diff --git a/environment.yml b/environment.yml
index ec3eb3dd..8c00dce4 100644
--- a/environment.yml
+++ b/environment.yml
@@ -2,6 +2,7 @@ name: nowcasting_dataset
 channels:
   - pvlib
   - conda-forge
+  - fastai
 dependencies:
   - python>=3.9
   - pip
@@ -16,6 +17,7 @@ dependencies:
   - xarray
   - ipykernel
   - h5netcdf # For opening NetCDF files from cloud buckets.
+  - fastai::opencv-python-headless
 
   # Cloud & distributed compute
   - gcsfs
diff --git a/notebooks/plot_optical_flow_batches.ipynb b/notebooks/plot_optical_flow_batches.ipynb
new file mode 100644
index 00000000..280654ee
--- /dev/null
+++ b/notebooks/plot_optical_flow_batches.ipynb
@@ -0,0 +1,19727 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "cb2858bd-479d-43a8-a644-7fc4306f2798",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "import xarray as xr\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1de03cdc-6187-4bae-9b93-f2623c48d1b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "BASE_PATH = Path(\"/mnt/storage_ssd_4tb/data/ocf/solar_pv_nowcasting/nowcasting_dataset_pipeline/prepared_ML_training_data/v15/test\")\n",
+    "SATELLITE_PATH = BASE_PATH / \"satellite\"\n",
+    "OPT_FLOW_PATH = BASE_PATH / \"opticalflow\"\n",
+    "BATCH_FILENAME = \"000170.nc\"\n",
+    "\n",
+    "assert SATELLITE_PATH.exists()\n",
+    "assert OPT_FLOW_PATH.exists()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "baa010e7-fba7-4e03-a427-e52e346a4373",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "satellite_filename = SATELLITE_PATH / BATCH_FILENAME\n",
+    "opt_flow_filename = OPT_FLOW_PATH / BATCH_FILENAME\n",
+    "\n",
+    "assert satellite_filename.exists()\n",
+    "assert opt_flow_filename.exists()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "4fcc6b66-f866-4c90-a07c-7eb953d0613a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sat_batch = xr.load_dataset(satellite_filename, mode=\"r\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7f59a386-f28e-4c56-b60e-2fa05493c3a3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "opt_flow_batch = xr.load_dataset(opt_flow_filename, mode=\"r\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "2ca3b5c0-b638-45c7-9a51-c73e6a8963e5",
+   "metadata": {},
+   "outputs": [
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+       "    time            (example, time_index) datetime64[ns] 2021-03-22T13:20:00 ...\n",
+       "    x               (example, x_index) float64 3.368e+05 3.401e+05 ... 5.419e+05\n",
+       "    y               (example, y_index) float64 6.92e+05 6.992e+05 ... 2.449e+05</pre><div class='xr-wrap' hidden><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-4d2834ba-e5b7-4994-9430-952aea105c41' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4d2834ba-e5b7-4994-9430-952aea105c41' 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'>example</span>: 32</li><li><span class='xr-has-index'>channels_index</span>: 11</li><li><span class='xr-has-index'>time_index</span>: 31</li><li><span class='xr-has-index'>x_index</span>: 24</li><li><span class='xr-has-index'>y_index</span>: 24</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-58ecf1db-bd28-466a-97e5-a85830ca159f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-58ecf1db-bd28-466a-97e5-a85830ca159f' class='xr-section-summary' >Coordinates: <span>(5)</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'>channels_index</span></div><div class='xr-var-dims'>(channels_index)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7 8 9 10</div><input id='attrs-91c20684-31ef-484f-8074-8f11dae6967b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-91c20684-31ef-484f-8074-8f11dae6967b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbc78eee-827e-45c4-a8e4-f55d47096566' class='xr-var-data-in' type='checkbox'><label for='data-dbc78eee-827e-45c4-a8e4-f55d47096566' 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'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>example</span></div><div class='xr-var-dims'>(example)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 26 27 28 29 30 31</div><input id='attrs-08a722a6-7d5b-437f-9eec-437b2db805c3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-08a722a6-7d5b-437f-9eec-437b2db805c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c2fe8c7-f304-4df2-b808-c01aac901416' class='xr-var-data-in' type='checkbox'><label for='data-7c2fe8c7-f304-4df2-b808-c01aac901416' 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'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_index</span></div><div class='xr-var-dims'>(time_index)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 25 26 27 28 29 30</div><input id='attrs-e70c1b4d-10c5-42a0-9b2f-435ec35f33c2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e70c1b4d-10c5-42a0-9b2f-435ec35f33c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5fc76aa-3723-45b2-ba1b-5f5f9ba29946' class='xr-var-data-in' type='checkbox'><label for='data-c5fc76aa-3723-45b2-ba1b-5f5f9ba29946' 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'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x_index</span></div><div class='xr-var-dims'>(x_index)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 18 19 20 21 22 23</div><input id='attrs-080f5ece-e193-4dfb-9dc1-0e9f450d251b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-080f5ece-e193-4dfb-9dc1-0e9f450d251b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20d30ceb-8b2b-4b69-970d-bf355ac6b160' class='xr-var-data-in' type='checkbox'><label for='data-20d30ceb-8b2b-4b69-970d-bf355ac6b160' 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'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y_index</span></div><div class='xr-var-dims'>(y_index)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 18 19 20 21 22 23</div><input id='attrs-2fd50bab-cdc9-4472-ad89-2c2f3d209302' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2fd50bab-cdc9-4472-ad89-2c2f3d209302' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a851283-3619-4cad-8e7f-666699f809d4' class='xr-var-data-in' type='checkbox'><label for='data-9a851283-3619-4cad-8e7f-666699f809d4' 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'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7006645a-81c6-4cdc-858c-32bc1b75cef8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7006645a-81c6-4cdc-858c-32bc1b75cef8' class='xr-section-summary' >Data variables: <span>(5)</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>channels</span></div><div class='xr-var-dims'>(example, channels_index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;IR_016&#x27; &#x27;IR_039&#x27; ... &#x27;WV_073&#x27;</div><input id='attrs-199eb4ae-9caa-40f8-87be-43a8cfae3c48' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-199eb4ae-9caa-40f8-87be-43a8cfae3c48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a01a3ecc-4fda-4af7-ab65-9f9f9503dbdd' class='xr-var-data-in' type='checkbox'><label for='data-a01a3ecc-4fda-4af7-ab65-9f9f9503dbdd' 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'></dl></div><div class='xr-var-data'><pre>array([[&#x27;IR_016&#x27;, &#x27;IR_039&#x27;, &#x27;IR_087&#x27;, &#x27;IR_097&#x27;, &#x27;IR_108&#x27;, &#x27;IR_120&#x27;,\n",
+       "        &#x27;IR_134&#x27;, &#x27;VIS006&#x27;, &#x27;VIS008&#x27;, &#x27;WV_062&#x27;, &#x27;WV_073&#x27;],\n",
+       "       [&#x27;IR_016&#x27;, &#x27;IR_039&#x27;, &#x27;IR_087&#x27;, &#x27;IR_097&#x27;, &#x27;IR_108&#x27;, &#x27;IR_120&#x27;,\n",
+       "        &#x27;IR_134&#x27;, &#x27;VIS006&#x27;, &#x27;VIS008&#x27;, &#x27;WV_062&#x27;, &#x27;WV_073&#x27;],\n",
+       "       [&#x27;IR_016&#x27;, &#x27;IR_039&#x27;, &#x27;IR_087&#x27;, &#x27;IR_097&#x27;, &#x27;IR_108&#x27;, &#x27;IR_120&#x27;,\n",
+       "        &#x27;IR_134&#x27;, &#x27;VIS006&#x27;, &#x27;VIS008&#x27;, &#x27;WV_062&#x27;, &#x27;WV_073&#x27;],\n",
+       "       [&#x27;IR_016&#x27;, &#x27;IR_039&#x27;, &#x27;IR_087&#x27;, &#x27;IR_097&#x27;, &#x27;IR_108&#x27;, &#x27;IR_120&#x27;,\n",
+       "        &#x27;IR_134&#x27;, &#x27;VIS006&#x27;, &#x27;VIS008&#x27;, &#x27;WV_062&#x27;, &#x27;WV_073&#x27;],\n",
+       "       [&#x27;IR_016&#x27;, &#x27;IR_039&#x27;, &#x27;IR_087&#x27;, &#x27;IR_097&#x27;, &#x27;IR_108&#x27;, &#x27;IR_120&#x27;,\n",
+       "        &#x27;IR_134&#x27;, &#x27;VIS006&#x27;, &#x27;VIS008&#x27;, &#x27;WV_062&#x27;, &#x27;WV_073&#x27;],\n",
+       "       [&#x27;IR_016&#x27;, &#x27;IR_039&#x27;, &#x27;IR_087&#x27;, &#x27;IR_097&#x27;, &#x27;IR_108&#x27;, &#x27;IR_120&#x27;,\n",
+       "        &#x27;IR_134&#x27;, &#x27;VIS006&#x27;, &#x27;VIS008&#x27;, &#x27;WV_062&#x27;, &#x27;WV_073&#x27;],\n",
+       "       [&#x27;IR_016&#x27;, &#x27;IR_039&#x27;, &#x27;IR_087&#x27;, &#x27;IR_097&#x27;, &#x27;IR_108&#x27;, &#x27;IR_120&#x27;,\n",
+       "        &#x27;IR_134&#x27;, &#x27;VIS006&#x27;, &#x27;VIS008&#x27;, &#x27;WV_062&#x27;, &#x27;WV_073&#x27;],\n",
+       "       [&#x27;IR_016&#x27;, &#x27;IR_039&#x27;, &#x27;IR_087&#x27;, &#x27;IR_097&#x27;, &#x27;IR_108&#x27;, &#x27;IR_120&#x27;,\n",
+       "        &#x27;IR_134&#x27;, &#x27;VIS006&#x27;, &#x27;VIS008&#x27;, &#x27;WV_062&#x27;, &#x27;WV_073&#x27;],\n",
+       "       [&#x27;IR_016&#x27;, &#x27;IR_039&#x27;, &#x27;IR_087&#x27;, &#x27;IR_097&#x27;, &#x27;IR_108&#x27;, &#x27;IR_120&#x27;,\n",
+       "        &#x27;IR_134&#x27;, &#x27;VIS006&#x27;, &#x27;VIS008&#x27;, &#x27;WV_062&#x27;, &#x27;WV_073&#x27;],\n",
+       "       [&#x27;IR_016&#x27;, &#x27;IR_039&#x27;, &#x27;IR_087&#x27;, &#x27;IR_097&#x27;, &#x27;IR_108&#x27;, &#x27;IR_120&#x27;,\n",
+       "        &#x27;IR_134&#x27;, &#x27;VIS006&#x27;, &#x27;VIS008&#x27;, &#x27;WV_062&#x27;, &#x27;WV_073&#x27;],\n",
+       "...\n",
+       "       [&#x27;IR_016&#x27;, &#x27;IR_039&#x27;, &#x27;IR_087&#x27;, &#x27;IR_097&#x27;, &#x27;IR_108&#x27;, &#x27;IR_120&#x27;,\n",
+       "        &#x27;IR_134&#x27;, &#x27;VIS006&#x27;, &#x27;VIS008&#x27;, &#x27;WV_062&#x27;, &#x27;WV_073&#x27;],\n",
+       "       [&#x27;IR_016&#x27;, &#x27;IR_039&#x27;, &#x27;IR_087&#x27;, &#x27;IR_097&#x27;, &#x27;IR_108&#x27;, &#x27;IR_120&#x27;,\n",
+       "        &#x27;IR_134&#x27;, &#x27;VIS006&#x27;, &#x27;VIS008&#x27;, &#x27;WV_062&#x27;, &#x27;WV_073&#x27;],\n",
+       "       [&#x27;IR_016&#x27;, &#x27;IR_039&#x27;, &#x27;IR_087&#x27;, &#x27;IR_097&#x27;, &#x27;IR_108&#x27;, &#x27;IR_120&#x27;,\n",
+       "        &#x27;IR_134&#x27;, &#x27;VIS006&#x27;, &#x27;VIS008&#x27;, &#x27;WV_062&#x27;, &#x27;WV_073&#x27;],\n",
+       "       [&#x27;IR_016&#x27;, &#x27;IR_039&#x27;, &#x27;IR_087&#x27;, &#x27;IR_097&#x27;, &#x27;IR_108&#x27;, &#x27;IR_120&#x27;,\n",
+       "        &#x27;IR_134&#x27;, &#x27;VIS006&#x27;, &#x27;VIS008&#x27;, &#x27;WV_062&#x27;, &#x27;WV_073&#x27;],\n",
+       "       [&#x27;IR_016&#x27;, &#x27;IR_039&#x27;, &#x27;IR_087&#x27;, &#x27;IR_097&#x27;, &#x27;IR_108&#x27;, &#x27;IR_120&#x27;,\n",
+       "        &#x27;IR_134&#x27;, &#x27;VIS006&#x27;, &#x27;VIS008&#x27;, &#x27;WV_062&#x27;, &#x27;WV_073&#x27;],\n",
+       "       [&#x27;IR_016&#x27;, &#x27;IR_039&#x27;, &#x27;IR_087&#x27;, &#x27;IR_097&#x27;, &#x27;IR_108&#x27;, &#x27;IR_120&#x27;,\n",
+       "        &#x27;IR_134&#x27;, &#x27;VIS006&#x27;, &#x27;VIS008&#x27;, &#x27;WV_062&#x27;, &#x27;WV_073&#x27;],\n",
+       "       [&#x27;IR_016&#x27;, &#x27;IR_039&#x27;, &#x27;IR_087&#x27;, &#x27;IR_097&#x27;, &#x27;IR_108&#x27;, &#x27;IR_120&#x27;,\n",
+       "        &#x27;IR_134&#x27;, &#x27;VIS006&#x27;, &#x27;VIS008&#x27;, &#x27;WV_062&#x27;, &#x27;WV_073&#x27;],\n",
+       "       [&#x27;IR_016&#x27;, &#x27;IR_039&#x27;, &#x27;IR_087&#x27;, &#x27;IR_097&#x27;, &#x27;IR_108&#x27;, &#x27;IR_120&#x27;,\n",
+       "        &#x27;IR_134&#x27;, &#x27;VIS006&#x27;, &#x27;VIS008&#x27;, &#x27;WV_062&#x27;, &#x27;WV_073&#x27;],\n",
+       "       [&#x27;IR_016&#x27;, &#x27;IR_039&#x27;, &#x27;IR_087&#x27;, &#x27;IR_097&#x27;, &#x27;IR_108&#x27;, &#x27;IR_120&#x27;,\n",
+       "        &#x27;IR_134&#x27;, &#x27;VIS006&#x27;, &#x27;VIS008&#x27;, &#x27;WV_062&#x27;, &#x27;WV_073&#x27;],\n",
+       "       [&#x27;IR_016&#x27;, &#x27;IR_039&#x27;, &#x27;IR_087&#x27;, &#x27;IR_097&#x27;, &#x27;IR_108&#x27;, &#x27;IR_120&#x27;,\n",
+       "        &#x27;IR_134&#x27;, &#x27;VIS006&#x27;, &#x27;VIS008&#x27;, &#x27;WV_062&#x27;, &#x27;WV_073&#x27;]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>data</span></div><div class='xr-var-dims'>(example, time_index, x_index, y_index, channels_index)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>301 861 724 755 ... 356 422 686 580</div><input id='attrs-1105356c-8dbc-4dba-8f81-3a8d5a7b6d31' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1105356c-8dbc-4dba-8f81-3a8d5a7b6d31' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b7563bc-d86e-481b-b00b-63091d2c4de6' class='xr-var-data-in' type='checkbox'><label for='data-6b7563bc-d86e-481b-b00b-63091d2c4de6' 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>meta :</span></dt><dd>{&#x27;resolution&#x27;: 3000.403165817, &#x27;ancillary_variables&#x27;: [], &#x27;reader&#x27;: &#x27;seviri_l1b_native&#x27;, &#x27;area&#x27;: Area ID: msg_seviri_rss_3km\n",
+       "Description: MSG SEVIRI Rapid Scanning Service area definition with 3 km resolution\n",
+       "Projection: {&#x27;a&#x27;: &#x27;6378169&#x27;, &#x27;h&#x27;: &#x27;35785831&#x27;, &#x27;lon_0&#x27;: &#x27;9.5&#x27;, &#x27;no_defs&#x27;: &#x27;None&#x27;, &#x27;proj&#x27;: &#x27;geos&#x27;, &#x27;rf&#x27;: &#x27;295.488065897014&#x27;, &#x27;type&#x27;: &#x27;crs&#x27;, &#x27;units&#x27;: &#x27;m&#x27;, &#x27;x_0&#x27;: &#x27;0&#x27;, &#x27;y_0&#x27;: &#x27;0&#x27;}\n",
+       "Number of columns: 615\n",
+       "Number of rows: 298\n",
+       "Area extent: (28503.8301, 5090183.9708, -1816744.1169, 4196063.8274), &#x27;end_time&#x27;: Timestamp(&#x27;2020-01-04 06:40:00&#x27;), &#x27;start_time&#x27;: datetime.datetime(2020, 1, 4, 6, 35, 9, 345706), &#x27;orbital_parameters&#x27;: {&#x27;projection_longitude&#x27;: 9.5, &#x27;projection_latitude&#x27;: 0.0, &#x27;projection_altitude&#x27;: 35785831.0, &#x27;satellite_nominal_longitude&#x27;: 9.5, &#x27;satellite_nominal_latitude&#x27;: 0.0, &#x27;satellite_actual_longitude&#x27;: 9.429925602398619, &#x27;satellite_actual_latitude&#x27;: -0.6091495420177121, &#x27;satellite_actual_altitude&#x27;: 35776668.57351005}, &#x27;platform_name&#x27;: &#x27;Meteosat-10&#x27;, &#x27;modifiers&#x27;: (), &#x27;sensor&#x27;: &#x27;seviri&#x27;, &#x27;georef_offset_corrected&#x27;: True}</dd><dt><span>coordinates :</span></dt><dd>acq_time</dd></dl></div><div class='xr-var-data'><pre>array([[[[[301, 861, 724, ..., 225, 454, 452],\n",
+       "          [325, 861, 722, ..., 240, 458, 448],\n",
+       "          [337, 862, 715, ..., 247, 451, 443],\n",
+       "          ...,\n",
+       "          [459, 878, 740, ..., 348, 515, 531],\n",
+       "          [468, 878, 737, ..., 345, 513, 529],\n",
+       "          [468, 880, 739, ..., 343, 523, 533]],\n",
+       "\n",
+       "         [[306, 858, 721, ..., 232, 462, 451],\n",
+       "          [323, 860, 722, ..., 244, 456, 454],\n",
+       "          [330, 859, 716, ..., 247, 447, 439],\n",
+       "          ...,\n",
+       "          [465, 879, 740, ..., 350, 517, 533],\n",
+       "          [466, 877, 737, ..., 343, 513, 529],\n",
+       "          [471, 880, 738, ..., 340, 519, 531]],\n",
+       "\n",
+       "         [[311, 857, 719, ..., 240, 460, 444],\n",
+       "          [317, 859, 724, ..., 242, 460, 448],\n",
+       "          [328, 855, 718, ..., 253, 447, 441],\n",
+       "          ...,\n",
+       "...\n",
+       "          ...,\n",
+       "          [469, 902, 767, ..., 371, 685, 581],\n",
+       "          [552, 904, 753, ..., 446, 688, 576],\n",
+       "          [621, 906, 738, ..., 504, 682, 574]],\n",
+       "\n",
+       "         [[503, 896, 735, ..., 420, 692, 587],\n",
+       "          [324, 884, 742, ..., 316, 691, 590],\n",
+       "          [233, 878, 750, ..., 225, 691, 588],\n",
+       "          ...,\n",
+       "          [434, 894, 774, ..., 355, 689, 581],\n",
+       "          [444, 897, 783, ..., 346, 688, 581],\n",
+       "          [581, 906, 753, ..., 467, 679, 574]],\n",
+       "\n",
+       "         [[458, 894, 736, ..., 384, 691, 586],\n",
+       "          [314, 888, 744, ..., 266, 692, 588],\n",
+       "          [191, 883, 755, ..., 165, 692, 592],\n",
+       "          ...,\n",
+       "          [439, 892, 770, ..., 369, 689, 582],\n",
+       "          [424, 897, 790, ..., 322, 688, 585],\n",
+       "          [540, 908, 765, ..., 422, 686, 580]]]]], dtype=int16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(example, time_index)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2021-03-22T13:20:00 ... 2021-03-...</div><input id='attrs-e5a70162-c744-4fb6-b350-f0caefc64629' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e5a70162-c744-4fb6-b350-f0caefc64629' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56325d5b-fd71-45bb-b96b-35ade27b1a3b' class='xr-var-data-in' type='checkbox'><label for='data-56325d5b-fd71-45bb-b96b-35ade27b1a3b' 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'></dl></div><div class='xr-var-data'><pre>array([[&#x27;2021-03-22T13:20:00.000000000&#x27;, &#x27;2021-03-22T13:25:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-22T13:30:00.000000000&#x27;, &#x27;2021-03-22T13:35:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-22T13:40:00.000000000&#x27;, &#x27;2021-03-22T13:45:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-22T13:50:00.000000000&#x27;, &#x27;2021-03-22T13:55:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-22T14:00:00.000000000&#x27;, &#x27;2021-03-22T14:05:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-22T14:10:00.000000000&#x27;, &#x27;2021-03-22T14:15:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-22T14:20:00.000000000&#x27;, &#x27;2021-03-22T14:25:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-22T14:30:00.000000000&#x27;, &#x27;2021-03-22T14:35:00.000000000&#x27;,\n",
