
+def simple_vo():
+    ind = pd.DatetimeIndex(["2020-01-01 13:00:00"] * 2 + ["2020-01-02 11:00:00"] * 2)
+    data = {
+        "v": [1, 2, 1.1, 2.1],
+        "z": [-1, -2, -1, -2],
+        "x": [20, 20, 20, 20],
+        "y": [55, 55, 55, 55],
+    }
+    vo = ms.VerticalObservation(
+        pd.DataFrame(data, index=ind), z_item="z", item="v", name="obs", x=20, y=55
+    )
+    return vo
+
+
+@pytest.fixture
+def simple_vm():
+    ind = pd.DatetimeIndex(["2020-01-01 12:00:00"] * 4 + ["2020-01-02 12:00:00"] * 4)
+    data = {
+        "mod": [1.1, 2.1, 3.1, 4.1, 1.2, 2.2, 3.2, 4.2],
+        "z": [-1, -2, -3, -4, -1, -2, -3, -4],
+    }
+    vm = ms.VerticalModelResult(pd.DataFrame(data, index=ind), z_item=1, item=0)
+    return vm
+
+
+class TestVerticalObservation:
+    # ============
+    # Check variations of match with vertical data
+    # ============
+    def test_match_dfs0_dfs0(self, o4, mr4):
+        cmp = ms.match(o4, mr4)
+        assert cmp.n_models == 1
+        assert cmp.n_points > 0
+        assert cmp.x == pytest.approx(657500)
+        assert cmp.y == pytest.approx(6553600)
+        assert cmp.z is not None
+        assert cmp.name == "vobs"
+        assert cmp.gtype == "vertical"
+        assert cmp.mod_names == ["vmod"]
+        assert cmp.data["vmod"].attrs["kind"] == "model"
+        assert cmp.data["Observation"].attrs["kind"] == "observation"
+
+    def test_match_dfsu_dfs0(self, o4, mr5):
+        cmp = ms.match(o4, mr5)
+        assert cmp.n_models == 1
+        assert cmp.n_points > 0
+        assert cmp.x == pytest.approx(657500)
+        assert cmp.y == pytest.approx(6553600)
+        assert cmp.z is not None
+        assert cmp.name == "vobs"
+        assert cmp.gtype == "vertical"
+        assert cmp.mod_names == ["3dmod"]
+        assert cmp.data["3dmod"].attrs["kind"] == "model"
+        assert cmp.data["Observation"].attrs["kind"] == "observation"
+
+    def test_match_multiple(self, o4, mr4, mr5):
+        cmp = ms.match(o4, [mr4, mr5])
+        assert cmp.n_models == 2
+        assert cmp.x == pytest.approx(657500)
+        assert cmp.y == pytest.approx(6553600)
+        assert cmp.z is not None
+        assert cmp.name == "vobs"
+        assert cmp.gtype == "vertical"
+        assert cmp.mod_names == ["vmod", "3dmod"]
+        assert cmp.data["vmod"].attrs["kind"] == "model"
+        assert cmp.data["3dmod"].attrs["kind"] == "model"
+        assert cmp.data["Observation"].attrs["kind"] == "observation"
+
+    def test_failing_z_and_z_item_set(self, simple_vo, simple_vm):
+        cmp = ms.match(simple_vo, simple_vm)
+        with pytest.raises(ValueError, match="z and z_item"):
+            ms.from_matched(cmp.to_dataframe(), z_item="z", z=cmp.z)
+
+    # ==========
+    # Test from_matched
+    # ==========
+    def test_round_trip_from_matched_df(self, o4, mr4):
+        cmp = ms.match(o4, mr4)
+        cmp2 = ms.from_matched(cmp.to_dataframe(), x=cmp.x, y=cmp.y)
+        assert cmp2.gtype == "vertical"
+        assert cmp2.n_models == 1
+        assert cmp2.n_points == cmp.n_points
+        assert cmp2.x == cmp.x  # set in from_matched
+        assert cmp2.y == cmp.y  # set in from_matched
+        assert np.array_equal(cmp2.z, cmp.z)
