diff --git a/book/intro.md b/book/intro.md
index 907d109..9601099 100644
--- a/book/intro.md
+++ b/book/intro.md
@@ -2,6 +2,8 @@
 
 ## Neural network models for Chloraphyll-a gap-filling for remote-sensing products
 
+### Authors: See individual notebooks
+
 2024 GeoSMART Hackweek:
 
 [Pitch slide](https://docs.google.com/presentation/d/1YfBLkspba2hRz5pTHG9OF3o9WHv-yNemZDq2QKFCme0/edit?usp=sharing)
diff --git a/book/notebooks/CHL_prediction_CNN.ipynb b/book/notebooks/CHL_prediction_CNN.ipynb
new file mode 100644
index 0000000..cd8537e
--- /dev/null
+++ b/book/notebooks/CHL_prediction_CNN.ipynb
@@ -0,0 +1,3041 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr\n",
+    "import numpy as np\n",
+    "\n",
+    "import dask.array as da\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import tensorflow as tf\n",
+    "from tensorflow.keras.callbacks import EarlyStopping\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import ConvLSTM2D, BatchNormalization, Conv2D, Dropout"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "All Physical Devices: [PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'), PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n",
+      "Available GPUs: [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n",
+      "GPU Details: {'device_name': 'NVIDIA GeForce RTX 4070 Laptop GPU', 'compute_capability': (8, 9)}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# list all the physical devices\n",
+    "physical_devices = tf.config.list_physical_devices()\n",
+    "print(\"All Physical Devices:\", physical_devices)\n",
+    "\n",
+    "# list all the available GPUs\n",
+    "gpus = tf.config.list_physical_devices('GPU')\n",
+    "print(\"Available GPUs:\", gpus)\n",
+    "\n",
+    "# Print infomation for available GPU if there exists any\n",
+    "if gpus:\n",
+    "    for gpu in gpus:\n",
+    "        details = tf.config.experimental.get_device_details(gpu)\n",
+    "        print(\"GPU Details:\", details)\n",
+    "else:\n",
+    "    print(\"No GPU available\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 711MB\n",
+       "Dimensions:          (time: 366, lat: 149, lon: 181)\n",
+       "Coordinates:\n",
+       "  * lat              (lat) float32 596B 32.0 31.75 31.5 ... -4.5 -4.75 -5.0\n",
+       "  * lon              (lon) float32 724B 45.0 45.25 45.5 ... 89.5 89.75 90.0\n",
+       "  * time             (time) datetime64[ns] 3kB 2020-01-01 ... 2020-12-31\n",
+       "Data variables: (12/19)\n",
+       "    CHL              (time, lat, lon) float32 39MB dask.array&lt;chunksize=(25, 149, 181), meta=np.ndarray&gt;\n",
+       "    CHL_uncertainty  (time, lat, lon) float32 39MB dask.array&lt;chunksize=(25, 149, 181), meta=np.ndarray&gt;\n",
+       "    adt              (time, lat, lon) float32 39MB dask.array&lt;chunksize=(25, 149, 181), meta=np.ndarray&gt;\n",
+       "    air_temp         (time, lat, lon) float32 39MB dask.array&lt;chunksize=(25, 149, 181), meta=np.ndarray&gt;\n",
+       "    curr_dir         (time, lat, lon) float32 39MB dask.array&lt;chunksize=(25, 149, 181), meta=np.ndarray&gt;\n",
+       "    curr_speed       (time, lat, lon) float32 39MB dask.array&lt;chunksize=(25, 149, 181), meta=np.ndarray&gt;\n",
+       "    ...               ...\n",
+       "    ug_curr          (time, lat, lon) float32 39MB dask.array&lt;chunksize=(25, 149, 181), meta=np.ndarray&gt;\n",
+       "    v_curr           (time, lat, lon) float32 39MB dask.array&lt;chunksize=(25, 149, 181), meta=np.ndarray&gt;\n",
+       "    v_wind           (time, lat, lon) float32 39MB dask.array&lt;chunksize=(25, 149, 181), meta=np.ndarray&gt;\n",
+       "    vg_curr          (time, lat, lon) float32 39MB dask.array&lt;chunksize=(25, 149, 181), meta=np.ndarray&gt;\n",
+       "    wind_dir         (time, lat, lon) float32 39MB dask.array&lt;chunksize=(25, 149, 181), meta=np.ndarray&gt;\n",
+       "    wind_speed       (time, lat, lon) float32 39MB dask.array&lt;chunksize=(25, 149, 181), meta=np.ndarray&gt;\n",
+       "Attributes: (12/17)\n",
+       "    creator_email:              minhphan@uw.edu\n",
+       "    creator_name:               Minh Phan\n",
+       "    creator_type:               person\n",
+       "    date_created:               2023-07-19\n",
+       "    geospatial_lat_max:         32.0\n",
+       "    geospatial_lat_min:         -12.0\n",
+       "    ...                         ...\n",
+       "    geospatial_lon_units:       degrees_east\n",
+       "    source:                     Earth &amp; Space Research (ESR), Copernicus Clim...\n",
+       "    summary:                    Daily mean of 0.25 x 0.25 degrees gridded dat...\n",
+       "    time_coverage_end:          2022-12-31T23:59:59\n",
+       "    time_coverage_start:        1979-01-01T00:00:00\n",
+       "    title:                      Climate Data for Coastal Upwelling Machine Le...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-62aa598e-e572-494f-83eb-2bb6124e373d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-62aa598e-e572-494f-83eb-2bb6124e373d' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 366</li><li><span class='xr-has-index'>lat</span>: 149</li><li><span class='xr-has-index'>lon</span>: 181</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-59490caf-214e-440f-a217-1af0c51f89c2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-59490caf-214e-440f-a217-1af0c51f89c2' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>32.0 31.75 31.5 ... -4.5 -4.75 -5.0</div><input id='attrs-b9e6fb88-cbaf-427e-a1bb-d5e879057380' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b9e6fb88-cbaf-427e-a1bb-d5e879057380' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25c95536-2a26-4517-98e7-32d489195ec8' class='xr-var-data-in' type='checkbox'><label for='data-25c95536-2a26-4517-98e7-32d489195ec8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([32.  , 31.75, 31.5 , 31.25, 31.  , 30.75, 30.5 , 30.25, 30.  , 29.75,\n",
+       "       29.5 , 29.25, 29.  , 28.75, 28.5 , 28.25, 28.  , 27.75, 27.5 , 27.25,\n",
+       "       27.  , 26.75, 26.5 , 26.25, 26.  , 25.75, 25.5 , 25.25, 25.  , 24.75,\n",
+       "       24.5 , 24.25, 24.  , 23.75, 23.5 , 23.25, 23.  , 22.75, 22.5 , 22.25,\n",
+       "       22.  , 21.75, 21.5 , 21.25, 21.  , 20.75, 20.5 , 20.25, 20.  , 19.75,\n",
+       "       19.5 , 19.25, 19.  , 18.75, 18.5 , 18.25, 18.  , 17.75, 17.5 , 17.25,\n",
+       "       17.  , 16.75, 16.5 , 16.25, 16.  , 15.75, 15.5 , 15.25, 15.  , 14.75,\n",
+       "       14.5 , 14.25, 14.  , 13.75, 13.5 , 13.25, 13.  , 12.75, 12.5 , 12.25,\n",
+       "       12.  , 11.75, 11.5 , 11.25, 11.  , 10.75, 10.5 , 10.25, 10.  ,  9.75,\n",
+       "        9.5 ,  9.25,  9.  ,  8.75,  8.5 ,  8.25,  8.  ,  7.75,  7.5 ,  7.25,\n",
+       "        7.  ,  6.75,  6.5 ,  6.25,  6.  ,  5.75,  5.5 ,  5.25,  5.  ,  4.75,\n",