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+       "        &#x27;2021-03-22T14:50:00.000000000&#x27;, &#x27;2021-03-22T14:55:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-22T15:00:00.000000000&#x27;, &#x27;2021-03-22T15:05:00.000000000&#x27;,\n",
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+       "        &#x27;2021-03-22T15:20:00.000000000&#x27;, &#x27;2021-03-22T15:25:00.000000000&#x27;,\n",
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+       "        &#x27;2021-03-22T15:50:00.000000000&#x27;],\n",
+       "       [&#x27;2021-08-13T09:15:00.000000000&#x27;, &#x27;2021-08-13T09:20:00.000000000&#x27;,\n",
+       "        &#x27;2021-08-13T09:25:00.000000000&#x27;, &#x27;2021-08-13T09:30:00.000000000&#x27;,\n",
+       "        &#x27;2021-08-13T09:35:00.000000000&#x27;, &#x27;2021-08-13T09:40:00.000000000&#x27;,\n",
+       "        &#x27;2021-08-13T09:45:00.000000000&#x27;, &#x27;2021-08-13T09:50:00.000000000&#x27;,\n",
+       "...\n",
+       "        &#x27;2021-05-01T09:45:00.000000000&#x27;, &#x27;2021-05-01T09:50:00.000000000&#x27;,\n",
+       "        &#x27;2021-05-01T09:55:00.000000000&#x27;, &#x27;2021-05-01T10:00:00.000000000&#x27;,\n",
+       "        &#x27;2021-05-01T10:05:00.000000000&#x27;, &#x27;2021-05-01T10:10:00.000000000&#x27;,\n",
+       "        &#x27;2021-05-01T10:15:00.000000000&#x27;],\n",
+       "       [&#x27;2021-03-25T09:40:00.000000000&#x27;, &#x27;2021-03-25T09:45:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-25T09:50:00.000000000&#x27;, &#x27;2021-03-25T09:55:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-25T10:00:00.000000000&#x27;, &#x27;2021-03-25T10:05:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-25T10:10:00.000000000&#x27;, &#x27;2021-03-25T10:15:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-25T10:20:00.000000000&#x27;, &#x27;2021-03-25T10:25:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-25T10:30:00.000000000&#x27;, &#x27;2021-03-25T10:35:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-25T10:40:00.000000000&#x27;, &#x27;2021-03-25T10:45:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-25T10:50:00.000000000&#x27;, &#x27;2021-03-25T10:55:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-25T11:00:00.000000000&#x27;, &#x27;2021-03-25T11:05:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-25T11:10:00.000000000&#x27;, &#x27;2021-03-25T11:15:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-25T11:20:00.000000000&#x27;, &#x27;2021-03-25T11:25:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-25T11:30:00.000000000&#x27;, &#x27;2021-03-25T11:35:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-25T11:40:00.000000000&#x27;, &#x27;2021-03-25T11:45:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-25T11:50:00.000000000&#x27;, &#x27;2021-03-25T11:55:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-25T12:00:00.000000000&#x27;, &#x27;2021-03-25T12:05:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-25T12:10:00.000000000&#x27;]], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(example, x_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.368e+05 3.401e+05 ... 5.419e+05</div><input id='attrs-8003e143-619e-47a8-92e4-6dee80fdff97' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8003e143-619e-47a8-92e4-6dee80fdff97' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-caa2b51f-a7d9-48ef-abde-983ed1a80095' class='xr-var-data-in' type='checkbox'><label for='data-caa2b51f-a7d9-48ef-abde-983ed1a80095' 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'></dl></div><div class='xr-var-data'><pre>array([[336832.65676005, 340110.37519061, 343387.28176287,\n",
+       "        346663.37952932, 349938.67153651, 353213.16082505,\n",
+       "        356486.85042959, 359759.74337897, 363031.84269612,\n",
+       "        366303.15139817, 369573.67249643, 372843.40899644,\n",
+       "        376112.363898  , 379380.54019516, 382647.94087629,\n",
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+       "        395709.84700967, 398973.41423837, 402236.22366295,\n",
+       "        405498.27823258, 408759.58089095, 412020.13457627],\n",
+       "       [284276.23477042, 287567.36304503, 290857.62978805,\n",
+       "        294147.0381511 , 297435.59127947, 300723.29231199,\n",
+       "        304010.14438122, 307296.15061333, 310581.3141282 ,\n",
+       "        313865.63803951, 317149.12545461, 320431.77947469,\n",
+       "        323713.60319476, 326994.59970366, 330274.77208411,\n",
+       "        333554.12341272, 336832.65676005, 340110.37519061,\n",
+       "        343387.28176287, 346663.37952932, 349938.67153651,\n",
+       "        353213.16082505, 356486.85042959, 359759.74337897],\n",
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+       "        349938.67153651, 353213.16082505, 356486.85042959,\n",
+       "...\n",
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+       "        297435.59127947, 300723.29231199, 304010.14438122,\n",
+       "        307296.15061333, 310581.3141282 , 313865.63803951,\n",
+       "        317149.12545461, 320431.77947469, 323713.60319476],\n",
+       "       [382647.94087629, 385914.56892408, 389180.42731555,\n",
+       "        392445.51902214, 395709.84700967, 398973.41423837,\n",
+       "        402236.22366295, 405498.27823258, 408759.58089095,\n",
+       "        412020.13457627, 415279.94222129, 418539.00675334,\n",
+       "        421797.33109435, 425054.9181609 , 428311.77086418,\n",
+       "        431567.89211008, 434823.28479919, 438077.95182678,\n",
+       "        441331.89608294, 444585.12045243, 447837.6278149 ,\n",
+       "        451089.42104474, 454340.50301123, 457590.87657849],\n",
+       "       [467337.77544945, 470585.34395901, 473832.21831368,\n",
+       "        477078.40134722, 480323.89588844, 483568.70476117,\n",
+       "        486812.83078432, 490056.2767719 , 493299.045533  ,\n",
+       "        496541.13987186, 499782.56258788, 503023.31647563,\n",
+       "        506263.40432488, 509502.82892061, 512741.59304307,\n",
+       "        515979.69946774, 519217.15096541, 522453.95030214,\n",
+       "        525690.10023937, 528925.60353383, 532160.46293769,\n",
+       "        535394.68119842, 538628.261059  , 541861.20525775]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(example, y_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.92e+05 6.992e+05 ... 2.449e+05</div><input id='attrs-2867dc34-fe9e-4237-8029-3a2526b26fce' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2867dc34-fe9e-4237-8029-3a2526b26fce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8cbfba69-02e8-4c8d-b7db-b6f3f9270de5' class='xr-var-data-in' type='checkbox'><label for='data-8cbfba69-02e8-4c8d-b7db-b6f3f9270de5' 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'></dl></div><div class='xr-var-data'><pre>array([[ 691983.81201156,  699191.30217573,  706417.27021661,\n",
+       "         713661.86369404,  720925.23209771,  728207.52688225,\n",
+       "         735508.90150385,  742829.51145715,  750169.51431353,\n",
+       "         757529.06975998,  764908.33963884,  772307.48798912,\n",
+       "         779726.68108793,  787166.08749384,  794625.87809047,\n",
+       "         802106.2261317 ,  809607.30728825,  817129.29969451,\n",
+       "         824672.38399735,  832236.74340598,  839822.56374312,\n",
+       "         847430.03349711,  855059.34387602,  862710.68886252],\n",
+       "       [ 307674.87219714,  314023.7453993 ,  320385.02533963,\n",
+       "         326758.78923897,  333145.11509682,  339544.08170269,\n",
+       "         345955.76864689,  352380.25633243,  358817.62598633,\n",
+       "         365267.95967159,  371731.34029941,  378207.85164112,\n",
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+       "         423918.31440545,  430502.99472325,  437101.6005631 ,\n",
+       "         443714.22503023,  450340.96224207,  456981.90734356],\n",
+       "       [ 417347.46750153,  423918.31440545,  430502.99472325,\n",
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+       "         456981.90734356,  463637.15652297,  470306.80702807,\n",
+       "         476990.95718231,  483689.70640177,  490403.15521192,\n",
+       "...\n",
+       "         677623.68451453,  684794.65405907,  691983.81201156,\n",
+       "         699191.30217573,  706417.27021661,  713661.86369404,\n",
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+       "         742829.51145715,  750169.51431353,  757529.06975998],\n",
+       "       [ 345955.76864689,  352380.25633243,  358817.62598633,\n",
+       "         365267.95967159,  371731.34029941,  378207.85164112,\n",
+       "         384697.57834117,  391200.60592955,  397717.02083507,\n",
+       "         404246.91039832,  410790.36288534,  417347.46750153,\n",
+       "         423918.31440545,  430502.99472325,  437101.6005631 ,\n",
+       "         443714.22503023,  450340.96224207,  456981.90734356,\n",
+       "         463637.15652297,  470306.80702807,  476990.95718231,\n",
+       "         483689.70640177,  490403.15521192,  497131.40526528],\n",
+       "       [ 104658.38976747,  110636.63685315,  116625.11730355,\n",
+       "         122623.88772928,  128633.0052451 ,  134652.52747607,\n",
+       "         140682.51256414,  146723.01917444,  152774.1065021 ,\n",
+       "         158835.83427895,  164908.26278005,  170991.4528311 ,\n",
+       "         177085.46581514,  183190.36367979,  189306.20894474,\n",
+       "         195433.06470887,  201570.99465798,  207720.06307237,\n",
+       "         213880.33483468,  220051.87543777,  226234.75099277,\n",
+       "         232429.02823737,  238634.774544  ,  244852.05792849]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e0f9e1bc-1ffd-4621-addf-e01560eda600' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e0f9e1bc-1ffd-4621-addf-e01560eda600' 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.Dataset>\n",
+       "Dimensions:         (example: 32, channels_index: 11, time_index: 31, x_index: 24, y_index: 24)\n",
+       "Coordinates:\n",
+       "  * channels_index  (channels_index) int64 0 1 2 3 4 5 6 7 8 9 10\n",
+       "  * example         (example) int64 0 1 2 3 4 5 6 7 ... 24 25 26 27 28 29 30 31\n",
+       "  * time_index      (time_index) int64 0 1 2 3 4 5 6 7 ... 24 25 26 27 28 29 30\n",
+       "  * x_index         (x_index) int64 0 1 2 3 4 5 6 7 ... 16 17 18 19 20 21 22 23\n",
+       "  * y_index         (y_index) int64 0 1 2 3 4 5 6 7 ... 16 17 18 19 20 21 22 23\n",
+       "Data variables:\n",
+       "    channels        (example, channels_index) object 'IR_016' ... 'WV_073'\n",
+       "    data            (example, time_index, x_index, y_index, channels_index) int16 ...\n",
+       "    time            (example, time_index) datetime64[ns] 2021-03-22T13:20:00 ...\n",
+       "    x               (example, x_index) float64 3.368e+05 3.401e+05 ... 5.419e+05\n",
+       "    y               (example, y_index) float64 6.92e+05 6.992e+05 ... 2.449e+05"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sat_batch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "dbb924d9-8591-47f8-9eea-239a1bf0dc63",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'zlib': False,\n",
+       " 'shuffle': False,\n",
+       " 'complevel': 0,\n",
+       " 'fletcher32': False,\n",
+       " 'contiguous': True,\n",
+       " 'chunksizes': None,\n",
+       " 'source': '/mnt/storage_ssd_4tb/data/ocf/solar_pv_nowcasting/nowcasting_dataset_pipeline/prepared_ML_training_data/v15/test/satellite/000170.nc',\n",
+       " 'original_shape': (32, 31, 24, 24, 11),\n",
+       " 'dtype': dtype('int16')}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sat_batch[\"data\"].encoding"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "69c76de6-5fa5-4fd6-8496-0c6869079f67",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:         (example: 32, channels_index: 11, time_index: 24, x_index: 24, y_index: 24)\n",
+       "Coordinates:\n",
+       "  * channels_index  (channels_index) int64 0 1 2 3 4 5 6 7 8 9 10\n",
+       "  * example         (example) int64 0 1 2 3 4 5 6 7 ... 24 25 26 27 28 29 30 31\n",
+       "  * time_index      (time_index) int64 0 1 2 3 4 5 6 7 ... 17 18 19 20 21 22 23\n",
+       "  * x_index         (x_index) int64 0 1 2 3 4 5 6 7 ... 16 17 18 19 20 21 22 23\n",
+       "  * y_index         (y_index) int64 0 1 2 3 4 5 6 7 ... 16 17 18 19 20 21 22 23\n",
+       "Data variables:\n",
+       "    channels        (example, channels_index) object &#x27;IR_016&#x27; ... &#x27;WV_073&#x27;\n",
+       "    data            (example, time_index, x_index, y_index, channels_index) int16 ...\n",
+       "    time            (example, time_index) datetime64[ns] 2021-03-22T13:55:00 ...\n",
+       "    x               (example, x_index) float64 3.368e+05 3.401e+05 ... 5.419e+05\n",
+       "    y               (example, y_index) float64 6.92e+05 6.992e+05 ... 2.449e+05</pre><div class='xr-wrap' hidden><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-8ab34fbc-e0c8-4a53-96bc-775fa43158a8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8ab34fbc-e0c8-4a53-96bc-775fa43158a8' 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'>example</span>: 32</li><li><span class='xr-has-index'>channels_index</span>: 11</li><li><span class='xr-has-index'>time_index</span>: 24</li><li><span class='xr-has-index'>x_index</span>: 24</li><li><span class='xr-has-index'>y_index</span>: 24</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-05caba87-f043-4004-ba96-aadba83664bd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-05caba87-f043-4004-ba96-aadba83664bd' class='xr-section-summary' >Coordinates: <span>(5)</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'>channels_index</span></div><div class='xr-var-dims'>(channels_index)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7 8 9 10</div><input id='attrs-bf2215a4-6dcb-4fa0-80a6-07a5ba40a27d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bf2215a4-6dcb-4fa0-80a6-07a5ba40a27d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9fab659-7b41-402b-a481-aa65023559a0' class='xr-var-data-in' type='checkbox'><label for='data-d9fab659-7b41-402b-a481-aa65023559a0' 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'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>example</span></div><div class='xr-var-dims'>(example)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 26 27 28 29 30 31</div><input id='attrs-6ae1dd75-342e-448c-82f1-f62888a5733e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6ae1dd75-342e-448c-82f1-f62888a5733e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1168e909-eb3f-4b14-8229-46393aaf90e2' class='xr-var-data-in' type='checkbox'><label for='data-1168e909-eb3f-4b14-8229-46393aaf90e2' 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'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_index</span></div><div class='xr-var-dims'>(time_index)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 18 19 20 21 22 23</div><input id='attrs-37e5bc1f-a16f-4d40-a82f-b3290a3f71f9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-37e5bc1f-a16f-4d40-a82f-b3290a3f71f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1eb75795-d45e-4fa7-af62-5833bbfb64a3' class='xr-var-data-in' type='checkbox'><label for='data-1eb75795-d45e-4fa7-af62-5833bbfb64a3' 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'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x_index</span></div><div class='xr-var-dims'>(x_index)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 18 19 20 21 22 23</div><input id='attrs-ab272482-71a2-4f90-bd42-b53c1bdbc95f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ab272482-71a2-4f90-bd42-b53c1bdbc95f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-198e171b-1ab7-4fe4-986c-73a196cbeb11' class='xr-var-data-in' type='checkbox'><label for='data-198e171b-1ab7-4fe4-986c-73a196cbeb11' 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'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y_index</span></div><div class='xr-var-dims'>(y_index)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 18 19 20 21 22 23</div><input id='attrs-5f9af453-7b16-4592-a46e-636a0a3df39b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5f9af453-7b16-4592-a46e-636a0a3df39b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f6d52ec9-42d9-4c33-a5f4-32dabeb3d57e' class='xr-var-data-in' type='checkbox'><label for='data-f6d52ec9-42d9-4c33-a5f4-32dabeb3d57e' 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'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ac1525e0-57bc-47fc-8b07-68b3b7eaa76d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ac1525e0-57bc-47fc-8b07-68b3b7eaa76d' class='xr-section-summary' >Data variables: <span>(5)</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>channels</span></div><div class='xr-var-dims'>(example, channels_index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;IR_016&#x27; &#x27;IR_039&#x27; ... &#x27;WV_073&#x27;</div><input id='attrs-7654410e-63a4-41ad-bdcb-0e9bad7d0c10' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7654410e-63a4-41ad-bdcb-0e9bad7d0c10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b56ebb5-8dce-4c5d-b1c1-f909ab6baac5' class='xr-var-data-in' type='checkbox'><label for='data-8b56ebb5-8dce-4c5d-b1c1-f909ab6baac5' 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'></dl></div><div class='xr-var-data'><pre>array([[&#x27;IR_016&#x27;, &#x27;IR_039&#x27;, &#x27;IR_087&#x27;, &#x27;IR_097&#x27;, &#x27;IR_108&#x27;, &#x27;IR_120&#x27;,\n",