+        assert cmp2.name == cmp._obs_name  # not set, defaults to obs name
+        assert cmp2.mod_names == cmp.mod_names
+
+    def test_round_trip_from_matched_dfs0(self, simple_vo, simple_vm):
+        cmp = ms.match(simple_vo, simple_vm)
+        # create df0 from matched data and create comparer from that again
+        ds = mikeio.from_pandas(cmp.to_dataframe())
+        cmp2 = ms.from_matched(ds)
+        assert cmp2.gtype == cmp.gtype == "vertical"
+        assert cmp2.n_models == 1
+        assert cmp2.n_points == cmp.n_points
+        assert np.isnan(cmp2.x)  # x and y not propagate with dataframe
+        assert np.isnan(cmp2.y)  # x and y not propagate with dataframe
+        assert np.array_equal(cmp2.z, cmp.z)
+        assert cmp2.name == "Observation"
+        assert cmp2.gtype == cmp.gtype
+        assert cmp2.mod_names == cmp.mod_names
+
+    def test_round_trip_from_matched_xr(self, simple_vo, simple_vm):
+        cmp = ms.match(simple_vo, simple_vm)
+        cmp2 = ms.from_matched(cmp.data)
+
+        assert cmp2.gtype == cmp.gtype == "vertical"
+        assert cmp2.n_models == 1
+        assert cmp2.n_points == cmp.n_points
+        assert cmp2.x == cmp.x  # propagated
+        assert cmp2.y == cmp.y  # propagated
+        assert np.array_equal(cmp2.z, cmp.z)
+        assert cmp2.name == cmp.name
+        assert cmp2.gtype == cmp.gtype
+        assert cmp2.mod_names == cmp.mod_names
+
+    # ==========
+    # Test correct results
+    # ==========
+    def test_align_same_negative_depth(self, simple_vo, simple_vm):
+        vo = simple_vo
+        vm = simple_vm
+        cmp = ms.match(vo, vm)
+        expected_mod_values = [1.1, 2.1, 1.2, 2.2]
+
+        assert cmp.data["mod"].to_numpy() == pytest.approx(expected_mod_values)
+        assert cmp.data["z"].to_numpy() == pytest.approx(simple_vo.data["z"].to_numpy())
+
+    def test_align_unevently_sampled_obs(self, simple_vo, simple_vm):
+        # drop the last point and modify last obs depth to -4 and create new VerticalObservation
+        df_o = simple_vo.to_dataframe().iloc[0:-1, :]
+        df_o.iloc[-1, 0] = -4
+        vo = ms.VerticalObservation(df_o)
+        cmp = ms.match(vo, simple_vm)
+        expected_mod_values = [1.1, 2.1, 4.2]
+        expected_depths = simple_vo.data["z"].to_numpy()[0:-1]
+        expected_depths[-1] = -4
+
+        assert cmp.data["mod"].to_numpy() == pytest.approx(expected_mod_values)
+        assert cmp.data["z"].to_numpy() == pytest.approx(expected_depths)
+
+    def test_align_vertical_intepolation(self, simple_vm):
+        # Create observations from model df
+        vo = ms.VerticalObservation(simple_vm.to_dataframe())
+        # Modify model to be inbetween the obs depths and create new VerticalModelResult
+        df = simple_vm.to_dataframe()
+        df["z"] = df["z"] - 0.5
+        # INPUT
+        # mod_z = [-1.5, -2.5, -3.5, -4.5, -1.5, -2.5, -3.5, -4.5],
+        # mod_v = [1.1, 2.1, 3.1, 4.1, 1.2, 2.2, 3.2, 4.2]
+        # obs_z = [-1, -2, -3, -4, -1, -2, -3, -4]
+        vm = ms.VerticalModelResult(df)
+        cmp = ms.match(vo, vm)
+
+        # first depth is nan in models becasuse obs outside model domain
+        # obs at -2 is between model depths -1.5 and -2.5, so mod value is = 1.6
+        # ...