+       "        4.5 ,  4.25,  4.  ,  3.75,  3.5 ,  3.25,  3.  ,  2.75,  2.5 ,  2.25,\n",
+       "        2.  ,  1.75,  1.5 ,  1.25,  1.  ,  0.75,  0.5 ,  0.25,  0.  , -0.25,\n",
+       "       -0.5 , -0.75, -1.  , -1.25, -1.5 , -1.75, -2.  , -2.25, -2.5 , -2.75,\n",
+       "       -3.  , -3.25, -3.5 , -3.75, -4.  , -4.25, -4.5 , -4.75, -5.  ],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>45.0 45.25 45.5 ... 89.5 89.75 90.0</div><input id='attrs-8a71016c-ca30-488e-8924-dc11e07a7e55' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a71016c-ca30-488e-8924-dc11e07a7e55' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d2eddb8-33e4-4337-b704-9d4af78e0fc5' class='xr-var-data-in' type='checkbox'><label for='data-1d2eddb8-33e4-4337-b704-9d4af78e0fc5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([45.  , 45.25, 45.5 , 45.75, 46.  , 46.25, 46.5 , 46.75, 47.  , 47.25,\n",
+       "       47.5 , 47.75, 48.  , 48.25, 48.5 , 48.75, 49.  , 49.25, 49.5 , 49.75,\n",
+       "       50.  , 50.25, 50.5 , 50.75, 51.  , 51.25, 51.5 , 51.75, 52.  , 52.25,\n",
+       "       52.5 , 52.75, 53.  , 53.25, 53.5 , 53.75, 54.  , 54.25, 54.5 , 54.75,\n",
+       "       55.  , 55.25, 55.5 , 55.75, 56.  , 56.25, 56.5 , 56.75, 57.  , 57.25,\n",
+       "       57.5 , 57.75, 58.  , 58.25, 58.5 , 58.75, 59.  , 59.25, 59.5 , 59.75,\n",
+       "       60.  , 60.25, 60.5 , 60.75, 61.  , 61.25, 61.5 , 61.75, 62.  , 62.25,\n",
+       "       62.5 , 62.75, 63.  , 63.25, 63.5 , 63.75, 64.  , 64.25, 64.5 , 64.75,\n",
+       "       65.  , 65.25, 65.5 , 65.75, 66.  , 66.25, 66.5 , 66.75, 67.  , 67.25,\n",
+       "       67.5 , 67.75, 68.  , 68.25, 68.5 , 68.75, 69.  , 69.25, 69.5 , 69.75,\n",
+       "       70.  , 70.25, 70.5 , 70.75, 71.  , 71.25, 71.5 , 71.75, 72.  , 72.25,\n",
+       "       72.5 , 72.75, 73.  , 73.25, 73.5 , 73.75, 74.  , 74.25, 74.5 , 74.75,\n",
+       "       75.  , 75.25, 75.5 , 75.75, 76.  , 76.25, 76.5 , 76.75, 77.  , 77.25,\n",
+       "       77.5 , 77.75, 78.  , 78.25, 78.5 , 78.75, 79.  , 79.25, 79.5 , 79.75,\n",
+       "       80.  , 80.25, 80.5 , 80.75, 81.  , 81.25, 81.5 , 81.75, 82.  , 82.25,\n",
+       "       82.5 , 82.75, 83.  , 83.25, 83.5 , 83.75, 84.  , 84.25, 84.5 , 84.75,\n",
+       "       85.  , 85.25, 85.5 , 85.75, 86.  , 86.25, 86.5 , 86.75, 87.  , 87.25,\n",
+       "       87.5 , 87.75, 88.  , 88.25, 88.5 , 88.75, 89.  , 89.25, 89.5 , 89.75,\n",
+       "       90.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-01-01 ... 2020-12-31</div><input id='attrs-4431f0cb-05fa-4811-97a8-ba4690bbd229' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4431f0cb-05fa-4811-97a8-ba4690bbd229' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec325bc6-85df-4981-8fe7-366104ce08c6' class='xr-var-data-in' type='checkbox'><label for='data-ec325bc6-85df-4981-8fe7-366104ce08c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>comment :</span></dt><dd>Data is averaged over the day</dd><dt><span>long_name :</span></dt><dd>time centered on the day</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_bounds :</span></dt><dd>2000-01-01 00:00:00 to 2000-01-01 23:59:59</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-01-01T00:00:00.000000000&#x27;, &#x27;2020-01-02T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-03T00:00:00.000000000&#x27;, ..., &#x27;2020-12-29T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-12-30T00:00:00.000000000&#x27;, &#x27;2020-12-31T00:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c9f98f68-fcc5-4118-84ca-3737d33eecec' class='xr-section-summary-in' type='checkbox'  ><label for='section-c9f98f68-fcc5-4118-84ca-3737d33eecec' class='xr-section-summary' >Data variables: <span>(19)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>CHL</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25, 149, 181), meta=np.ndarray&gt;</div><input id='attrs-e06c1d1c-32bc-46cb-a1a0-559d5bf7030b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e06c1d1c-32bc-46cb-a1a0-559d5bf7030b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0662669-3f8f-4731-9b21-cc60b9d24a88' class='xr-var-data-in' type='checkbox'><label for='data-b0662669-3f8f-4731-9b21-cc60b9d24a88' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>[1, 256, 256]</dd><dt><span>ancillary_variables :</span></dt><dd>flags CHL_uncertainty</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>input_files_reprocessings :</span></dt><dd>Processors versions: MODIS R2022.0NRT/VIIRSN R2022.0NRT/OLCIA 07.02/VIIRSJ1 R2022.0NRT/OLCIB 07.02</dd><dt><span>long_name :</span></dt><dd>Chlorophyll-a concentration - Mean of the binned pixels</dd><dt><span>standard_name :</span></dt><dd>mass_concentration_of_chlorophyll_a_in_sea_water</dd><dt><span>type :</span></dt><dd>surface</dd><dt><span>units :</span></dt><dd>milligram m-3</dd><dt><span>valid_max :</span></dt><dd>1000.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 37.65 MiB </td>\n",
+       "                        <td> 10.29 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (366, 149, 181) </td>\n",
+       "                        <td> (100, 149, 181) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 5 chunks in 6 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CHL_uncertainty</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25, 149, 181), meta=np.ndarray&gt;</div><input id='attrs-e23a84e9-3fcf-4eac-81b1-149d386b7c2b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e23a84e9-3fcf-4eac-81b1-149d386b7c2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8b0cc78-b9cc-4fb9-9154-ead8e260b8fc' class='xr-var-data-in' type='checkbox'><label for='data-c8b0cc78-b9cc-4fb9-9154-ead8e260b8fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>[1, 256, 256]</dd><dt><span>coverage_content_type :</span></dt><dd>qualityInformation</dd><dt><span>long_name :</span></dt><dd>Chlorophyll-a concentration - Uncertainty estimation</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>valid_max :</span></dt><dd>32767</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
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+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 37.65 MiB </td>\n",
+       "                        <td> 10.29 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (366, 149, 181) </td>\n",
+       "                        <td> (100, 149, 181) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 5 chunks in 6 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>adt</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25, 149, 181), meta=np.ndarray&gt;</div><input id='attrs-653ecd7a-73bc-4fcc-8e09-be8a393ed921' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-653ecd7a-73bc-4fcc-8e09-be8a393ed921' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55c7fbb5-d8dd-4a0e-94d7-2126cc54f60c' class='xr-var-data-in' type='checkbox'><label for='data-55c7fbb5-d8dd-4a0e-94d7-2126cc54f60c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>The absolute dynamic topography is the sea surface height above geoid; the adt is obtained as follows: adt=sla+mdt where mdt is the mean dynamic topography; see the product user manual for details</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>Absolute dynamic topography</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_geoid</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 37.65 MiB </td>\n",