+       "        &#x27;IR_134&#x27;, &#x27;VIS006&#x27;, &#x27;VIS008&#x27;, &#x27;WV_062&#x27;, &#x27;WV_073&#x27;],\n",
+       "       [&#x27;IR_016&#x27;, &#x27;IR_039&#x27;, &#x27;IR_087&#x27;, &#x27;IR_097&#x27;, &#x27;IR_108&#x27;, &#x27;IR_120&#x27;,\n",
+       "        &#x27;IR_134&#x27;, &#x27;VIS006&#x27;, &#x27;VIS008&#x27;, &#x27;WV_062&#x27;, &#x27;WV_073&#x27;],\n",
+       "       [&#x27;IR_016&#x27;, &#x27;IR_039&#x27;, &#x27;IR_087&#x27;, &#x27;IR_097&#x27;, &#x27;IR_108&#x27;, &#x27;IR_120&#x27;,\n",
+       "        &#x27;IR_134&#x27;, &#x27;VIS006&#x27;, &#x27;VIS008&#x27;, &#x27;WV_062&#x27;, &#x27;WV_073&#x27;],\n",
+       "       [&#x27;IR_016&#x27;, &#x27;IR_039&#x27;, &#x27;IR_087&#x27;, &#x27;IR_097&#x27;, &#x27;IR_108&#x27;, &#x27;IR_120&#x27;,\n",
+       "        &#x27;IR_134&#x27;, &#x27;VIS006&#x27;, &#x27;VIS008&#x27;, &#x27;WV_062&#x27;, &#x27;WV_073&#x27;],\n",
+       "       [&#x27;IR_016&#x27;, &#x27;IR_039&#x27;, &#x27;IR_087&#x27;, &#x27;IR_097&#x27;, &#x27;IR_108&#x27;, &#x27;IR_120&#x27;,\n",
+       "        &#x27;IR_134&#x27;, &#x27;VIS006&#x27;, &#x27;VIS008&#x27;, &#x27;WV_062&#x27;, &#x27;WV_073&#x27;],\n",
+       "       [&#x27;IR_016&#x27;, &#x27;IR_039&#x27;, &#x27;IR_087&#x27;, &#x27;IR_097&#x27;, &#x27;IR_108&#x27;, &#x27;IR_120&#x27;,\n",
+       "        &#x27;IR_134&#x27;, &#x27;VIS006&#x27;, &#x27;VIS008&#x27;, &#x27;WV_062&#x27;, &#x27;WV_073&#x27;],\n",
+       "       [&#x27;IR_016&#x27;, &#x27;IR_039&#x27;, &#x27;IR_087&#x27;, &#x27;IR_097&#x27;, &#x27;IR_108&#x27;, &#x27;IR_120&#x27;,\n",
+       "        &#x27;IR_134&#x27;, &#x27;VIS006&#x27;, &#x27;VIS008&#x27;, &#x27;WV_062&#x27;, &#x27;WV_073&#x27;],\n",
+       "       [&#x27;IR_016&#x27;, &#x27;IR_039&#x27;, &#x27;IR_087&#x27;, &#x27;IR_097&#x27;, &#x27;IR_108&#x27;, &#x27;IR_120&#x27;,\n",
+       "        &#x27;IR_134&#x27;, &#x27;VIS006&#x27;, &#x27;VIS008&#x27;, &#x27;WV_062&#x27;, &#x27;WV_073&#x27;],\n",
+       "       [&#x27;IR_016&#x27;, &#x27;IR_039&#x27;, &#x27;IR_087&#x27;, &#x27;IR_097&#x27;, &#x27;IR_108&#x27;, &#x27;IR_120&#x27;,\n",
+       "        &#x27;IR_134&#x27;, &#x27;VIS006&#x27;, &#x27;VIS008&#x27;, &#x27;WV_062&#x27;, &#x27;WV_073&#x27;],\n",
+       "       [&#x27;IR_016&#x27;, &#x27;IR_039&#x27;, &#x27;IR_087&#x27;, &#x27;IR_097&#x27;, &#x27;IR_108&#x27;, &#x27;IR_120&#x27;,\n",
+       "        &#x27;IR_134&#x27;, &#x27;VIS006&#x27;, &#x27;VIS008&#x27;, &#x27;WV_062&#x27;, &#x27;WV_073&#x27;],\n",
+       "...\n",
+       "       [&#x27;IR_016&#x27;, &#x27;IR_039&#x27;, &#x27;IR_087&#x27;, &#x27;IR_097&#x27;, &#x27;IR_108&#x27;, &#x27;IR_120&#x27;,\n",
+       "        &#x27;IR_134&#x27;, &#x27;VIS006&#x27;, &#x27;VIS008&#x27;, &#x27;WV_062&#x27;, &#x27;WV_073&#x27;],\n",
+       "       [&#x27;IR_016&#x27;, &#x27;IR_039&#x27;, &#x27;IR_087&#x27;, &#x27;IR_097&#x27;, &#x27;IR_108&#x27;, &#x27;IR_120&#x27;,\n",
+       "        &#x27;IR_134&#x27;, &#x27;VIS006&#x27;, &#x27;VIS008&#x27;, &#x27;WV_062&#x27;, &#x27;WV_073&#x27;],\n",
+       "       [&#x27;IR_016&#x27;, &#x27;IR_039&#x27;, &#x27;IR_087&#x27;, &#x27;IR_097&#x27;, &#x27;IR_108&#x27;, &#x27;IR_120&#x27;,\n",
+       "        &#x27;IR_134&#x27;, &#x27;VIS006&#x27;, &#x27;VIS008&#x27;, &#x27;WV_062&#x27;, &#x27;WV_073&#x27;],\n",
+       "       [&#x27;IR_016&#x27;, &#x27;IR_039&#x27;, &#x27;IR_087&#x27;, &#x27;IR_097&#x27;, &#x27;IR_108&#x27;, &#x27;IR_120&#x27;,\n",
+       "        &#x27;IR_134&#x27;, &#x27;VIS006&#x27;, &#x27;VIS008&#x27;, &#x27;WV_062&#x27;, &#x27;WV_073&#x27;],\n",
+       "       [&#x27;IR_016&#x27;, &#x27;IR_039&#x27;, &#x27;IR_087&#x27;, &#x27;IR_097&#x27;, &#x27;IR_108&#x27;, &#x27;IR_120&#x27;,\n",
+       "        &#x27;IR_134&#x27;, &#x27;VIS006&#x27;, &#x27;VIS008&#x27;, &#x27;WV_062&#x27;, &#x27;WV_073&#x27;],\n",
+       "       [&#x27;IR_016&#x27;, &#x27;IR_039&#x27;, &#x27;IR_087&#x27;, &#x27;IR_097&#x27;, &#x27;IR_108&#x27;, &#x27;IR_120&#x27;,\n",
+       "        &#x27;IR_134&#x27;, &#x27;VIS006&#x27;, &#x27;VIS008&#x27;, &#x27;WV_062&#x27;, &#x27;WV_073&#x27;],\n",
+       "       [&#x27;IR_016&#x27;, &#x27;IR_039&#x27;, &#x27;IR_087&#x27;, &#x27;IR_097&#x27;, &#x27;IR_108&#x27;, &#x27;IR_120&#x27;,\n",
+       "        &#x27;IR_134&#x27;, &#x27;VIS006&#x27;, &#x27;VIS008&#x27;, &#x27;WV_062&#x27;, &#x27;WV_073&#x27;],\n",
+       "       [&#x27;IR_016&#x27;, &#x27;IR_039&#x27;, &#x27;IR_087&#x27;, &#x27;IR_097&#x27;, &#x27;IR_108&#x27;, &#x27;IR_120&#x27;,\n",
+       "        &#x27;IR_134&#x27;, &#x27;VIS006&#x27;, &#x27;VIS008&#x27;, &#x27;WV_062&#x27;, &#x27;WV_073&#x27;],\n",
+       "       [&#x27;IR_016&#x27;, &#x27;IR_039&#x27;, &#x27;IR_087&#x27;, &#x27;IR_097&#x27;, &#x27;IR_108&#x27;, &#x27;IR_120&#x27;,\n",
+       "        &#x27;IR_134&#x27;, &#x27;VIS006&#x27;, &#x27;VIS008&#x27;, &#x27;WV_062&#x27;, &#x27;WV_073&#x27;],\n",
+       "       [&#x27;IR_016&#x27;, &#x27;IR_039&#x27;, &#x27;IR_087&#x27;, &#x27;IR_097&#x27;, &#x27;IR_108&#x27;, &#x27;IR_120&#x27;,\n",
+       "        &#x27;IR_134&#x27;, &#x27;VIS006&#x27;, &#x27;VIS008&#x27;, &#x27;WV_062&#x27;, &#x27;WV_073&#x27;]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>data</span></div><div class='xr-var-dims'>(example, time_index, x_index, y_index, channels_index)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>354 859 724 754 ... 291 306 665 574</div><input id='attrs-a4189af8-2e75-4b34-9aac-cc07d1595e0d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a4189af8-2e75-4b34-9aac-cc07d1595e0d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e406eb9-685d-42d7-bda5-476f627ba544' class='xr-var-data-in' type='checkbox'><label for='data-6e406eb9-685d-42d7-bda5-476f627ba544' 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'></dl></div><div class='xr-var-data'><pre>array([[[[[354, 859, 724, ..., 263, 460, 462],\n",
+       "          [353, 863, 726, ..., 255, 458, 462],\n",
+       "          [359, 864, 729, ..., 257, 466, 471],\n",
+       "          ...,\n",
+       "          [475, 875, 742, ..., 352, 529, 542],\n",
+       "          [467, 870, 744, ..., 350, 535, 547],\n",
+       "          [457, 873, 744, ..., 337, 546, 551]],\n",
+       "\n",
+       "         [[337, 855, 720, ..., 258, 455, 461],\n",
+       "          [350, 860, 722, ..., 259, 453, 458],\n",
+       "          [369, 862, 725, ..., 268, 458, 463],\n",
+       "          ...,\n",
+       "          [485, 876, 740, ..., 366, 529, 537],\n",
+       "          [469, 871, 743, ..., 355, 539, 546],\n",
+       "          [461, 872, 744, ..., 344, 546, 550]],\n",
+       "\n",
+       "         [[297, 853, 717, ..., 234, 454, 452],\n",
+       "          [330, 857, 719, ..., 247, 451, 451],\n",
+       "          [365, 861, 722, ..., 268, 451, 455],\n",
+       "          ...,\n",
+       "...\n",
+       "          ...,\n",
+       "          [425, 889, 746, ..., 299, 668, 576],\n",
+       "          [441, 882, 741, ..., 269, 667, 578],\n",
+       "          [458, 881, 740, ..., 304, 666, 577]],\n",
+       "\n",
+       "         [[341, 892, 753, ..., 153, 669, 590],\n",
+       "          [425, 897, 749, ..., 197, 669, 589],\n",
+       "          [454, 898, 743, ..., 291, 673, 586],\n",
+       "          ...,\n",
+       "          [427, 894, 747, ..., 310, 665, 576],\n",
+       "          [426, 886, 740, ..., 278, 666, 577],\n",
+       "          [448, 880, 741, ..., 305, 666, 576]],\n",
+       "\n",
+       "         [[330, 886, 750, ..., 192, 671, 590],\n",
+       "          [419, 893, 747, ..., 211, 673, 588],\n",
+       "          [439, 890, 740, ..., 284, 676, 588],\n",
+       "          ...,\n",
+       "          [442, 896, 747, ..., 321, 663, 574],\n",
+       "          [425, 888, 739, ..., 287, 665, 577],\n",
+       "          [432, 883, 742, ..., 306, 665, 574]]]]], dtype=int16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(example, time_index)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2021-03-22T13:55:00 ... 2021-03-...</div><input id='attrs-6ef41875-76f8-46db-a15d-3119903bd5c4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6ef41875-76f8-46db-a15d-3119903bd5c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5916ef59-d25d-436b-9294-be331e6b6d1e' class='xr-var-data-in' type='checkbox'><label for='data-5916ef59-d25d-436b-9294-be331e6b6d1e' 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'></dl></div><div class='xr-var-data'><pre>array([[&#x27;2021-03-22T13:55:00.000000000&#x27;, &#x27;2021-03-22T14:00:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-22T14:05:00.000000000&#x27;, &#x27;2021-03-22T14:10:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-22T14:15:00.000000000&#x27;, &#x27;2021-03-22T14:20:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-22T14:25:00.000000000&#x27;, &#x27;2021-03-22T14:30:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-22T14:35:00.000000000&#x27;, &#x27;2021-03-22T14:40:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-22T14:45:00.000000000&#x27;, &#x27;2021-03-22T14:50:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-22T14:55:00.000000000&#x27;, &#x27;2021-03-22T15:00:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-22T15:05:00.000000000&#x27;, &#x27;2021-03-22T15:10:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-22T15:15:00.000000000&#x27;, &#x27;2021-03-22T15:20:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-22T15:25:00.000000000&#x27;, &#x27;2021-03-22T15:30:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-22T15:35:00.000000000&#x27;, &#x27;2021-03-22T15:40:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-22T15:45:00.000000000&#x27;, &#x27;2021-03-22T15:50:00.000000000&#x27;],\n",
+       "       [&#x27;2021-08-13T09:50:00.000000000&#x27;, &#x27;2021-08-13T09:55:00.000000000&#x27;,\n",
+       "        &#x27;2021-08-13T10:00:00.000000000&#x27;, &#x27;2021-08-13T10:05:00.000000000&#x27;,\n",
+       "        &#x27;2021-08-13T10:10:00.000000000&#x27;, &#x27;2021-08-13T10:15:00.000000000&#x27;,\n",
+       "        &#x27;2021-08-13T10:20:00.000000000&#x27;, &#x27;2021-08-13T10:25:00.000000000&#x27;,\n",
+       "        &#x27;2021-08-13T10:30:00.000000000&#x27;, &#x27;2021-08-13T10:35:00.000000000&#x27;,\n",
+       "        &#x27;2021-08-13T10:40:00.000000000&#x27;, &#x27;2021-08-13T10:45:00.000000000&#x27;,\n",
+       "        &#x27;2021-08-13T10:50:00.000000000&#x27;, &#x27;2021-08-13T10:55:00.000000000&#x27;,\n",
+       "        &#x27;2021-08-13T11:00:00.000000000&#x27;, &#x27;2021-08-13T11:05:00.000000000&#x27;,\n",
+       "...\n",
+       "        &#x27;2021-05-01T09:10:00.000000000&#x27;, &#x27;2021-05-01T09:15:00.000000000&#x27;,\n",
+       "        &#x27;2021-05-01T09:20:00.000000000&#x27;, &#x27;2021-05-01T09:25:00.000000000&#x27;,\n",
+       "        &#x27;2021-05-01T09:30:00.000000000&#x27;, &#x27;2021-05-01T09:35:00.000000000&#x27;,\n",
+       "        &#x27;2021-05-01T09:40:00.000000000&#x27;, &#x27;2021-05-01T09:45:00.000000000&#x27;,\n",
+       "        &#x27;2021-05-01T09:50:00.000000000&#x27;, &#x27;2021-05-01T09:55:00.000000000&#x27;,\n",
+       "        &#x27;2021-05-01T10:00:00.000000000&#x27;, &#x27;2021-05-01T10:05:00.000000000&#x27;,\n",
+       "        &#x27;2021-05-01T10:10:00.000000000&#x27;, &#x27;2021-05-01T10:15:00.000000000&#x27;],\n",
+       "       [&#x27;2021-03-25T10:15:00.000000000&#x27;, &#x27;2021-03-25T10:20:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-25T10:25:00.000000000&#x27;, &#x27;2021-03-25T10:30:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-25T10:35:00.000000000&#x27;, &#x27;2021-03-25T10:40:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-25T10:45:00.000000000&#x27;, &#x27;2021-03-25T10:50:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-25T10:55:00.000000000&#x27;, &#x27;2021-03-25T11:00:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-25T11:05:00.000000000&#x27;, &#x27;2021-03-25T11:10:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-25T11:15:00.000000000&#x27;, &#x27;2021-03-25T11:20:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-25T11:25:00.000000000&#x27;, &#x27;2021-03-25T11:30:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-25T11:35:00.000000000&#x27;, &#x27;2021-03-25T11:40:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-25T11:45:00.000000000&#x27;, &#x27;2021-03-25T11:50:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-25T11:55:00.000000000&#x27;, &#x27;2021-03-25T12:00:00.000000000&#x27;,\n",
+       "        &#x27;2021-03-25T12:05:00.000000000&#x27;, &#x27;2021-03-25T12:10:00.000000000&#x27;]],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(example, x_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.368e+05 3.401e+05 ... 5.419e+05</div><input id='attrs-72477c48-f9ea-4b95-adc0-c04274aebc40' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-72477c48-f9ea-4b95-adc0-c04274aebc40' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51092cc0-b5c7-4798-ac07-1bc535709120' class='xr-var-data-in' type='checkbox'><label for='data-51092cc0-b5c7-4798-ac07-1bc535709120' 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'></dl></div><div class='xr-var-data'><pre>array([[336832.65676005, 340110.37519061, 343387.28176287,\n",
+       "        346663.37952932, 349938.67153651, 353213.16082505,\n",
+       "        356486.85042959, 359759.74337897, 363031.84269612,\n",
+       "        366303.15139817, 369573.67249643, 372843.40899644,\n",
+       "        376112.363898  , 379380.54019516, 382647.94087629,\n",
+       "        385914.56892408, 389180.42731555, 392445.51902214,\n",
+       "        395709.84700967, 398973.41423837, 402236.22366295,\n",
+       "        405498.27823258, 408759.58089095, 412020.13457627],\n",
+       "       [284276.23477042, 287567.36304503, 290857.62978805,\n",
+       "        294147.0381511 , 297435.59127947, 300723.29231199,\n",
+       "        304010.14438122, 307296.15061333, 310581.3141282 ,\n",
+       "        313865.63803951, 317149.12545461, 320431.77947469,\n",
+       "        323713.60319476, 326994.59970366, 330274.77208411,\n",
+       "        333554.12341272, 336832.65676005, 340110.37519061,\n",
+       "        343387.28176287, 346663.37952932, 349938.67153651,\n",
+       "        353213.16082505, 356486.85042959, 359759.74337897],\n",
+       "       [320431.77947469, 323713.60319476, 326994.59970366,\n",
+       "        330274.77208411, 333554.12341272, 336832.65676005,\n",
+       "        340110.37519061, 343387.28176287, 346663.37952932,\n",
+       "        349938.67153651, 353213.16082505, 356486.85042959,\n",
+       "...\n",
+       "        287567.36304503, 290857.62978805, 294147.0381511 ,\n",
+       "        297435.59127947, 300723.29231199, 304010.14438122,\n",
+       "        307296.15061333, 310581.3141282 , 313865.63803951,\n",
+       "        317149.12545461, 320431.77947469, 323713.60319476],\n",
+       "       [382647.94087629, 385914.56892408, 389180.42731555,\n",
+       "        392445.51902214, 395709.84700967, 398973.41423837,\n",
+       "        402236.22366295, 405498.27823258, 408759.58089095,\n",
+       "        412020.13457627, 415279.94222129, 418539.00675334,\n",
+       "        421797.33109435, 425054.9181609 , 428311.77086418,\n",
+       "        431567.89211008, 434823.28479919, 438077.95182678,\n",
+       "        441331.89608294, 444585.12045243, 447837.6278149 ,\n",
+       "        451089.42104474, 454340.50301123, 457590.87657849],\n",
+       "       [467337.77544945, 470585.34395901, 473832.21831368,\n",
+       "        477078.40134722, 480323.89588844, 483568.70476117,\n",
+       "        486812.83078432, 490056.2767719 , 493299.045533  ,\n",
+       "        496541.13987186, 499782.56258788, 503023.31647563,\n",
+       "        506263.40432488, 509502.82892061, 512741.59304307,\n",
+       "        515979.69946774, 519217.15096541, 522453.95030214,\n",
+       "        525690.10023937, 528925.60353383, 532160.46293769,\n",
+       "        535394.68119842, 538628.261059  , 541861.20525775]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(example, y_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.92e+05 6.992e+05 ... 2.449e+05</div><input id='attrs-6d723b68-3b89-47d5-9a7b-fccf329662b7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6d723b68-3b89-47d5-9a7b-fccf329662b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a85ebad-2274-4e3c-a408-ff9a67216fae' class='xr-var-data-in' type='checkbox'><label for='data-8a85ebad-2274-4e3c-a408-ff9a67216fae' 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'></dl></div><div class='xr-var-data'><pre>array([[ 691983.81201156,  699191.30217573,  706417.27021661,\n",