+        expected_mod_values = [1.6, 2.6, 3.6, 1.7, 2.7, 3.7]
+        assert cmp.n_points == 6
+        assert cmp.data["mod"].to_numpy() == pytest.approx(expected_mod_values)
+
+    def test_same_results(self, simple_vm):
+        vo = ms.VerticalObservation(simple_vm.to_dataframe())
+        cmp = ms.match(vo, simple_vm)
+        assert cmp.n_points == 8
+        assert cmp.data["mod"].to_numpy() == pytest.approx(
+            simple_vm.data["mod"].to_numpy()
+        )
+        assert cmp.data["z"].to_numpy() == pytest.approx(simple_vm.data["z"].to_numpy())
+        assert cmp.data["Observation"].to_numpy() == pytest.approx(
+            simple_vm.data["mod"].to_numpy()
+        )
+
+    # ==========
+    # Skill test (MAYBE IN SKILL)
+    # ==========
+    # Same models give rmse = 0
+
+    # ==========
+    # Test slicing
+    # ==========
+    def test_slice_vertical(self, simple_vo, simple_vm):
+        cmp = ms.match(simple_vo, simple_vm)
+        cmp_slice = cmp.query("z>=-1.5 & z<=-0.5")
+        assert cmp_slice.n_points < cmp.n_points
+
+    # ==========
+    # Edge-cases
+    # ==========
+    def test_no_overlap_in_z(self, simple_vo, simple_vm):
+        # shift model to be outside obs range
+        df = simple_vm.to_dataframe()
+        df["z"] = df["z"] - 2
+        vm = ms.VerticalModelResult(df)
+        cmp = ms.match(simple_vo, vm)
+        assert cmp.n_points == 0
+
+    def test_only_1_model_depth_overlap(self, simple_vo, simple_vm):
+        # shift model to be outside obs range except for one point
+        df = simple_vm.to_dataframe()
+        df["z"] = df["z"] - 1  # only match with models at z=-2 exact
+        vm = ms.VerticalModelResult(df)
+        cmp = ms.match(simple_vo, vm)
+        assert cmp.n_points == 2
+
+
 def test_properties_after_match(o1, mr1):
     cmp = ms.match(o1, mr1)
     assert cmp.n_models == 1
diff --git a/tests/test_timeseries.py b/tests/test_timeseries.py
index d362a2943..a672a42be 100644
--- a/tests/test_timeseries.py
+++ b/tests/test_timeseries.py
@@ -4,6 +4,7 @@
 import xarray as xr
 import modelskill as ms
 from modelskill.timeseries import TimeSeries
+from modelskill.timeseries._timeseries import _validate_dataset
 from modelskill.types import GeometryType
 
 
@@ -41,6 +42,24 @@ def ds_track():
     return ds
 
 
+@pytest.fixture
+def ds_vertical():
+    x = 0
+    y = 3
+    z = [0, -5, -10]
+    time = pd.date_range("2000-01-01", periods=3)
+    data = np.random.rand(3)
+    ds = xr.Dataset(
+        {"dataitem": (["time"], data)},
+        coords={"time": time, "z": (["time"], z)},
+    )
+    ds.coords["x"] = x
+    ds.coords["y"] = y
+    ds.attrs["gtype"] = str(GeometryType.VERTICAL)
+    ds["dataitem"].attrs["kind"] = "observation"
+    return ds
+
+
 def test_timeseries_point_init(ds_point):
     # test that TimeSeries can be initialized from xr.Dataset
     ts = TimeSeries(ds_point)
@@ -89,6 +108,20 @@ def test_timeseries_validation_fails_xy(ds_point):
         TimeSeries(ds_without_y)
 
 
+def test_validate_dataset_vertical_with_z_coord(ds_vertical):
+    ds = _validate_dataset(ds_vertical)
+    assert ds.attrs["gtype"] == str(GeometryType.VERTICAL)
+    assert "z" in ds.coords
+    assert tuple(ds["z"].dims) == ("time",)
+    assert tuple(ds["z"].values) == (0, -5, -10)
+
+
+def test_validate_dataset_vertical_without_z_coord_raises(ds_vertical):
+    ds_without_z = ds_vertical.drop_vars("z")
+    with pytest.raises(ValueError, match="vertical.*z-coordinate"):
+        _validate_dataset(ds_without_z)
+
+
 def test_timeseries_point_properties(ds_point):
     ts = TimeSeries(ds_point)
     assert ts.name == "dataitem"
diff --git a/tests/testdata/vertical/VerticalModel_at_obs.dfs0 b/tests/testdata/vertical/VerticalModel_at_obs.dfs0
new file mode 100644
index 000000000..521adbe82
Binary files /dev/null and b/tests/testdata/vertical/VerticalModel_at_obs.dfs0 differ
diff --git a/tests/testdata/vertical/VerticalProfile_ST.dfs0 b/tests/testdata/vertical/VerticalProfile_ST.dfs0
new file mode 100644
index 000000000..7b34762e1
Binary files /dev/null and b/tests/testdata/vertical/VerticalProfile_ST.dfs0 differ
diff --git a/tests/testdata/vertical/VerticalProfile_obs1.csv b/tests/testdata/vertical/VerticalProfile_obs1.csv
new file mode 100644
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diff --git a/tests/testdata/vertical/VerticalProfile_obs2.dfs0 b/tests/testdata/vertical/VerticalProfile_obs2.dfs0
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diff --git a/tests/testdata/vertical/sigma_z_coast.dfsu b/tests/testdata/vertical/sigma_z_coast.dfsu
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