+       "                        <td> 10.29 MiB </td>\n",
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+       "                        <td> (100, 149, 181) </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 5 chunks in 6 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>air_temp</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25, 149, 181), meta=np.ndarray&gt;</div><input id='attrs-e1ea4c65-1a50-437b-8e43-1e1bb3d022d9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e1ea4c65-1a50-437b-8e43-1e1bb3d022d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-339b0cd0-64df-4566-87e0-c779e127bf65' class='xr-var-data-in' type='checkbox'><label for='data-339b0cd0-64df-4566-87e0-c779e127bf65' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>2 metre temperature</dd><dt><span>nameCDM :</span></dt><dd>2_metre_temperature_surface</dd><dt><span>nameECMWF :</span></dt><dd>2 metre temperature</dd><dt><span>product_type :</span></dt><dd>analysis</dd><dt><span>shortNameECMWF :</span></dt><dd>2t</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                <tbody>\n",
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+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 37.65 MiB </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 5 chunks in 6 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>curr_dir</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25, 149, 181), meta=np.ndarray&gt;</div><input id='attrs-e6b953d0-be8f-4a0a-9394-1bd0c171cace' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e6b953d0-be8f-4a0a-9394-1bd0c171cace' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-577e862b-681e-4d97-af70-afd0dea6f022' class='xr-var-data-in' type='checkbox'><label for='data-577e862b-681e-4d97-af70-afd0dea6f022' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comments :</span></dt><dd>Computed from total surface current velocity elements. Velocities are an average over the top 30m of the mixed layer</dd><dt><span>depth :</span></dt><dd>15m</dd><dt><span>long_name :</span></dt><dd>average direction of total surface currents</dd><dt><span>units :</span></dt><dd>degrees</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>curr_speed</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25, 149, 181), meta=np.ndarray&gt;</div><input id='attrs-a680b3dd-f439-4ac3-9311-36c1709be58b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a680b3dd-f439-4ac3-9311-36c1709be58b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f2fbc65-2461-4a61-bd8d-52e255ccf294' class='xr-var-data-in' type='checkbox'><label for='data-1f2fbc65-2461-4a61-bd8d-52e255ccf294' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comments :</span></dt><dd>Velocities are an average over the top 30m of the mixed layer</dd><dt><span>depth :</span></dt><dd>15m</dd><dt><span>long_name :</span></dt><dd>average total surface current speed</dd><dt><span>units :</span></dt><dd>m s**-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 37.65 MiB </td>\n",
+       "                        <td> 10.29 MiB </td>\n",
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+       "                        <th> Shape </th>\n",
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+       "                        <td> (100, 149, 181) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 5 chunks in 6 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mlotst</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25, 149, 181), meta=np.ndarray&gt;</div><input id='attrs-2c6589be-c691-4bce-b4f4-b72c310b349c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c6589be-c691-4bce-b4f4-b72c310b349c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b521ab8-a7b9-44a3-9a04-e5f8ef0a9b7f' class='xr-var-data-in' type='checkbox'><label for='data-4b521ab8-a7b9-44a3-9a04-e5f8ef0a9b7f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>[1, 681, 1440]</dd><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>long_name :</span></dt><dd>Density ocean mixed layer thickness</dd><dt><span>standard_name :</span></dt><dd>ocean_mixed_layer_thickness_defined_by_sigma_theta</dd><dt><span>unit_long :</span></dt><dd>Meters</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 37.65 MiB </td>\n",
+       "                        <td> 35.08 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (366, 149, 181) </td>\n",
+       "                        <td> (341, 149, 181) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2 chunks in 6 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sla</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25, 149, 181), meta=np.ndarray&gt;</div><input id='attrs-a7e3b393-8f74-40ad-b14e-34dfd208ee85' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7e3b393-8f74-40ad-b14e-34dfd208ee85' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08b9a4e2-460a-4fd4-ab60-8d6ded302493' class='xr-var-data-in' type='checkbox'><label for='data-08b9a4e2-460a-4fd4-ab60-8d6ded302493' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>ancillary_variables :</span></dt><dd>err_sla</dd><dt><span>comment :</span></dt><dd>The sea level anomaly is the sea surface height above mean sea surface; it is referenced to the [1993, 2012] period; see the product user manual for details</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>Sea level anomaly</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_sea_level</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
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+       "                    <tr>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 37.65 MiB </td>\n",