+       "         713661.86369404,  720925.23209771,  728207.52688225,\n",
+       "         735508.90150385,  742829.51145715,  750169.51431353,\n",
+       "         757529.06975998,  764908.33963884,  772307.48798912,\n",
+       "         779726.68108793,  787166.08749384,  794625.87809047,\n",
+       "         802106.2261317 ,  809607.30728825,  817129.29969451,\n",
+       "         824672.38399735,  832236.74340598,  839822.56374312,\n",
+       "         847430.03349711,  855059.34387602,  862710.68886252],\n",
+       "       [ 307674.87219714,  314023.7453993 ,  320385.02533963,\n",
+       "         326758.78923897,  333145.11509682,  339544.08170269,\n",
+       "         345955.76864689,  352380.25633243,  358817.62598633,\n",
+       "         365267.95967159,  371731.34029941,  378207.85164112,\n",
+       "         384697.57834117,  391200.60592955,  397717.02083507,\n",
+       "         404246.91039832,  410790.36288534,  417347.46750153,\n",
+       "         423918.31440545,  430502.99472325,  437101.6005631 ,\n",
+       "         443714.22503023,  450340.96224207,  456981.90734356],\n",
+       "       [ 417347.46750153,  423918.31440545,  430502.99472325,\n",
+       "         437101.6005631 ,  443714.22503023,  450340.96224207,\n",
+       "         456981.90734356,  463637.15652297,  470306.80702807,\n",
+       "         476990.95718231,  483689.70640177,  490403.15521192,\n",
+       "...\n",
+       "         677623.68451453,  684794.65405907,  691983.81201156,\n",
+       "         699191.30217573,  706417.27021661,  713661.86369404,\n",
+       "         720925.23209771,  728207.52688225,  735508.90150385,\n",
+       "         742829.51145715,  750169.51431353,  757529.06975998],\n",
+       "       [ 345955.76864689,  352380.25633243,  358817.62598633,\n",
+       "         365267.95967159,  371731.34029941,  378207.85164112,\n",
+       "         384697.57834117,  391200.60592955,  397717.02083507,\n",
+       "         404246.91039832,  410790.36288534,  417347.46750153,\n",
+       "         423918.31440545,  430502.99472325,  437101.6005631 ,\n",
+       "         443714.22503023,  450340.96224207,  456981.90734356,\n",
+       "         463637.15652297,  470306.80702807,  476990.95718231,\n",
+       "         483689.70640177,  490403.15521192,  497131.40526528],\n",
+       "       [ 104658.38976747,  110636.63685315,  116625.11730355,\n",
+       "         122623.88772928,  128633.0052451 ,  134652.52747607,\n",
+       "         140682.51256414,  146723.01917444,  152774.1065021 ,\n",
+       "         158835.83427895,  164908.26278005,  170991.4528311 ,\n",
+       "         177085.46581514,  183190.36367979,  189306.20894474,\n",
+       "         195433.06470887,  201570.99465798,  207720.06307237,\n",
+       "         213880.33483468,  220051.87543777,  226234.75099277,\n",
+       "         232429.02823737,  238634.774544  ,  244852.05792849]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-739d0585-d547-4f91-800a-4a484271e8ff' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-739d0585-d547-4f91-800a-4a484271e8ff' 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.Dataset>\n",
+       "Dimensions:         (example: 32, channels_index: 11, time_index: 24, x_index: 24, y_index: 24)\n",
+       "Coordinates:\n",
+       "  * channels_index  (channels_index) int64 0 1 2 3 4 5 6 7 8 9 10\n",
+       "  * example         (example) int64 0 1 2 3 4 5 6 7 ... 24 25 26 27 28 29 30 31\n",
+       "  * time_index      (time_index) int64 0 1 2 3 4 5 6 7 ... 17 18 19 20 21 22 23\n",
+       "  * x_index         (x_index) int64 0 1 2 3 4 5 6 7 ... 16 17 18 19 20 21 22 23\n",
+       "  * y_index         (y_index) int64 0 1 2 3 4 5 6 7 ... 16 17 18 19 20 21 22 23\n",
+       "Data variables:\n",
+       "    channels        (example, channels_index) object 'IR_016' ... 'WV_073'\n",
+       "    data            (example, time_index, x_index, y_index, channels_index) int16 ...\n",
+       "    time            (example, time_index) datetime64[ns] 2021-03-22T13:55:00 ...\n",
+       "    x               (example, x_index) float64 3.368e+05 3.401e+05 ... 5.419e+05\n",
+       "    y               (example, y_index) float64 6.92e+05 6.992e+05 ... 2.449e+05"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "opt_flow_batch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "56368a1b-3ca6-4d95-855a-aa8ecd92cc48",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'zlib': False,\n",
+       " 'shuffle': False,\n",
+       " 'complevel': 0,\n",
+       " 'fletcher32': False,\n",
+       " 'contiguous': False,\n",
+       " 'chunksizes': (8, 6, 6, 12, 6),\n",
+       " 'source': '/mnt/storage_ssd_4tb/data/ocf/solar_pv_nowcasting/nowcasting_dataset_pipeline/prepared_ML_training_data/v15/test/opticalflow/000170.nc',\n",
+       " 'original_shape': (32, 24, 24, 24, 11),\n",
+       " 'dtype': dtype('int16')}"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "opt_flow_batch[\"data\"].encoding"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "31f40bed-3faa-41ed-a207-9930c1522feb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f413615e9d0>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "EXAMPLE_I = 0\n",
+    "\n",
+    "opt_flow_batch[\"data\"].sel(example=EXAMPLE_I, channels_index=0, time_index=0).assign_coords(\n",
+    "    x=(\n",
+    "        \"x_index\", \n",
+    "        opt_flow_batch[\"x\"].sel(example=EXAMPLE_I).data\n",
+    "    ),\n",
+    "    y=(\n",
+    "        \"y_index\", \n",
+    "        opt_flow_batch[\"y\"].sel(example=EXAMPLE_I).data\n",
+    "    )\n",
+    ").swap_dims(\n",
+    "    {\n",
+    "        \"x_index\": \"x\",\n",
+    "        \"y_index\": \"y\"\n",
+    "    }\n",
+    ").plot.imshow(x=\"x\", y=\"y\", figsize=(10, 10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "3cf20dca-6508-49e1-8e4e-793c3f9aa029",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f4135f5d130>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sat_batch[\"data\"].sel(example=EXAMPLE_I, channels_index=0, time_index=7).assign_coords(\n",
+    "    x=(\n",
+    "        \"x_index\", \n",
+    "        sat_batch[\"x\"].sel(example=EXAMPLE_I).data\n",
+    "    ),\n",
+    "    y=(\n",
+    "        \"y_index\", \n",
+    "        sat_batch[\"y\"].sel(example=EXAMPLE_I).data\n",
+    "    )\n",
+    ").swap_dims(\n",
+    "    {\n",
+    "        \"x_index\": \"x\",\n",
+    "        \"y_index\": \"y\"\n",
+    "    }\n",
+    ").plot.imshow(x=\"x\", y=\"y\", figsize=(10, 10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "892ae298-046c-479f-a639-500b4fd9491b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from IPython.display import HTML\n",
+    "from matplotlib.animation import FuncAnimation\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from nowcasting_dataset.data_sources.optical_flow.optical_flow_data_source import crop_center"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "33fcdc69-a3c7-49f4-ae3a-3dfea70fbbf3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<video width=\"1296\" height=\"576\" controls autoplay loop>\n",
+       "  <source type=\"video/mp4\" src=\"data:video/mp4;base64,AAAAIGZ0eXBNNFYgAAACAE00ViBpc29taXNvMmF2YzEAAAAIZnJlZQAIjJhtZGF0AAACrgYF//+q\n",
+       "3EXpvebZSLeWLNgg2SPu73gyNjQgLSBjb3JlIDE2MCByMzAxMSBjZGU5YTkzIC0gSC4yNjQvTVBF\n",
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+       "\">\n",
+       "  Your browser does not support the video tag.\n",
+       "</video>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 1296x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "EXAMPLE_I = 6\n",
+    "CHANNEL_I = 7\n",
+    "HISTORY_LENGTH = 7\n",
+    "sat_data = sat_batch[\"data\"].sel(example=EXAMPLE_I, channels_index=CHANNEL_I)\n",
+    "channel_name = sat_batch[\"channels\"].sel(example=EXAMPLE_I, channels_index=CHANNEL_I).values\n",
+    "opt_flow_data = opt_flow_batch[\"data\"].sel(example=EXAMPLE_I, channels_index=CHANNEL_I)\n",
+    "min_pixel_val = min(sat_data.min(), opt_flow_data.min())\n",
+    "max_pixel_val = min(sat_data.max(), opt_flow_data.max())\n",
+    "imshow_kwargs = dict(x='x_index', y='y_index', add_colorbar=False, vmin=min_pixel_val, vmax=max_pixel_val)\n",
+    "\n",
+    "fig, axes = plt.subplots(figsize=(18, 8), ncols=2)\n",
+    "\n",
+    "ax = axes[0]\n",
+    "sat_img = sat_data.isel(time_index=0).plot.imshow(ax=ax, **imshow_kwargs)\n",
+    "\n",
+    "ax = axes[1]\n",
+    "opt_flow_img = opt_flow_data.isel(time_index=0).plot.imshow(ax=ax, **imshow_kwargs)\n",
+    "OPT_FLOW_TITLE = \"Optical flow precitions\"\n",
+    "ax.set_title(OPT_FLOW_TITLE)\n",
+    "\n",
+    "\n",
+    "def format_date(dt: np.datetime64) -> str:\n",
+    "    return pd.Timestamp(dt).strftime(\"%Y-%m-%d %H:%M\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "def init():\n",
+    "    sat_img.set_data(sat_data.isel(time_index=0))\n",
+    "    axes[1].set_title(OPT_FLOW_TITLE)\n",
+    "    opt_flow_img.set_data(np.full(shape=opt_flow_data.isel(time_index=0).shape, fill_value=np.NaN))\n",
+    "    return sat_img, opt_flow_img\n",
+    "\n",
+    "def update(i):\n",
+    "    # SAT DATA\n",
+    "    sat_img.set_data(sat_data.isel(time_index=i))\n",
+    "    datetime = sat_batch[\"time\"].isel(example=EXAMPLE_I, time_index=i).values\n",
+    "    axes[0].set_title(\"Real satellite data | \" + format_date(datetime) + \" | chan = \" + channel_name)\n",
+    "    \n",
+    "    # OPTICAL FLOW PREDICTIONS\n",
+    "    if i > HISTORY_LENGTH:\n",
+    "        opt_flow_datetime = opt_flow_batch[\"time\"].isel(example=EXAMPLE_I, time_index=i-HISTORY_LENGTH).values\n",
+    "        axes[1].set_title(OPT_FLOW_TITLE + \" | \" + format_date(opt_flow_datetime))\n",
+    "        new_opt_flow_data = opt_flow_data.isel(time_index=i-HISTORY_LENGTH).values.copy()\n",
+    "        opt_flow_img.set_data(new_opt_flow_data)\n",
+    "    return sat_img, opt_flow_img\n",
+    "\n",
+    "anim = FuncAnimation(fig, func=update, frames=np.arange(30), init_func=init, interval=250, blit=True)\n",
+    "#anim.save('optical_flow.gif', writer='imagemagick')\n",
+    "html = anim.to_html5_video()\n",
+    "HTML(html)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "id": "e63ccdad-819f-45cd-83cf-0f9fdadf57e0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "\">\n",
+       "  Your browser does not support the video tag.\n",
+       "</video>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 1296x504 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "EXAMPLE_I = 2\n",
+    "CHANNEL_I = 7\n",
+    "HISTORY_LENGTH = 6\n",
+    "sat_data = sat_batch[\"data\"].sel(example=EXAMPLE_I, channels_index=CHANNEL_I)\n",
+    "channel_name = sat_batch[\"channels\"].sel(example=EXAMPLE_I, channels_index=CHANNEL_I).values\n",
+    "opt_flow_data = opt_flow_batch[\"data\"].sel(example=EXAMPLE_I, channels_index=CHANNEL_I)\n",
+    "min_pixel_val = min(sat_data.min(), opt_flow_data.min())\n",
+    "max_pixel_val = min(sat_data.max(), opt_flow_data.max())\n",
+    "imshow_kwargs = dict(x='x_index', y='y_index', add_colorbar=False, vmin=min_pixel_val, vmax=max_pixel_val)\n",
+    "\n",
+    "fig, axes = plt.subplots(figsize=(18, 7), ncols=3)\n",
+    "\n",
+    "ax = axes[0]\n",
+    "sat_img = sat_data.isel(time_index=0).plot.imshow(ax=ax, **imshow_kwargs)\n",
+    "\n",
+    "ax = axes[1]\n",
+    "opt_flow_cropped_img = crop_center(\n",
+    "    opt_flow_data.isel(time_index=0),\n",
+    "    24,\n",
+    "    24\n",
+    ").plot.imshow(ax=ax, **imshow_kwargs)\n",
+    "OPT_FLOW_TITLE = \"Optical flow precitions (cropped) \"\n",
+    "ax.set_title(OPT_FLOW_TITLE)\n",
+    "\n",
+    "ax = axes[2]\n",
+    "opt_flow_img = opt_flow_data.isel(time_index=0).plot.imshow(ax=ax, **imshow_kwargs)\n",
+    "ax.set_title(\"Optical flow precitions (zoomed out)\")\n",
+    "\n",
+    "\n",
+    "def format_date(dt: np.datetime64) -> str:\n",
+    "    return pd.Timestamp(dt).strftime(\"%Y-%m-%d %H:%M\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "def init():\n",
+    "    sat_img.set_data(sat_data.isel(time_index=0))\n",
+    "    axes[1].set_title(OPT_FLOW_TITLE)\n",
+    "    opt_flow_cropped_img.set_data(np.full(shape=sat_data.isel(time_index=0).shape, fill_value=np.NaN))\n",
+    "    opt_flow_img.set_data(np.full(shape=opt_flow_data.isel(time_index=0).shape, fill_value=np.NaN))\n",
+    "    return sat_img, opt_flow_cropped_img, opt_flow_img\n",
+    "\n",
+    "def update(i):\n",
+    "    # SAT DATA\n",
+    "    sat_img.set_data(sat_data.isel(time_index=i))\n",
+    "    datetime = sat_batch[\"time\"].isel(example=EXAMPLE_I, time_index=i).values\n",
+    "    axes[0].set_title(\"Real satellite data | \" + format_date(datetime) + \" | chan = \" + channel_name)\n",
+    "    \n",
+    "    # OPTICAL FLOW PREDICTIONS\n",
+    "    if i > HISTORY_LENGTH:\n",
+    "        opt_flow_datetime = opt_flow_batch[\"time\"].isel(example=EXAMPLE_I, time_index=i-HISTORY_LENGTH).values\n",
+    "        axes[1].set_title(OPT_FLOW_TITLE + format_date(opt_flow_datetime))\n",
+    "        new_opt_flow_data = opt_flow_data.isel(time_index=i-HISTORY_LENGTH).values.copy()\n",
+    "        opt_flow_cropped_img.set_data(\n",
+    "            crop_center(\n",
+    "                new_opt_flow_data,\n",
+    "                24,\n",
+    "                24\n",
+    "            )\n",
+    "        )\n",
+    "        new_opt_flow_data[[39, 63], 39:63] = 0\n",
+    "        new_opt_flow_data[39:64, [39, 63]] = 0\n",
+    "        opt_flow_img.set_data(new_opt_flow_data)\n",
+    "    return sat_img, opt_flow_cropped_img, opt_flow_img\n",
+    "\n",
+    "anim = FuncAnimation(fig, func=update, frames=np.arange(30), init_func=init, interval=250, blit=True)\n",
+    "#anim.save('optical_flow.gif', writer='imagemagick')\n",
+    "html = anim.to_html5_video()\n",
+    "HTML(html)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5b906a09-e147-44e7-93ed-c60e14b37031",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "nowcasting_dataset",
+   "language": "python",
+   "name": "nowcasting_dataset"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/nowcasting_dataset/config/gcp.yaml b/nowcasting_dataset/config/gcp.yaml
index 12915f26..8e74ba9d 100644
--- a/nowcasting_dataset/config/gcp.yaml
+++ b/nowcasting_dataset/config/gcp.yaml
@@ -4,10 +4,14 @@ general:
 input_data:
   default_forecast_minutes: 60
   default_history_minutes: 30
+
+  #---------------------- GSP -------------------
   gsp:
     forecast_minutes: 60
     gsp_zarr_path: gs://solar-pv-nowcasting-data/PV/GSP/v3/pv_gsp.zarr
     history_minutes: 60
+
+  #---------------------- NWP -------------------
   nwp:
     forecast_minutes: 60
     history_minutes: 60
@@ -24,12 +28,16 @@ input_data:
       - hcc
     nwp_image_size_pixels: 64
     nwp_zarr_path: gs://solar-pv-nowcasting-data/NWP/UK_Met_Office/UKV__2018-01_to_2019-12__chunks__variable10__init_time1__step1__x548__y704__.zarr
+
+  #---------------------- PV -------------------
   pv:
     forecast_minutes: 60
     history_minutes: 30
     pv_filename: gs://solar-pv-nowcasting-data/PV/Passive/ocf_formatted/v0/passiv.netcdf
     pv_metadata_filename: gs://solar-pv-nowcasting-data/PV/Passive/ocf_formatted/v0/system_metadata.csv
     get_center: false
+
+  #---------------------- Satellite -------------
   satellite:
     forecast_minutes: 60
     history_minutes: 30
@@ -48,14 +56,43 @@ input_data:
       - WV_073
     satellite_image_size_pixels: 64
     satellite_zarr_path: gs://solar-pv-nowcasting-data/satellite/EUMETSAT/SEVIRI_RSS/OSGB36/all_zarr_int16_single_timestep.zarr
+
+  #---------------------- HRVSatellite -------------
+  # The satellite Zarr data on GCP is the older Zarr, which contains
+  # HRV and the non-HRV channels in a single Zarr.
+
+  # ------------------------- Sun ------------------------
   sun:
     forecast_minutes: 60
     history_minutes: 30
     sun_zarr_path: gs://solar-pv-nowcasting-data/Sun/v0/sun.zarr
+
+  # ------------------------- Topographic ----------------
   topographic:
     forecast_minutes: 60
     history_minutes: 30
     topographic_filename: gs://solar-pv-nowcasting-data/Topographic/europe_dem_1km_osgb.tif
+
+  # ------------------------- Optical Flow ---------------
+  opticalflow:
+    opticalflow_zarr_path: gs://solar-pv-nowcasting-data/satellite/EUMETSAT/SEVIRI_RSS/OSGB36/all_zarr_int16_single_timestep.zarr
+    history_minutes: 5
+    forecast_minutes: 120
+    opticalflow_input_image_size_pixels: 102
+    opticalflow_output_image_size_pixels: 24
+    opticalflow_source_data_source_class_name: SatelliteDataSource
+    opticalflow_channels:
+      - IR_016
+      - IR_039
+      - IR_087
+      - IR_097
+      - IR_108
+      - IR_120
+      - IR_134
+      - VIS006
+      - VIS008
+      - WV_062
+      - WV_073
 output_data:
   filepath: gs://solar-pv-nowcasting-data/prepared_ML_training_data/v9/
 process:
diff --git a/nowcasting_dataset/config/model.py b/nowcasting_dataset/config/model.py
index e7b91bd1..818c2e15 100644
--- a/nowcasting_dataset/config/model.py
+++ b/nowcasting_dataset/config/model.py
@@ -30,7 +30,10 @@
 )
 from nowcasting_dataset.dataset.split import split
 