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+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 5 chunks in 6 graph layers </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>so</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25, 149, 181), meta=np.ndarray&gt;</div><input id='attrs-929c82fb-bf0e-49ac-9a50-d9902ccdeed5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-929c82fb-bf0e-49ac-9a50-d9902ccdeed5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18a32b5f-87a4-4ba9-bed0-df7a666388bb' class='xr-var-data-in' type='checkbox'><label for='data-18a32b5f-87a4-4ba9-bed0-df7a666388bb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>[1, 7, 341, 720]</dd><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>long_name :</span></dt><dd>mean sea water salinity at 0.49 metres below ocean surface</dd><dt><span>standard_name :</span></dt><dd>sea_water_salinity</dd><dt><span>unit_long :</span></dt><dd>Practical Salinity Unit</dd><dt><span>units :</span></dt><dd>1e-3</dd><dt><span>valid_max :</span></dt><dd>28336</dd><dt><span>valid_min :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u_wind</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25, 149, 181), meta=np.ndarray&gt;</div><input id='attrs-987df6f1-bd60-46a3-abac-f60162cafc24' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-987df6f1-bd60-46a3-abac-f60162cafc24' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8458291-0d66-46ae-8108-455b816a2f25' class='xr-var-data-in' type='checkbox'><label for='data-c8458291-0d66-46ae-8108-455b816a2f25' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>10 metre U wind component</dd><dt><span>nameCDM :</span></dt><dd>10_metre_U_wind_component_surface</dd><dt><span>nameECMWF :</span></dt><dd>10 metre U wind component</dd><dt><span>product_type :</span></dt><dd>analysis</dd><dt><span>shortNameECMWF :</span></dt><dd>10u</dd><dt><span>standard_name :</span></dt><dd>eastward_wind</dd><dt><span>units :</span></dt><dd>m s**-1</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 37.65 MiB </td>\n",
+       "                        <td> 10.29 MiB </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 5 chunks in 6 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ug_curr</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25, 149, 181), meta=np.ndarray&gt;</div><input id='attrs-9162905e-6efe-4815-9c48-15b079bb3ece' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9162905e-6efe-4815-9c48-15b079bb3ece' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0bfdf621-ce41-4431-8980-ac99df91cb36' class='xr-var-data-in' type='checkbox'><label for='data-0bfdf621-ce41-4431-8980-ac99df91cb36' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Geostrophic velocities calculated from absolute dynamic topography</dd><dt><span>depth :</span></dt><dd>15m</dd><dt><span>long_name :</span></dt><dd>zonal geostrophic surface current</dd><dt><span>source :</span></dt><dd>SSH source: CMEMS SSALTO/DUACS SEALEVEL_GLO_PHY_L4_MY_008_047 DOI: 10.48670/moi-00148</dd><dt><span>standard_name :</span></dt><dd>geostrophic_eastward_sea_water_velocity</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>valid_max :</span></dt><dd>3.0</dd><dt><span>valid_min :</span></dt><dd>-3.0</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 37.65 MiB </td>\n",
+       "                        <td> 10.29 MiB </td>\n",
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+       "                        <th> Shape </th>\n",
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+       "                        <td> (100, 149, 181) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 5 chunks in 6 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_curr</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25, 149, 181), meta=np.ndarray&gt;</div><input id='attrs-cf338c8d-5cac-4387-80fc-ac6cf96f6509' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf338c8d-5cac-4387-80fc-ac6cf96f6509' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd2b8027-4e1a-4e9b-af10-17705bcdc644' class='xr-var-data-in' type='checkbox'><label for='data-cd2b8027-4e1a-4e9b-af10-17705bcdc644' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Velocities are an average over the top 30m of the mixed layer</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>depth :</span></dt><dd>15m</dd><dt><span>long_name :</span></dt><dd>meridional total surface current</dd><dt><span>source :</span></dt><dd>SSH source: CMEMS SSALTO/DUACS SEALEVEL_GLO_PHY_L4_MY_008_047 DOI: 10.48670/moi-00148 ; WIND source: ECMWF ERA5 10m wind DOI: 10.24381/cds.adbb2d47 ; SST source: CMC 0.2 deg SST V2.0 DOI: 10.5067/GHCMC-4FM02</dd><dt><span>standard_name :</span></dt><dd>northward_sea_water_velocity</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>valid_max :</span></dt><dd>3.0</dd><dt><span>valid_min :</span></dt><dd>-3.0</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 37.65 MiB </td>\n",
+       "                        <td> 10.29 MiB </td>\n",
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+       "                        <th> Bytes </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vg_curr</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25, 149, 181), meta=np.ndarray&gt;</div><input id='attrs-b2b045f5-a451-43cb-a2f7-92cb607ae7ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b2b045f5-a451-43cb-a2f7-92cb607ae7ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c5c336b-b5a6-488b-a3ec-361239aff458' class='xr-var-data-in' type='checkbox'><label for='data-8c5c336b-b5a6-488b-a3ec-361239aff458' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Geostrophic velocities calculated from absolute dynamic topography</dd><dt><span>depth :</span></dt><dd>15m</dd><dt><span>long_name :</span></dt><dd>meridional geostrophic surface current</dd><dt><span>source :</span></dt><dd>SSH source: CMEMS SSALTO/DUACS SEALEVEL_GLO_PHY_L4_MY_008_047 DOI: 10.48670/moi-00148</dd><dt><span>standard_name :</span></dt><dd>geostrophic_northward_sea_water_velocity</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>valid_max :</span></dt><dd>3.0</dd><dt><span>valid_min :</span></dt><dd>-3.0</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 37.65 MiB </td>\n",
+       "                        <td> 10.29 MiB </td>\n",
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+       "                        <th> Shape </th>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 5 chunks in 6 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind_dir</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25, 149, 181), meta=np.ndarray&gt;</div><input id='attrs-1f3cf15d-c817-4243-867f-ec7fe75f7c65' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1f3cf15d-c817-4243-867f-ec7fe75f7c65' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c00618c-97a1-4341-b0d6-f7b179711eab' class='xr-var-data-in' type='checkbox'><label for='data-9c00618c-97a1-4341-b0d6-f7b179711eab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>10 metre wind direction</dd><dt><span>units :</span></dt><dd>degrees</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                        <th> Data type </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind_speed</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25, 149, 181), meta=np.ndarray&gt;</div><input id='attrs-18a741bc-8982-47ca-b0ab-42fa6213269c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-18a741bc-8982-47ca-b0ab-42fa6213269c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90e6c196-b837-4c98-a3bf-8bd4de8fbc81' class='xr-var-data-in' type='checkbox'><label for='data-90e6c196-b837-4c98-a3bf-8bd4de8fbc81' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>10 metre absolute speed</dd><dt><span>units :</span></dt><dd>m s**-1</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                        <th> Shape </th>\n",