-IMAGE_SIZE_PIXELS_FIELD = Field(64, description="The number of pixels of the region of interest.")
+IMAGE_SIZE_PIXELS = 64
+IMAGE_SIZE_PIXELS_FIELD = Field(
+    IMAGE_SIZE_PIXELS, description="The number of pixels of the region of interest."
+)
 METERS_PER_PIXEL_FIELD = Field(2000, description="The number of meters per pixel.")
 
 logger = logging.getLogger(__name__)
@@ -189,6 +192,55 @@ class HRVSatellite(DataSourceMixin):
     hrvsatellite_meters_per_pixel: int = METERS_PER_PIXEL_FIELD
 
 
+class OpticalFlow(DataSourceMixin):
+    """Optical Flow configuration model"""
+
+    opticalflow_zarr_path: str = Field(
+        "",
+        description=(
+            "The satellite Zarr data to use. If in doubt, use the same value as"
+            " satellite.satellite_zarr_path."
+        ),
+    )
+
+    # history_minutes, set in DataSourceMixin.
+    # Duration of historical data to use when computing the optical flow field.
+    # For example, set to 5 to use just two images: the t-1 and t0 images.  Set to 10 to
+    # compute the optical flow field separately for the image pairs (t-2, t-1), and
+    # (t-1, t0) and to use the mean optical flow field.
+
+    # forecast_minutes, set in DataSourceMixin.
+    # Duration of the optical flow predictions.
+
+    opticalflow_meters_per_pixel: int = METERS_PER_PIXEL_FIELD
+    opticalflow_input_image_size_pixels: int = Field(
+        IMAGE_SIZE_PIXELS * 2,
+        description=(
+            "The *input* image size (i.e. the image size to load off disk)."
+            " This should be larger than output_image_size_pixels to provide sufficient border to"
+            " mean that, even after the image has been flowed, all edges of the output image are"
+            " real pixels values, and not NaNs."
+        ),
+    )
+    opticalflow_output_image_size_pixels: int = Field(
+        IMAGE_SIZE_PIXELS,
+        description=(
+            "The size of the images after optical flow has been applied. The output image is a"
+            " center-crop of the input image, after it has been flowed."
+        ),
+    )
+    opticalflow_channels: tuple = Field(
+        SAT_VARIABLE_NAMES[1:], description="the satellite channels that are used"
+    )
+    opticalflow_source_data_source_class_name: str = Field(
+        "SatelliteDataSource",
+        description=(
+            "Either SatelliteDataSource or HRVSatelliteDataSource."
+            "  The name of the DataSource that will load the satellite images."
+        ),
+    )
+
+
 class NWP(DataSourceMixin):
     """NWP configuration model"""
 