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+       "                        <td> (100, 149, 181) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 5 chunks in 6 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-a121309b-df1e-4b9d-be5b-c52d132bf622' class='xr-section-summary-in' type='checkbox'  ><label for='section-a121309b-df1e-4b9d-be5b-c52d132bf622' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-46b5ea28-ac47-49f7-9521-4aa9fd47a421' class='xr-index-data-in' type='checkbox'/><label for='index-46b5ea28-ac47-49f7-9521-4aa9fd47a421' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 32.0, 31.75,  31.5, 31.25,  31.0, 30.75,  30.5, 30.25,  30.0, 29.75,\n",
+       "       ...\n",
+       "       -2.75,  -3.0, -3.25,  -3.5, -3.75,  -4.0, -4.25,  -4.5, -4.75,  -5.0],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=149))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6076579c-2ee8-4bd5-8b5a-aaeb981e9ea8' class='xr-index-data-in' type='checkbox'/><label for='index-6076579c-2ee8-4bd5-8b5a-aaeb981e9ea8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 45.0, 45.25,  45.5, 45.75,  46.0, 46.25,  46.5, 46.75,  47.0, 47.25,\n",
+       "       ...\n",
+       "       87.75,  88.0, 88.25,  88.5, 88.75,  89.0, 89.25,  89.5, 89.75,  90.0],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=181))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-438736a8-468b-447c-a2b2-c709efa64015' class='xr-index-data-in' type='checkbox'/><label for='index-438736a8-468b-447c-a2b2-c709efa64015' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-01-01&#x27;, &#x27;2020-01-02&#x27;, &#x27;2020-01-03&#x27;, &#x27;2020-01-04&#x27;,\n",
+       "               &#x27;2020-01-05&#x27;, &#x27;2020-01-06&#x27;, &#x27;2020-01-07&#x27;, &#x27;2020-01-08&#x27;,\n",
+       "               &#x27;2020-01-09&#x27;, &#x27;2020-01-10&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2020-12-22&#x27;, &#x27;2020-12-23&#x27;, &#x27;2020-12-24&#x27;, &#x27;2020-12-25&#x27;,\n",
+       "               &#x27;2020-12-26&#x27;, &#x27;2020-12-27&#x27;, &#x27;2020-12-28&#x27;, &#x27;2020-12-29&#x27;,\n",
+       "               &#x27;2020-12-30&#x27;, &#x27;2020-12-31&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=366, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-55e93bf3-93d7-451a-839d-8cf3e4489128' class='xr-section-summary-in' type='checkbox'  ><label for='section-55e93bf3-93d7-451a-839d-8cf3e4489128' class='xr-section-summary' >Attributes: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>creator_email :</span></dt><dd>minhphan@uw.edu</dd><dt><span>creator_name :</span></dt><dd>Minh Phan</dd><dt><span>creator_type :</span></dt><dd>person</dd><dt><span>date_created :</span></dt><dd>2023-07-19</dd><dt><span>geospatial_lat_max :</span></dt><dd>32.0</dd><dt><span>geospatial_lat_min :</span></dt><dd>-12.0</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.25</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_max :</span></dt><dd>102.0</dd><dt><span>geospatial_lon_min :</span></dt><dd>42.0</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.25</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>source :</span></dt><dd>Earth &amp; Space Research (ESR), Copernicus Climate Change Service (C3S), Copernicus Marine Environment Monitoring Service (CMEMS), United States Geological Survey (USGS)</dd><dt><span>summary :</span></dt><dd>Daily mean of 0.25 x 0.25 degrees gridded data from multiple climate variables that may influence the patterns of coastal upwelling in the focused area</dd><dt><span>time_coverage_end :</span></dt><dd>2022-12-31T23:59:59</dd><dt><span>time_coverage_start :</span></dt><dd>1979-01-01T00:00:00</dd><dt><span>title :</span></dt><dd>Climate Data for Coastal Upwelling Machine Learning Project in Indian Ocean</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 711MB\n",
+       "Dimensions:          (time: 366, lat: 149, lon: 181)\n",
+       "Coordinates:\n",
+       "  * lat              (lat) float32 596B 32.0 31.75 31.5 ... -4.5 -4.75 -5.0\n",
+       "  * lon              (lon) float32 724B 45.0 45.25 45.5 ... 89.5 89.75 90.0\n",
+       "  * time             (time) datetime64[ns] 3kB 2020-01-01 ... 2020-12-31\n",
+       "Data variables: (12/19)\n",
+       "    CHL              (time, lat, lon) float32 39MB dask.array<chunksize=(25, 149, 181), meta=np.ndarray>\n",
+       "    CHL_uncertainty  (time, lat, lon) float32 39MB dask.array<chunksize=(25, 149, 181), meta=np.ndarray>\n",
+       "    adt              (time, lat, lon) float32 39MB dask.array<chunksize=(25, 149, 181), meta=np.ndarray>\n",
+       "    air_temp         (time, lat, lon) float32 39MB dask.array<chunksize=(25, 149, 181), meta=np.ndarray>\n",
+       "    curr_dir         (time, lat, lon) float32 39MB dask.array<chunksize=(25, 149, 181), meta=np.ndarray>\n",
+       "    curr_speed       (time, lat, lon) float32 39MB dask.array<chunksize=(25, 149, 181), meta=np.ndarray>\n",
+       "    ...               ...\n",
+       "    ug_curr          (time, lat, lon) float32 39MB dask.array<chunksize=(25, 149, 181), meta=np.ndarray>\n",
+       "    v_curr           (time, lat, lon) float32 39MB dask.array<chunksize=(25, 149, 181), meta=np.ndarray>\n",
+       "    v_wind           (time, lat, lon) float32 39MB dask.array<chunksize=(25, 149, 181), meta=np.ndarray>\n",
+       "    vg_curr          (time, lat, lon) float32 39MB dask.array<chunksize=(25, 149, 181), meta=np.ndarray>\n",
+       "    wind_dir         (time, lat, lon) float32 39MB dask.array<chunksize=(25, 149, 181), meta=np.ndarray>\n",
+       "    wind_speed       (time, lat, lon) float32 39MB dask.array<chunksize=(25, 149, 181), meta=np.ndarray>\n",
+       "Attributes: (12/17)\n",
+       "    creator_email:              minhphan@uw.edu\n",
+       "    creator_name:               Minh Phan\n",
+       "    creator_type:               person\n",
+       "    date_created:               2023-07-19\n",
+       "    geospatial_lat_max:         32.0\n",
+       "    geospatial_lat_min:         -12.0\n",
+       "    ...                         ...\n",
+       "    geospatial_lon_units:       degrees_east\n",
+       "    source:                     Earth & Space Research (ESR), Copernicus Clim...\n",
+       "    summary:                    Daily mean of 0.25 x 0.25 degrees gridded dat...\n",
+       "    time_coverage_end:          2022-12-31T23:59:59\n",
+       "    time_coverage_start:        1979-01-01T00:00:00\n",
+       "    title:                      Climate Data for Coastal Upwelling Machine Le..."