@@ -254,6 +306,7 @@ class InputData(BaseModel):
     pv: Optional[PV] = None
     satellite: Optional[Satellite] = None
     hrvsatellite: Optional[HRVSatellite] = None
+    opticalflow: Optional[OpticalFlow] = None
     nwp: Optional[NWP] = None
     gsp: Optional[GSP] = None
     topographic: Optional[Topographic] = None
@@ -301,6 +354,7 @@ def set_forecast_and_history_minutes(cls, values):
             "gsp",
             "topographic",
             "sun",
+            "opticalflow",
         )
         enabled_data_sources = [
             data_source_name
@@ -331,6 +385,7 @@ def set_all_to_defaults(cls):
             gsp=GSP(),
             topographic=Topographic(),
             sun=Sun(),
+            optical_flow=OpticalFlow(),
         )
 
 
@@ -406,7 +461,14 @@ class Process(BaseModel):
         ),
     )
 
-    local_temp_path: str = Field("~/temp/")
+    local_temp_path: Path = Field(
+        Path("~/temp/").expanduser(),
+        description=(
+            "This is only necessary if using a VM on a public cloud and when the finished batches"
+            " will be uploaded to a cloud bucket. This is the local temporary path on the VM."
+            "  This will be emptied."
+        ),
+    )
 
     @validator("local_temp_path")
     def local_temp_path_to_path_object_expanduser(cls, v):
diff --git a/nowcasting_dataset/config/on_premises.yaml b/nowcasting_dataset/config/on_premises.yaml
index 6d3905c7..e239c25a 100644
--- a/nowcasting_dataset/config/on_premises.yaml
+++ b/nowcasting_dataset/config/on_premises.yaml
@@ -48,6 +48,7 @@ input_data:
     satellite_image_size_pixels: 24
     satellite_zarr_path: /mnt/storage_ssd_8tb/data/ocf/solar_pv_nowcasting/nowcasting_dataset_pipeline/satellite/EUMETSAT/SEVIRI_RSS/zarr/v2/eumetsat_zarr_*
 
+  #---------------------- HRVSatellite -------------
   hrvsatellite:
     hrvsatellite_channels:
       - HRV
@@ -62,6 +63,27 @@ input_data:
   topographic:
     topographic_filename: /mnt/storage_b/data/ocf/solar_pv_nowcasting/nowcasting_dataset_pipeline/Topographic/europe_dem_1km_osgb.tif
 
+  # ------------------------- Optical Flow ---------------
+  opticalflow:
+    opticalflow_zarr_path: /mnt/storage_ssd_8tb/data/ocf/solar_pv_nowcasting/nowcasting_dataset_pipeline/satellite/EUMETSAT/SEVIRI_RSS/zarr/v2/eumetsat_zarr_*
+    history_minutes: 5
+    forecast_minutes: 120
+    opticalflow_input_image_size_pixels: 102
+    opticalflow_output_image_size_pixels: 24
+    opticalflow_source_data_source_class_name: SatelliteDataSource
+    opticalflow_channels:
+      - IR_016
+      - IR_039
+      - IR_087
+      - IR_097
+      - IR_108
+      - IR_120
+      - IR_134
+      - VIS006
+      - VIS008
+      - WV_062
+      - WV_073
+
 output_data:
   filepath: /mnt/storage_ssd_4tb/data/ocf/solar_pv_nowcasting/nowcasting_dataset_pipeline/prepared_ML_training_data/v15
 process:
diff --git a/nowcasting_dataset/data_sources/__init__.py b/nowcasting_dataset/data_sources/__init__.py
index c96dad9c..56c0a510 100644
--- a/nowcasting_dataset/data_sources/__init__.py
+++ b/nowcasting_dataset/data_sources/__init__.py
@@ -2,6 +2,12 @@
 from nowcasting_dataset.data_sources.data_source import DataSource  # noqa: F401
 from nowcasting_dataset.data_sources.gsp.gsp_data_source import GSPDataSource
 from nowcasting_dataset.data_sources.nwp.nwp_data_source import NWPDataSource
+
+# We must import OpticalFlowDataSource *after* SatelliteDataSource,
+# otherwise we get circular import errors!
+from nowcasting_dataset.data_sources.optical_flow.optical_flow_data_source import (
+    OpticalFlowDataSource,
+)
 from nowcasting_dataset.data_sources.pv.pv_data_source import PVDataSource
 from nowcasting_dataset.data_sources.satellite.satellite_data_source import (
     HRVSatelliteDataSource,
@@ -18,6 +24,7 @@
     "pv": PVDataSource,
     "satellite": SatelliteDataSource,
     "hrvsatellite": HRVSatelliteDataSource,
+    "opticalflow": OpticalFlowDataSource,
     "nwp": NWPDataSource,
     "gsp": GSPDataSource,
     "topographic": TopographicDataSource,
diff --git a/nowcasting_dataset/data_sources/data_source.py b/nowcasting_dataset/data_sources/data_source.py
index c23a4d17..234c4834 100644
--- a/nowcasting_dataset/data_sources/data_source.py
+++ b/nowcasting_dataset/data_sources/data_source.py
@@ -116,7 +116,7 @@ def sample_period_minutes(self) -> int:
 
         This functions may be overwritten if the sample period of the data source is not 5 minutes.
         """
-        logging.debug(
+        logger.debug(
             "Getting sample_period_minutes default of 5 minutes. "
             "This means the data is spaced 5 minutes apart"
         )
@@ -132,14 +132,8 @@ def open(self):
         """
         pass
 
-    def check_input_paths_exist(self) -> None:
-        """Check any input paths exist.  Raise FileNotFoundError if not.
-
-        Can be overridden by child classes.
-        """
-        pass
-
     # TODO: Issue #319: Standardise parameter names.
+    # TODO: Issue #367: Reduce duplication.
     def create_batches(
         self,
         spatial_and_temporal_locations_of_each_example: pd.DataFrame,
@@ -154,26 +148,39 @@ def create_batches(
         Safe to call from worker processes.
 
         Args:
-          spatial_and_temporal_locations_of_each_example: A DataFrame where each row specifies
-            the spatial and temporal location of an example.  The number of rows must be
-            an exact multiple of `batch_size`.
-            Columns are: t0_datetime_UTC, x_center_OSGB, y_center_OSGB.
-          idx_of_first_batch: The batch number of the first batch to create.
-          batch_size: The number of examples per batch.
-          dst_path: The final destination path for the batches.  Must exist.
-          local_temp_path: The local temporary path.  This is only required when dst_path is a
-            cloud storage bucket, so files must first be created on the VM's local disk in temp_path
-            and then uploaded to dst_path every upload_every_n_batches. Must exist. Will be emptied.
-          upload_every_n_batches: Upload the contents of temp_path to dst_path after this number
-            of batches have been created.  If 0 then will write directly to dst_path.
+            spatial_and_temporal_locations_of_each_example (pd.DataFrame): A DataFrame where each
+                row specifies the spatial and temporal location of an example. The number of rows
+                must be an exact multiple of `batch_size`.
+                Columns are: t0_datetime_UTC, x_center_OSGB, y_center_OSGB.
+            idx_of_first_batch (int): The batch number of the first batch to create.
+            batch_size (int): The number of examples per batch.
+            dst_path (Path): The final destination path for the batches.  Must exist.
+            local_temp_path (Path): The local temporary path.  This is only required when dst_path
+                is a cloud storage bucket, so files must first be created on the VM's local disk in
+                temp_path and then uploaded to dst_path every `upload_every_n_batches`. Must exist.
+                Will be emptied.
+            upload_every_n_batches (int): Upload the contents of temp_path to dst_path after this
+                number of batches have been created.  If 0 then will write directly to `dst_path`.
         """
         # Sanity checks:
-        assert idx_of_first_batch >= 0
-        assert batch_size > 0
-        assert len(spatial_and_temporal_locations_of_each_example) % batch_size == 0
-        assert upload_every_n_batches >= 0
-        assert spatial_and_temporal_locations_of_each_example.columns.to_list() == list(
+        assert (
+            idx_of_first_batch >= 0
+        ), "The batch number of the first batch to create should be greater than 0"
+        assert batch_size > 0, "The batch size should be strictly greater than 0."
+        assert len(spatial_and_temporal_locations_of_each_example) % batch_size == 0, (
+            f"{len(spatial_and_temporal_locations_of_each_example)=} must be"
+            f" exactly divisible by {batch_size=}"
+        )
+        assert upload_every_n_batches >= 0, "`upload_every_n_batches` must be >= 0"
+
+        spatial_and_temporal_locations_of_each_example_columns = (
+            spatial_and_temporal_locations_of_each_example.columns.to_list()
+        )
+        assert spatial_and_temporal_locations_of_each_example_columns == list(
             SPATIAL_AND_TEMPORAL_LOCATIONS_COLUMN_NAMES
+        ), (
+            f"The provided data columns {spatial_and_temporal_locations_of_each_example_columns}"
+            f" do not match {SPATIAL_AND_TEMPORAL_LOCATIONS_COLUMN_NAMES=}"
         )
 
         self.open()
@@ -210,8 +217,10 @@ def create_batches(
             )
 
             # Save batch to disk.
+            # TODO: Issue #524: Use DataSourceOutput.save_netcdf in place of to_netcdf
             netcdf_filename = path_to_write_to / nd_utils.get_netcdf_filename(batch_idx)
-            batch.to_netcdf(netcdf_filename, engine="h5netcdf")
+            encoding = {name: {"compression": "lzf"} for name in batch.data_vars}
+            batch.to_netcdf(netcdf_filename, engine="h5netcdf", encoding=encoding)
 
             # Upload if necessary.
             if (
@@ -261,7 +270,19 @@ def get_batch(
                     self.get_example, t0_datetime, x_location, y_location
                 )
                 future_examples.append(future_example)
-            examples = [future_example.result() for future_example in future_examples]
+
+            # Get the examples back.  Loop round each future so we can log a helpful error.
+            # If the worker thread raised an exception then the exception won't "bubble up"
+            # until we call future_example.result().
+            examples = []
+            for example_i, future_example in enumerate(future_examples):
+                try:
+                    result = future_example.result()
+                except Exception:
+                    logger.error(f"Exception when processing {example_i=}!")
+                    raise
+                else:
+                    examples.append(result)
 