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zarr_ds = xr.open_zarr(store=\"./参考资料/2023_INTERN_MATERIAL/INDIAN_OCEAN_025GRID_DAILY.zarr\", consolidated=True)  # get data\n",
+    "\n",
+    "zarr_sliced = zarr_ds.sel(lat=slice(35, -5), lon=slice(45,90))  # choose long and lat\n",
+    "\n",
+    "all_nan_CHL = np.isnan(zarr_sliced.CHL).all(dim=[\"lon\", \"lat\"]).compute()  # find sample indices where CHL is NaN\n",
+    "\n",
+    "zarr_CHL = zarr_sliced.sel(time=(all_nan_CHL == False))  # select samples with CHL not NaN\n",
+    "\n",
+    "zarr_CHL = zarr_CHL.sortby('time')\n",
+    "zarr_CHL = zarr_CHL.sel(time=slice('2020-01-01', '2020-12-31'))\n",
+    "zarr_CHL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "p = zarr_CHL.sel(time='2020-09-02').CHL.plot(y='lat', x='lon')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['1', 'b', 'c']"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# xr.merge((zarr_CHL.CHL, zarr_CHL.sst))\n",
+    "zarr_test = zarr_CHL.copy()\n",
+    "zarr_test['CHL'] = np.log(zarr_CHL.CHL).copy()\n",
+    "# zarr_test\n",
+    "zarr_test.CHL[0].compute() - zarr_CHL.CHL[0].compute()\n",
+    "shape = (100, ) + zarr_test.CHL.shape[1:]\n",
+    "# xr.array(zarr_test.CHL, chunks=shape)\n",
+    "zarr_test.sel(time=slice('2020-01-01', '2020-9-30'))\n",
+    "zarr_test['CHL'] = (('time', 'lat', 'lon'), da.where(da.isnan(zarr_CHL.CHL), 10E-10, zarr_CHL.CHL))\n",
+    "a = ['1', 'b']\n",
+    "a+['c']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;CHL_uncertainty&#x27; ()&gt; Size: 4B\n",
+       "array(127.318726, dtype=float32)</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'CHL_uncertainty'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-7275a690-038e-48c5-8daf-e872d754fc9f' class='xr-array-in' type='checkbox' checked><label for='section-7275a690-038e-48c5-8daf-e872d754fc9f' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>127.3</span></div><div class='xr-array-data'><pre>array(127.318726, dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-68506c42-f47d-4144-b742-90966a22e90c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-68506c42-f47d-4144-b742-90966a22e90c' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-47073ff1-1fae-4dc8-84c0-e5edc4b8cb70' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-47073ff1-1fae-4dc8-84c0-e5edc4b8cb70' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-d727b071-0229-492b-abbb-539ef3e9da1e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d727b071-0229-492b-abbb-539ef3e9da1e' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'CHL_uncertainty' ()> Size: 4B\n",
+       "array(127.318726, dtype=float32)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zarr_CHL.CHL_uncertainty.max().compute()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x1e4a9dc1280>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "np.log(zarr_CHL.sel(time='2020-12-02').CHL).plot(y='lat', x='lon')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def log_label(data, label):\n",
+    "    data_logged = data.copy()\n",
+    "    data_logged[label] = np.log(data[label]).copy()\n",
+    "    return data_logged\n",
+    "\n",
+    "# Add more preprocessing later\n",
+    "def preprocess_data(data, features, label):\n",
+    "    # sel_data = data[features + label]\n",
+    "    # sel_data = da.where(da.isnan(sel_data), 0.0, sel_data)\n",
+    "    data_logged = log_label(data, label)\n",
+    "\n",
+    "    sel_data_list = []\n",
+    "    for var in (features + [label]):\n",
+    "        sel_var_data = data_logged[var]\n",
+    "        sel_var_data = da.where(da.isnan(sel_var_data), 0.0, sel_var_data)\n",
+    "        sel_data_list.append(sel_var_data)\n",
+    "        # print(data[var]).shape\n",
+    "    # sel_data_da = da.from_array(sel_data_arr)\n",
+    "    # sel_data = da.where(da.isnan(sel_data_da), 0.0, sel_data_da)\n",
+    "    sel_data_da =  da.array(sel_data_list)\n",
+    "    # sel_data_da = np.moveaxis(sel_data_da, 0, -1)\n",
+    "    return sel_data_da\n",
+    "\n",
+    "\n",
+    "\n",
+    "def time_series_split(data, split_ratio):\n",
+    "    X = data[:-1]\n",
+    "    y = data[-1]\n",
+    "    \n",
+    "    X = np.moveaxis(X, 0, -1)\n",
+    "\n",
+    "    total_length = X.shape[0]\n",
+    "    \n",
+    "    train_end = int(total_length * split_ratio[0])\n",
+    "    val_end = int(total_length * (split_ratio[0] + split_ratio[1]))\n",
+    "\n",
+    "    X_train, y_train = X[:train_end], y[:train_end]\n",
+    "    X_val, y_val = X[train_end: val_end], y[train_end: val_end]\n",
+    "    X_test, y_test = X[val_end:], y[val_end:]\n",
+    "\n",
+    "    return (X_train, y_train,\n",
+    "            X_val, y_val,\n",
+    "            X_test, y_test)\n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features = ['sst', 'so']  # Curr Features: Sea Surface Temp (K), Sea Salinity Concentration (m**-3 or PSL). [Excluding Topography/Bathymetry (m)]\n",
+    "label = 'CHL'  # Curr Label: : chlorophyll-a concentration (mg/m**3) [Not taking uncertainty into consideration for now]\n",
+    "model_data = preprocess_data(zarr_CHL, features, label)\n",
+    "\n",
+    "split_ratio = [.7, .2, .1]\n",
+    "X_train, y_train, X_val, y_val, X_test, y_test = time_series_split(model_data, split_ratio)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"sequential_1\"\n",
+      "_________________________________________________________________\n",
+      " Layer (type)                Output Shape              Param #   \n",
+      "=================================================================\n",
+      " conv2d_3 (Conv2D)           (None, 149, 181, 64)      1216      \n",
+      "                                                                 \n",
+      " batch_normalization_2 (Batc  (None, 149, 181, 64)     256       \n",
+      " hNormalization)                                                 \n",
+      "                                                                 \n",
+      " dropout_2 (Dropout)         (None, 149, 181, 64)      0         \n",
+      "                                                                 \n",
+      " conv2d_4 (Conv2D)           (None, 149, 181, 32)      18464     \n",
+      "                                                                 \n",
+      " batch_normalization_3 (Batc  (None, 149, 181, 32)     128       \n",
+      " hNormalization)                                                 \n",
+      "                                                                 \n",
+      " dropout_3 (Dropout)         (None, 149, 181, 32)      0         \n",