         # Get the DataSource class, this could be one of the data sources like Sun
         cls = self.get_data_model_for_batch()
@@ -322,7 +343,7 @@ def get_locations(self, t0_datetimes: pd.DatetimeIndex) -> Tuple[List[Number], L
     # ****************** METHODS THAT MUST BE OVERRIDDEN **********************
     # TODO: Issue #319: Standardise parameter names.
     def _get_time_slice(self, t0_dt: pd.Timestamp):
-        """Get a single timestep of data.  Must be overridden."""
+        """Get a single timestep of data.  Must be overridden if get_example is not overridden."""
         raise NotImplementedError()
 
     # TODO: Issue #319: Standardise parameter names.
@@ -335,12 +356,21 @@ def get_example(
         """Must be overridden by child classes."""
         raise NotImplementedError()
 
+    def check_input_paths_exist(self) -> None:
+        """Check any input paths exist.  Raise FileNotFoundError if not.
+
+        Must be overridden by child classes.
+        """
+        raise NotImplementedError()
+
 
 @dataclass
 class ImageDataSource(DataSource):
     """
     Image Data source
 
+    Note that this is an abstract class.
+
     Args:
       image_size_pixels: Size of the width and height of the image crop
         returned by get_sample().
diff --git a/nowcasting_dataset/data_sources/fake.py b/nowcasting_dataset/data_sources/fake.py
index c7b2db13..150f1d01 100644
--- a/nowcasting_dataset/data_sources/fake.py
+++ b/nowcasting_dataset/data_sources/fake.py
@@ -12,6 +12,7 @@
 from nowcasting_dataset.data_sources.gsp.gsp_model import GSP
 from nowcasting_dataset.data_sources.metadata.metadata_model import Metadata
 from nowcasting_dataset.data_sources.nwp.nwp_model import NWP
+from nowcasting_dataset.data_sources.optical_flow.optical_flow_model import OpticalFlow
 from nowcasting_dataset.data_sources.pv.pv_model import PV
 from nowcasting_dataset.data_sources.satellite.satellite_model import HRVSatellite, Satellite
 from nowcasting_dataset.data_sources.sun.sun_model import Sun
@@ -60,6 +61,8 @@ def metadata_fake(batch_size):
     # get random times
     all_datetimes = pd.date_range("2021-01-01", "2021-02-01", freq="5T")
     t0_datetimes_utc = np.random.choice(all_datetimes, batch_size, replace=False)
+    # np.random.choice turns the pd.Timestamp objects into datetime.datetime objects.
+    t0_datetimes_utc = pd.to_datetime(t0_datetimes_utc)
 
     metadata_dict = {}
     metadata_dict["batch_size"] = batch_size
@@ -164,6 +167,30 @@ def hrv_satellite_fake(
     return HRVSatellite(xr_dataset)
 
 
+def optical_flow_fake(
+    batch_size=32,
+    seq_length_5=19,
+    satellite_image_size_pixels=64,
+    number_satellite_channels=7,
+) -> OpticalFlow:
+    """Create fake data"""
+    # make batch of arrays
+    xr_arrays = [
+        create_image_array(
+            seq_length=seq_length_5,
+            freq="5T",
+            image_size_pixels=satellite_image_size_pixels,
+            channels=SAT_VARIABLE_NAMES[0:number_satellite_channels],
+        )
+        for _ in range(batch_size)
+    ]
+
+    # make dataset
+    xr_dataset = join_list_data_array_to_batch_dataset(xr_arrays)
+
+    return OpticalFlow(xr_dataset)
+
+
 def sun_fake(batch_size, seq_length_5):
     """Create fake data"""
     # create dataset with both azimuth and elevation, index with time
@@ -316,19 +343,20 @@ def create_gsp_pv_dataset(
     }
     coords = [(dim, ALL_COORDS[dim]) for dim in dims]
 
-    # make pv yield
-    data = np.random.randn(
-        seq_length,
-        number_of_systems,
-    )
-    data = data.clip(min=0)
+    # make pv yield.  randn samples from a Normal distribution (and so can go negative).
+    # The values are clipped to be positive later.
+    data = np.random.randn(seq_length, number_of_systems)
 
-    # smooth the data, the convolution method smooeths that data across systems first,
+    # smooth the data, the convolution method smooths that data across systems first,
     # and then a bit across time (depending what you set N)
     N = int(seq_length / 2)
     data = np.convolve(data.ravel(), np.ones(N) / N, mode="same").reshape(
         (seq_length, number_of_systems)
     )
+    # Need to clip  *after* smoothing, because the smoothing method might push
+    # non-zero data below zero.  Clip at 0.1 instead of 0 so we don't get div-by-zero errors
+    # if capacity is zero (capacity is computed as the max of the random numbers).
+    data = data.clip(min=0.1)
 
     # make into a Data Array
     data_array = xr.DataArray(
diff --git a/nowcasting_dataset/data_sources/gsp/eso.py b/nowcasting_dataset/data_sources/gsp/eso.py
index 4b8b1d9b..32b17c01 100644
--- a/nowcasting_dataset/data_sources/gsp/eso.py
+++ b/nowcasting_dataset/data_sources/gsp/eso.py
@@ -226,7 +226,7 @@ def get_list_of_gsp_ids(maximum_number_of_gsp: Optional[int] = None) -> List[int
     if maximum_number_of_gsp is None:
         maximum_number_of_gsp = len(metadata)
     if maximum_number_of_gsp > len(metadata):
-        logging.warning(f"Only {len(metadata)} gsp available to load")
+        logger.warning(f"Only {len(metadata)} gsp available to load")
     if maximum_number_of_gsp < len(metadata):
         gsp_ids = gsp_ids[0:maximum_number_of_gsp]
 
diff --git a/nowcasting_dataset/data_sources/metadata/__init__.py b/nowcasting_dataset/data_sources/metadata/__init__.py
deleted file mode 100644
index d95ccd84..00000000
--- a/nowcasting_dataset/data_sources/metadata/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-""" Metadata data sources and functions """
diff --git a/nowcasting_dataset/data_sources/metadata/metadata_model.py b/nowcasting_dataset/data_sources/metadata/metadata_model.py
index c18d8bb2..1e62c8eb 100644
--- a/nowcasting_dataset/data_sources/metadata/metadata_model.py
+++ b/nowcasting_dataset/data_sources/metadata/metadata_model.py
@@ -1,6 +1,5 @@
 """ Model for output of general/metadata data, useful for a batch """
 
-from datetime import datetime
 from typing import List
 
 import pandas as pd
@@ -21,7 +20,7 @@ class Metadata(BaseModel):
         "then this item stores one data item",
     )
 
-    t0_datetime_utc: List[datetime] = Field(
+    t0_datetime_utc: List[pd.Timestamp] = Field(
         ...,
         description="The t0s of each example ",
     )
diff --git a/nowcasting_dataset/data_sources/optical_flow/__init__.py b/nowcasting_dataset/data_sources/optical_flow/__init__.py
new file mode 100644
index 00000000..9a3ee67d
--- /dev/null
+++ b/nowcasting_dataset/data_sources/optical_flow/__init__.py
@@ -0,0 +1 @@
+""" Optical Flow data sources and functions """
diff --git a/nowcasting_dataset/data_sources/optical_flow/format_images.py b/nowcasting_dataset/data_sources/optical_flow/format_images.py
new file mode 100644
index 00000000..df8e012a
--- /dev/null
+++ b/nowcasting_dataset/data_sources/optical_flow/format_images.py
@@ -0,0 +1,67 @@
+""" Functions that format images """
+import cv2
+import numpy as np
+
+
+def remap_image(
+    image: np.ndarray,
+    flow: np.ndarray,
+    border_mode: int = cv2.BORDER_REPLICATE,
+) -> np.ndarray:
+    """
+    Takes an image and warps it forwards in time according to the flow field.
+
+    Args:
+        image: The grayscale image to warp.
+        flow: A 3D array.  The first two dimensions must be the same size as the first two
+            dimensions of the image.  The third dimension represented the x and y displacement.
+        border_mode: One of cv2's BorderTypes such as cv2.BORDER_CONSTANT or cv2.BORDER_REPLICATE.
+            If border_mode=cv2.BORDER_CONSTANT then the border will be set to -1.
+            For details of other border_mode settings, see the Open CV docs here:
+            docs.opencv.org/4.5.4/d2/de8/group__core__array.html#ga209f2f4869e304c82d07739337eae7c5
+
+    Returns:  Warped image.
+    """
+    # Adapted from https://github.com/opencv/opencv/issues/11068
+    height, width = flow.shape[:2]
+    remap = -flow.copy()
+    remap[..., 0] += np.arange(width)  # map_x
+    remap[..., 1] += np.arange(height)[:, np.newaxis]  # map_y
+    # remap docs:
+    # docs.opencv.org/4.5.4/da/d54/group__imgproc__transform.html#gab75ef31ce5cdfb5c44b6da5f3b908ea4
+    # TODO: Maybe use integer remap: docs say that might be faster?
+    remapped_image = cv2.remap(
+        src=image,
+        map1=remap,
+        map2=None,
+        interpolation=cv2.INTER_LINEAR,
+        borderMode=border_mode,
+        borderValue=-1,
+    )
+    return remapped_image
+
+
+def crop_center(image: np.ndarray, output_image_size_pixels: int) -> np.ndarray:
+    """
+    Crop center of a 2D numpy image.
+
+    Args:
+        image: The input image to crop.
+        output_image_size_pixels: The requested size of the output image.
+
+    Returns:
+        The cropped image, of size output_image_size_pixels x output_image_size_pixels
+    """
+    input_size_y, input_size_x = image.shape
+    assert (
+        input_size_x >= output_image_size_pixels
+    ), "output_image_size_pixels is larger than the input image!"
+    assert (
+        input_size_y >= output_image_size_pixels
+    ), "output_image_size_pixels is larger than the input image!"
+    half_output_image_size_pixels = output_image_size_pixels // 2
+    start_x = (input_size_x // 2) - half_output_image_size_pixels
+    start_y = (input_size_y // 2) - half_output_image_size_pixels
+    end_x = start_x + output_image_size_pixels
+    end_y = start_y + output_image_size_pixels
+    return image[start_y:end_y, start_x:end_x]
diff --git a/nowcasting_dataset/data_sources/optical_flow/optical_flow_data_source.py b/nowcasting_dataset/data_sources/optical_flow/optical_flow_data_source.py
new file mode 100644
index 00000000..71c1eb2b
--- /dev/null
+++ b/nowcasting_dataset/data_sources/optical_flow/optical_flow_data_source.py
@@ -0,0 +1,263 @@
+""" Optical Flow Data Source """
+import logging
+from dataclasses import dataclass
+from numbers import Number
+from pathlib import Path
+from typing import Iterable, Union
+
+import cv2
+import numpy as np
+import pandas as pd
+import xarray as xr
+
+import nowcasting_dataset.filesystem.utils as nd_fs_utils
+from nowcasting_dataset.data_sources import DataSource
+from nowcasting_dataset.data_sources.optical_flow.format_images import crop_center, remap_image
+from nowcasting_dataset.data_sources.optical_flow.optical_flow_model import OpticalFlow
+from nowcasting_dataset.dataset.xr_utils import convert_arrays_to_uint8
+
+_LOG = logging.getLogger(__name__)
+
+
+@dataclass
+class OpticalFlowDataSource(DataSource):
+    """
+    Optical Flow Data Source.
+
+    Predicts future satellite imagery by computing the "flow" between consecutive pairs of
+    satellite images and using that flow to "warp" the most recent satellite image (the "t0 image")
+    to predict future satellite images.
+
+    Optical flow is surprisingly effective at predicting future satellite images over time horizons
+    out to about 2 hours.  After 2 hours the predictions start to go a bit crazy.  There are some
+    notable problems with optical flow predictions:
+
+    1) Optical flow doesn't understand that clouds grow, shrink, appear from "nothing", and
+       disappear into "nothing".  Optical flow just moves pixels around.
+    2) Optical flow doesn't understand that satellite images tend to get brighter as the sun rises
+       and darker as the sun sets.
+
+    Arguments for the OpticalFlowDataSource constructor:
+
+    history_minutes: Duration of historical data to use when computing the optical flow field.
+        For example, set to 5 to use just two images: the t-1 and t0 images.  Set to 10 to compute
+        the optical flow field separately for the image pairs (t-2, t-1) and (t-1, t0) and to
+        use the mean optical flow field.
+    forecast_minutes: Duration of the optical flow predictions.
+    zarr_path: The location of the intermediate satellite data to compute optical flows with.
+    input_image_size_pixels: The *input* image size (i.e. the image size to load off disk).
+        This should be significantly larger than output_image_size_pixels to provide sufficient
+        border so that, even after the image has been "flowed", all edges of the output image are
+        "real" pixels values, and not NaNs.  For a forecast horizon of 120 minutes, and an output
+        image size of 24 pixels, we have found that the input image size needs to be at least
+        128 pixels.
+    output_image_size_pixels: The size of the output image.  The output image is a center-crop of
+        the input image after it has been "flowed".
+    source_data_source_class_name: Either HRVSatelliteDataSource or SatelliteDataSource.
+    channels: The satellite channels to compute optical flow for.
+    """
+
+    zarr_path: Union[Path, str]
+    channels: Iterable[str]
+    input_image_size_pixels: int = 64
+    meters_per_pixel: int = 2000
+    output_image_size_pixels: int = 32
+    source_data_source_class_name: str = "SatelliteDataSource"
+
+    def __post_init__(self):  # noqa
+        assert self.output_image_size_pixels <= self.input_image_size_pixels, (
+            "output_image_size_pixels must be equal to or smaller than input_image_size_pixels"
+            f" {self.output_image_size_pixels=}, {self.input_image_size_pixels=}"
+        )
+
+        super().__post_init__()
+
+        # Get round circular import problem
+        from nowcasting_dataset.data_sources import HRVSatelliteDataSource, SatelliteDataSource
+
+        _MAP_SATELLITE_DATA_SOURCE_NAME_TO_CLASS = {
+            "HRVSatelliteDataSource": HRVSatelliteDataSource,
+            "SatelliteDataSource": SatelliteDataSource,
+        }
+
+        source_data_source_class = _MAP_SATELLITE_DATA_SOURCE_NAME_TO_CLASS[
+            self.source_data_source_class_name
+        ]
+        self.source_data_source = source_data_source_class(
+            zarr_path=self.zarr_path,
+            image_size_pixels=self.input_image_size_pixels,
+            history_minutes=self.history_minutes,
+            forecast_minutes=0,
+            channels=self.channels,
+            meters_per_pixel=self.meters_per_pixel,
+        )
+
+    def open(self):
+        """Open the underlying self.source_data_source."""
+        self.source_data_source.open()
+
+    def get_example(
+        self, t0_dt: pd.Timestamp, x_meters_center: Number, y_meters_center: Number
+    ) -> xr.Dataset:
+        """
+        Get Optical Flow Example data
+
+        Args:
+            t0_dt: list of timestamps for the datetime of the batches. The batch will also include
+                data for historic and future depending on `history_minutes` and `future_minutes`.
+            x_meters_center: x center batch locations
+            y_meters_center: y center batch locations
+
+        Returns: Example Data
+
+        """
+        satellite_data: xr.Dataset = self.source_data_source.get_example(
+            t0_dt=t0_dt, x_meters_center=x_meters_center, y_meters_center=y_meters_center
+        )
+        satellite_data = satellite_data["data"]
+        optical_flow_data_array = self._compute_and_return_optical_flow(satellite_data)
+        return optical_flow_data_array.to_dataset()
+
+    @staticmethod
+    def get_data_model_for_batch():
+        """Get the model that is used in the batch"""
+        return OpticalFlow
+
+    def _put_predictions_into_data_array(
+        self,
+        satellite_data: xr.DataArray,
+        predictions: np.ndarray,
+    ) -> xr.DataArray:
+        """
+        Puts optical flow predictions into an xr.DataArray.
+
+        Args:
+            satellite_data: Satellite data
+            predictions: Predictions from the optical flow
+
+        Returns:
+            The Xarray DataArray with the optical flow predictions
+        """
+        # Generate a pd.DatetimeIndex for the optical flow predictions.
+        t0_datetime_utc = satellite_data.isel(time=-1)["time"].values
+        t1_datetime_utc = t0_datetime_utc + self.sample_period_duration
+        datetime_index_of_predictions = pd.date_range(
+            t1_datetime_utc, periods=self.forecast_length, freq=self.sample_period_duration
+        )
+
+        # Select the center crop.
+        satellite_data_cropped = satellite_data.isel(time=0, channels=0)
+        satellite_data_cropped = crop_center(satellite_data_cropped, self.output_image_size_pixels)
+
+        # Put into DataArray:
+        return xr.DataArray(
+            data=predictions,
+            coords=(
+                ("time", datetime_index_of_predictions),
+                ("x", satellite_data_cropped.coords["x"].values),
+                ("y", satellite_data_cropped.coords["y"].values),
+                ("channels", satellite_data.coords["channels"].values),
+            ),
+            name="data",
+        )
+
+    def _compute_and_return_optical_flow(self, satellite_data: xr.DataArray) -> xr.DataArray:
+        """
+        Compute and return optical flow predictions for the example
+
+        Args:
+            satellite_data: Satellite DataArray of historical satellite images, up to and include t0
+
+        Returns:
+            DataArray with the optical flow predictions from t1 to the forecast horizon.
+        """
+        n_channels = satellite_data.sizes["channels"]
+
+        # Sanity check
+        assert (
+            len(satellite_data.coords["time"]) == self.history_length + 1
+        ), f"{len(satellite_data.coords['time'])=} != {self.history_length+1=}"
+        assert n_channels == len(self.channels), f"{n_channels=} != {len(self.channels)=}"
+
+        # Pre-allocate an array, into which our optical flow prediction will be placed.
+        prediction_block = np.full(
+            shape=(
+                self.forecast_length,
+                self.output_image_size_pixels,
+                self.output_image_size_pixels,
+                n_channels,
+            ),
+            fill_value=-1,
+            dtype=np.int16,
+        )
+
+        # Compute flow fields and optical flow predictions separately for each satellite channel
+        # because the different channels represent different physical phenomena and so,
+        # in principle, could move in different directions (e.g. water vapour vs high clouds).
+        for channel_i in range(n_channels):
+            # Compute optical flow field:
+            sat_data_for_chan = satellite_data.isel(channels=channel_i)
+
+            # Loop through pairs of historical images to compute optical flow fields for each
+            # pair of consecutive satellite images, and then compute the mean of those flow fields.
+            optical_flows = []
+            # self.history_length does not include t0.
+            for history_timestep in range(self.history_length):
+                prev_image = sat_data_for_chan.isel(time=history_timestep).data
+                next_image = sat_data_for_chan.isel(time=history_timestep + 1).data
+                optical_flow = compute_optical_flow(prev_image, next_image)
+                optical_flows.append(optical_flow)
+            optical_flow = np.mean(optical_flows, axis=0)
+
+            # Compute predicted images.
+            t0_image = sat_data_for_chan.isel(time=-1).data
+            for prediction_timestep in range(self.forecast_length):
+                flow = optical_flow * (prediction_timestep + 1)
+                warped_image = remap_image(image=t0_image, flow=flow)
+                warped_image = crop_center(warped_image, self.output_image_size_pixels)
+                prediction_block[prediction_timestep, :, :, channel_i] = warped_image
+
+        data_array = self._put_predictions_into_data_array(
+            satellite_data=satellite_data, predictions=prediction_block
+        )
+        return data_array
+
+    def check_input_paths_exist(self) -> None:
+        """Check input paths exist.  If not, raise a FileNotFoundError."""
+        nd_fs_utils.check_path_exists(self.zarr_path)
+
+
+def compute_optical_flow(prev_image: np.ndarray, next_image: np.ndarray) -> np.ndarray:
+    """
+    Compute the optical flow for a set of images
+
+    Args:
+        prev_image, next_image: A pair of images representing two timesteps.  This algorithm
+            will estimate the "movement" across these two timesteps.  Both images must be the
+            same dtype.
+
+    Returns:
+        Dense optical flow field: A 3D array.  The first two dimension are the same size as the
+            input images.  The third dimension is of size 2 and represents the
+            displacement in x and y.
+    """
+    assert prev_image.dtype == next_image.dtype, "Images must be the same dtype!"
+
+    # cv2.calcOpticalFlowFarneback expects images to be uint8:
+    prev_image, next_image = convert_arrays_to_uint8(prev_image, next_image)
+
+    # Docs for cv2.calcOpticalFlowFarneback:
+    # https://docs.opencv.org/4.5.4/dc/d6b/group__video__track.html#ga5d10ebbd59fe09c5f650289ec0ece5af
+    flow = cv2.calcOpticalFlowFarneback(
+        prev=prev_image,
+        next=next_image,
+        flow=None,
+        pyr_scale=0.5,
+        levels=2,
+        winsize=40,
+        iterations=3,
+        poly_n=5,
+        poly_sigma=0.7,
+        flags=cv2.OPTFLOW_FARNEBACK_GAUSSIAN,
+    )
+    return flow
diff --git a/nowcasting_dataset/data_sources/optical_flow/optical_flow_model.py b/nowcasting_dataset/data_sources/optical_flow/optical_flow_model.py
new file mode 100644
index 00000000..1f410e05
--- /dev/null
+++ b/nowcasting_dataset/data_sources/optical_flow/optical_flow_model.py
@@ -0,0 +1,22 @@
+""" Model for output of Optical Flow data """
+from __future__ import annotations
+
+import numpy as np
+
+from nowcasting_dataset.data_sources.datasource_output import DataSourceOutput
+
+
+class OpticalFlow(DataSourceOutput):
+    """Class to store optical flow data as a xr.Dataset with some validation"""
+
+    __slots__ = ()
+    _expected_dimensions = ("time", "x", "y", "channels")
+    _expected_data_vars = ("data",)
+
+    @classmethod
+    def model_validation(cls, v):
+        """Check that all values are not NaN, Infinite, or -1."""
+        assert (~np.isnan(v.data)).all(), "Some optical flow data values are NaNs"
+        assert (~np.isinf(v.data)).all(), "Some optical flow data values are Infinite"
+        assert (v.data != -1).all(), "Some optical flow data values are -1"
+        return v
diff --git a/nowcasting_dataset/data_sources/satellite/satellite_data_source.py b/nowcasting_dataset/data_sources/satellite/satellite_data_source.py
index bf9caf5c..3ef50e5f 100644
--- a/nowcasting_dataset/data_sources/satellite/satellite_data_source.py
+++ b/nowcasting_dataset/data_sources/satellite/satellite_data_source.py
@@ -14,7 +14,7 @@
 from nowcasting_dataset.data_sources.data_source import ZarrDataSource
 from nowcasting_dataset.data_sources.satellite.satellite_model import Satellite
 