+      "                                                                 \n",
+      " conv2d_5 (Conv2D)           (None, 149, 181, 1)       289       \n",
+      "                                                                 \n",
+      "=================================================================\n",
+      "Total params: 20,353\n",
+      "Trainable params: 20,161\n",
+      "Non-trainable params: 192\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "def create_model_CNN(input_shape=(149, 181, 2)):\n",
+    "    model = Sequential()\n",
+    "    \n",
+    "    model.add(Conv2D(filters=64, \n",
+    "                     kernel_size=(3, 3), \n",
+    "                     input_shape=input_shape, \n",
+    "                     padding='same',\n",
+    "                     activation='relu'\n",
+    "                     ))\n",
+    "    model.add(BatchNormalization())\n",
+    "    model.add(Dropout(0.2))\n",
+    "\n",
+    "    model.add(Conv2D(filters=32, \n",
+    "                     kernel_size=(3, 3), \n",
+    "                     padding='same',\n",
+    "                     activation='relu'\n",
+    "                     ))\n",
+    "    model.add(BatchNormalization())\n",
+    "    model.add(Dropout(0.2))\n",
+    "\n",
+    "    model.add(Conv2D(filters=1, \n",
+    "                     kernel_size=(3, 3), \n",
+    "                     padding='same', \n",
+    "                     activation='linear'\n",
+    "                     ))\n",
+    "    \n",
+    "    return model\n",
+    "\n",
+    "model = create_model_CNN()\n",
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/50\n",
+      "32/32 [==============================] - 2s 29ms/step - loss: 1.2335 - mae: 1.2335 - val_loss: 3.7761 - val_mae: 3.7761\n",
+      "Epoch 2/50\n",
+      "32/32 [==============================] - 1s 28ms/step - loss: 0.8703 - mae: 0.8703 - val_loss: 2.0273 - val_mae: 2.0273\n",
+      "Epoch 3/50\n",
+      "32/32 [==============================] - 1s 28ms/step - loss: 0.6252 - mae: 0.6252 - val_loss: 0.9374 - val_mae: 0.9374\n",
+      "Epoch 4/50\n",
+      "32/32 [==============================] - 1s 28ms/step - loss: 0.5182 - mae: 0.5182 - val_loss: 0.5586 - val_mae: 0.5586\n",
+      "Epoch 5/50\n",
+      "32/32 [==============================] - 1s 27ms/step - loss: 0.4712 - mae: 0.4712 - val_loss: 0.4161 - val_mae: 0.4161\n",
+      "Epoch 6/50\n",
+      "32/32 [==============================] - 1s 27ms/step - loss: 0.4358 - mae: 0.4358 - val_loss: 0.9170 - val_mae: 0.9170\n",
+      "Epoch 7/50\n",
+      "32/32 [==============================] - 1s 28ms/step - loss: 0.4059 - mae: 0.4059 - val_loss: 0.3857 - val_mae: 0.3857\n",
+      "Epoch 8/50\n",
+      "32/32 [==============================] - 1s 28ms/step - loss: 0.3828 - mae: 0.3828 - val_loss: 0.5535 - val_mae: 0.5535\n",
+      "Epoch 9/50\n",
+      "32/32 [==============================] - 1s 27ms/step - loss: 0.3717 - mae: 0.3717 - val_loss: 0.3228 - val_mae: 0.3228\n",
+      "Epoch 10/50\n",
+      "32/32 [==============================] - 1s 28ms/step - loss: 0.3611 - mae: 0.3611 - val_loss: 0.2872 - val_mae: 0.2872\n",
+      "Epoch 11/50\n",
+      "32/32 [==============================] - 1s 28ms/step - loss: 0.3565 - mae: 0.3565 - val_loss: 0.3834 - val_mae: 0.3834\n",
+      "Epoch 12/50\n",
+      "32/32 [==============================] - 1s 27ms/step - loss: 0.3469 - mae: 0.3469 - val_loss: 0.3106 - val_mae: 0.3106\n",
+      "Epoch 13/50\n",
+      "32/32 [==============================] - 1s 27ms/step - loss: 0.3389 - mae: 0.3389 - val_loss: 0.3001 - val_mae: 0.3001\n",
+      "Epoch 14/50\n",
+      "32/32 [==============================] - 1s 28ms/step - loss: 0.3351 - mae: 0.3351 - val_loss: 0.2925 - val_mae: 0.2925\n",
+      "Epoch 15/50\n",
+      "32/32 [==============================] - 1s 28ms/step - loss: 0.3321 - mae: 0.3321 - val_loss: 0.5618 - val_mae: 0.5618\n",
+      "Epoch 16/50\n",
+      "32/32 [==============================] - 1s 28ms/step - loss: 0.3259 - mae: 0.3259 - val_loss: 0.3511 - val_mae: 0.3511\n",
+      "Epoch 17/50\n",
+      "32/32 [==============================] - 1s 28ms/step - loss: 0.3214 - mae: 0.3214 - val_loss: 0.3055 - val_mae: 0.3055\n",
+      "Epoch 18/50\n",
+      "32/32 [==============================] - 1s 28ms/step - loss: 0.3178 - mae: 0.3178 - val_loss: 0.3272 - val_mae: 0.3272\n",
+      "Epoch 19/50\n",
+      "32/32 [==============================] - 1s 27ms/step - loss: 0.3152 - mae: 0.3152 - val_loss: 0.3728 - val_mae: 0.3728\n",
+      "Epoch 20/50\n",
+      "32/32 [==============================] - 1s 28ms/step - loss: 0.3124 - mae: 0.3124 - val_loss: 0.3400 - val_mae: 0.3400\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.compile(optimizer='adam', loss='mae', metrics=['mae'])\n",
+    "\n",
+    "early_stop = EarlyStopping(patience=10, restore_best_weights=True)\n",
+    "\n",
+    "train_dataset = tf.data.Dataset.from_tensor_slices((X_train, y_train))\n",
+    "train_dataset = train_dataset.shuffle(buffer_size=1024).batch(8)\n",
+    "\n",
+    "val_dataset = tf.data.Dataset.from_tensor_slices((X_val, y_val))\n",
+    "val_dataset = val_dataset.batch(8)\n",
+    "\n",
+    "history = model.fit(train_dataset, epochs=50, validation_data=val_dataset, callbacks=[early_stop])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot training & validation loss values\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(history.history['loss'], label='Train Loss')\n",
+    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
+    "plt.title('Model Loss')\n",
+    "plt.xlabel('Epoch')\n",
+    "plt.ylabel('Loss')\n",
+    "plt.legend(loc='upper right')\n",
+    "plt.grid(True)\n",
+    "plt.show()\n",
+    "\n",
+    "# Plot training & validation MAE values\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(history.history['mae'], label='Train MAE')\n",
+    "plt.plot(history.history['val_mae'], label='Validation MAE')\n",
+    "plt.title('Model Mean Absolute Error (MAE)')\n",
+    "plt.xlabel('Epoch')\n",
+    "plt.ylabel('MAE')\n",
+    "plt.legend(loc='upper right')\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10/10 [==============================] - 0s 4ms/step - loss: 0.2366 - mae: 0.2366\n",
+      "Test Loss: 0.23657584190368652\n",
+      "Test MAE: 0.23657581210136414\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Prepare test dataset\n",
+    "test_dataset = tf.data.Dataset.from_tensor_slices((X_test, y_test))\n",
+    "test_dataset = test_dataset.batch(4)\n",
+    "\n",
+    "# Evaluate the model on the test dataset\n",
+    "test_loss, test_mae = model.evaluate(test_dataset)\n",
+    "print(f\"Test Loss: {test_loss}\")\n",
+    "print(f\"Test MAE: {test_mae}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1/1 [==============================] - 0s 9ms/step\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "date_to_predict = '2020-09-02'\n",
+    "date_index = (np.datetime64(date_to_predict) - np.datetime64('2020-01-01')).item().days\n",
+    "true_output = np.log(zarr_CHL.sel(time=date_to_predict).CHL)\n",
+    "input = np.moveaxis(model_data[:-1], 0, -1)[date_index]\n",
+    "input = np.array(input)\n",
+    "predicted_output = model.predict(input[np.newaxis, ...])[0]\n",
+    "\n",
+    "predicted_output = predicted_output[:,:,0]\n",
+    "\n",
+    "land_mask = np.load(r\"E:\\24SU Varanasi Intern\\参考资料\\2023_Intern_Material\\land_mask_nc.npy\")\n",
+    "predicted_output[land_mask] = np.nan\n",
+    "\n",
+    "# true_output_2 = (model_data[-1])[date_index]\n",
+    "\n",
+    "vmax = np.nanmax((true_output, predicted_output))\n",
+    "vmin = np.nanmin((true_output, predicted_output))\n",
+    "\n",
+    "plt.imshow(true_output, vmin=vmin, vmax=vmax)\n",
+    "plt.colorbar()\n",
+    "plt.title(f'True CHL on {date_to_predict}')\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "plt.imshow(predicted_output, vmin=vmin, vmax=vmax)\n",
+    "plt.colorbar()\n",
+    "plt.title(f'Predicted CHL on {date_to_predict}')\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MAE between Predicted Output and True Output: 0.5261644721031189\n"
+     ]
+    }
+   ],
+   "source": [
+    "def compute_mae(y_true, y_pred):\n",
+    "    mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n",
+    "    return np.mean(np.abs(y_true[mask] - y_pred[mask]))\n",
+    "\n",
+    "\n",
+    "predicted_mae = compute_mae(np.array(true_output), predicted_output)\n",
+    "print(f\"MAE between Predicted Output and True Output: {predicted_mae}\")\n",
+    "\n",
+    "# last_input_frame = input_data[-1]\n",
+    "# last_input_frame_2d = np.squeeze(last_input_frame)\n",
+    "# true_output_2d = np.squeeze(true_output)\n",
+    "# last_frame_mae = compute_mae(true_output_2d, last_input_frame_2d)\n",
+    "# print(f\"MAE between Last Input Frame and True Output: {last_frame_mae}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# (np.datetime64('2020-01-03') - np.datetime64('2020-01-01')) / np.timedelta64(1, 'D')\n",
+    "(np.datetime64('2020-01-03') - np.datetime64('2020-01-01')).item().days"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "tf_2.10_py_3.9",
+   "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": "3.9.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/book/notebooks/CHL_prediction_ConvLSTM_.ipynb b/book/notebooks/CHL_prediction_ConvLSTM_.ipynb
index ab1f27c..2f12393 100644
--- a/book/notebooks/CHL_prediction_ConvLSTM_.ipynb
+++ b/book/notebooks/CHL_prediction_ConvLSTM_.ipynb
@@ -2891,7 +2891,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "tf_2.10_py_3.9",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -2905,9 +2905,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.19"
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/book/notebooks/ipynb-notebook.ipynb b/book/notebooks/ipynb-notebook.ipynb
deleted file mode 100644
index fdb7176..0000000
--- a/book/notebooks/ipynb-notebook.ipynb
+++ /dev/null
@@ -1,122 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Content with notebooks\n",
-    "\n",
-    "You can also create content with Jupyter Notebooks. This means that you can include\n",
-    "code blocks and their outputs in your book.\n",
-    "\n",
-    "## Markdown + notebooks\n",
-    "\n",
-    "As it is markdown, you can embed images, HTML, etc into your posts!\n",
-    "\n",
-    "![](https://myst-parser.readthedocs.io/en/latest/_static/logo-wide.svg)\n",
-    "\n",
-    "You can also $add_{math}$ and\n",
-    "\n",
-    "$$\n",
-    "math^{blocks}\n",
-    "$$\n",
-    "\n",
-    "or\n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned}\n",
-    "\\mbox{mean} la_{tex} \\\\ \\\\\n",
-    "math blocks\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "But make sure you \\$Escape \\$your \\$dollar signs \\$you want to keep!\n",
-    "\n",
-    "## MyST markdown\n",
-    "\n",
-    "MyST markdown works in Jupyter Notebooks as well. For more information about MyST markdown, check\n",
-    "out [the MyST guide in Jupyter Book](https://jupyterbook.org/content/myst.html),\n",
-    "or see [the MyST markdown documentation](https://myst-parser.readthedocs.io/en/latest/).\n",
-    "\n",
-    "## Code blocks and outputs\n",
-    "\n",
-    "Jupyter Book will also embed your code blocks and output in your book.\n",
-    "For example, here's some sample Matplotlib code:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from matplotlib import rcParams, cycler\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "plt.ion()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Fixing random state for reproducibility\n",
-    "np.random.seed(19680801)\n",
-    "\n",
-    "N = 10\n",
-    "data = [np.logspace(0, 1, 100) + np.random.randn(100) + ii for ii in range(N)]\n",
-    "data = np.array(data).T\n",
-    "cmap = plt.cm.coolwarm\n",
-    "rcParams['axes.prop_cycle'] = cycler(color=cmap(np.linspace(0, 1, N)))\n",
-    "\n",
-    "\n",
-    "from matplotlib.lines import Line2D\n",
-    "custom_lines = [Line2D([0], [0], color=cmap(0.), lw=4),\n",
-    "                Line2D([0], [0], color=cmap(.5), lw=4),\n",
-    "                Line2D([0], [0], color=cmap(1.), lw=4)]\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(10, 5))\n",
-    "lines = ax.plot(data)\n",
-    "ax.legend(custom_lines, ['Cold', 'Medium', 'Hot']);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "There is a lot more that you can do with outputs (such as including interactive outputs)\n",
-    "with your book. For more information about this, see [the Jupyter Book documentation](https://jupyterbook.org)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.8.0"
-  },
-  "widgets": {
-   "application/vnd.jupyter.widget-state+json": {
-    "state": {},
-    "version_major": 2,
-    "version_minor": 0
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/book/notebooks/project_result_1.ipynb b/book/notebooks/project_result_1.ipynb
deleted file mode 100644
index e5206af..0000000
--- a/book/notebooks/project_result_1.ipynb
+++ /dev/null
@@ -1,31 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "\n"
-   ]
-  }
- ],
- "metadata": {
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
\ No newline at end of file