-_LOG = logging.getLogger("nowcasting_dataset")
+_LOG = logging.getLogger(__name__)
 
 
 @dataclass
@@ -27,6 +27,9 @@ class SatelliteDataSource(ZarrDataSource):
 
     def __post_init__(self, image_size_pixels: int, meters_per_pixel: int):
         """Post Init"""
+        assert len(self.channels) > 0, "channels cannot be empty!"
+        assert image_size_pixels > 0, "image_size_pixels cannot be <= 0!"
+        assert meters_per_pixel > 0, "meters_per_pixel cannot be <= 0!"
         super().__post_init__(image_size_pixels, meters_per_pixel)
         n_channels = len(self.channels)
         self._shape_of_example = (
@@ -46,9 +49,15 @@ def open(self) -> None:
         call open() _after_ creating separate processes.
         """
         self._data = self._open_data()
-        self._data = self._data.sel(variable=list(self.channels))
         if "variable" in self._data.dims:
             self._data = self._data.rename({"variable": "channels"})
+        if not set(self.channels).issubset(self._data.channels.values):
+            raise RuntimeError(
+                f"One or more requested channels are not available in {self.zarr_path}!"
+                f"  Requested channels={self.channels}."
+                f"  Available channels={self._data.channels.values}"
+            )
+        self._data = self._data.sel(channels=list(self.channels))
 
     def _open_data(self) -> xr.DataArray:
         return open_sat_data(zarr_path=self.zarr_path, consolidated=self.consolidated)
@@ -86,23 +95,48 @@ def get_spatial_region_of_interest(
         Returns:
             The selected data around the center
         """
-        x_index = (
-            np.searchsorted(data_array.x.values, x_center_osgb) - 1
-        )  # To have the center fall within the pixel
-        y_index = np.searchsorted(data_array.y.values, y_center_osgb) - 1
-        min_y = y_index - (self._square.size_pixels // 2)
-        min_x = x_index - (self._square.size_pixels // 2)
-        assert min_y >= 0, (
-            f"Y location must be at least {(self._square.size_pixels // 2)} "
-            f"pixels from the edge of the area, but is {y_index} for y center of {y_center_osgb}"
-        )
-        assert min_x >= 0, (
-            f"X location must be at least {(self._square.size_pixels // 2)}"
-            f" pixels from the edge of the area, but is {x_index} for x center of {x_center_osgb}"
+        # Get the index into x and y nearest to x_center_osgb and y_center_osgb:
+        x_index_at_center = np.searchsorted(data_array.x.values, x_center_osgb) - 1
+        y_index_at_center = np.searchsorted(data_array.y.values, y_center_osgb) - 1
+        # Put x_index_at_center and y_index_at_center into a pd.Series so we can operate
+        # on them both in a single line of code.
+        x_and_y_index_at_center = pd.Series({"x": x_index_at_center, "y": y_index_at_center})
+        half_image_size_pixels = self._square.size_pixels // 2
+        min_x_and_y_index = x_and_y_index_at_center - half_image_size_pixels
+        max_x_and_y_index = x_and_y_index_at_center + half_image_size_pixels
+
+        # Check whether the requested region of interest steps outside of the available data:
+        suggested_reduction_of_image_size_pixels = (
+            max(
+                (-min_x_and_y_index.min() if (min_x_and_y_index < 0).any() else 0),
+                (max_x_and_y_index.x - len(data_array.x)),
+                (max_x_and_y_index.y - len(data_array.y)),
+            )
+            * 2
         )
+        # If the requested region does step outside the available data then raise an exception
+        # with a helpful message:
+        if suggested_reduction_of_image_size_pixels > 0:
+            new_suggested_image_size_pixels = (
+                self._square.size_pixels - suggested_reduction_of_image_size_pixels
+            )
+            raise RuntimeError(
+                "Requested region of interest of satellite data steps outside of the available"
+                " geographical extent of the Zarr data.  The requested region of interest extends"
+                f" from pixel indicies"
+                f" x={min_x_and_y_index.x} to x={max_x_and_y_index.x},"
+                f" y={min_x_and_y_index.y} to y={max_x_and_y_index.y}.  In the Zarr data,"
+                f" len(x)={len(data_array.x)}, len(y)={len(data_array.y)}. Try reducing"
+                f" image_size_pixels from {self._square.size_pixels} to"
+                f" {new_suggested_image_size_pixels} pixels."
+            )
+
+        # Select the geographical region of interest.
+        # Note that isel is *exclusive* of the end of the slice.
+        # e.g. isel(x=slice(0, 3)) will return the first, second, and third values.
         data_array = data_array.isel(
-            x=slice(min_x, min_x + self._square.size_pixels),
-            y=slice(min_y, min_y + self._square.size_pixels),
+            x=slice(min_x_and_y_index.x, max_x_and_y_index.x),
+            y=slice(min_x_and_y_index.y, max_x_and_y_index.y),
         )
         return data_array
 
diff --git a/nowcasting_dataset/data_sources/sun/raw_data_load_save.py b/nowcasting_dataset/data_sources/sun/raw_data_load_save.py
index 355a8732..f3ed3b30 100644
--- a/nowcasting_dataset/data_sources/sun/raw_data_load_save.py
+++ b/nowcasting_dataset/data_sources/sun/raw_data_load_save.py
@@ -141,13 +141,8 @@ def load_from_zarr(
         The index is timestamps, and the columns are the x and y coordinates
 
     """
-    logger.debug("Loading sun data")
+    logger.debug(f"Loading sun data from {zarr_path}")
 
-    # It is possible to simplify the code below and do
-    # xr.open_dataset(file, engine='h5netcdf')
-    # in the first 'with' block, and delete the second 'with' block.
-    # But that takes 1 minute to load the data, where as loading into memory
-    # first and then loading from memory takes 23 seconds!
     sun = xr.open_dataset(zarr_path, engine="zarr")
 
     if (start_dt is not None) and (end_dt is not None):
diff --git a/nowcasting_dataset/dataset/batch.py b/nowcasting_dataset/dataset/batch.py
index b5184bcb..20fd74ef 100644
--- a/nowcasting_dataset/dataset/batch.py
+++ b/nowcasting_dataset/dataset/batch.py
@@ -17,6 +17,7 @@
     hrv_satellite_fake,
     metadata_fake,
     nwp_fake,
+    optical_flow_fake,
     pv_fake,
     satellite_fake,
     sun_fake,
@@ -25,6 +26,7 @@
 from nowcasting_dataset.data_sources.gsp.gsp_model import GSP
 from nowcasting_dataset.data_sources.metadata.metadata_model import Metadata, load_from_csv
 from nowcasting_dataset.data_sources.nwp.nwp_model import NWP
+from nowcasting_dataset.data_sources.optical_flow.optical_flow_model import OpticalFlow
 from nowcasting_dataset.data_sources.pv.pv_model import PV
 from nowcasting_dataset.data_sources.satellite.satellite_model import HRVSatellite, Satellite
 from nowcasting_dataset.data_sources.sun.sun_model import Sun
@@ -52,6 +54,7 @@ class Batch(BaseModel):
     satellite: Optional[Satellite]
     hrvsatellite: Optional[HRVSatellite]
     topographic: Optional[Topographic]
+    opticalflow: Optional[OpticalFlow]
     pv: Optional[PV]
     sun: Optional[Sun]
     gsp: Optional[GSP]
@@ -64,6 +67,7 @@ def data_sources(self):
             self.satellite,
             self.hrvsatellite,
             self.topographic,
+            self.opticalflow,
             self.pv,
             self.sun,
             self.gsp,
@@ -93,6 +97,14 @@ def fake(configuration: Configuration):
                 satellite_image_size_pixels=satellite_image_size_pixels,
                 number_satellite_channels=1,
             ),
+            opticalflow=optical_flow_fake(
+                batch_size=batch_size,
+                seq_length_5=configuration.input_data.satellite.seq_length_5_minutes,
+                satellite_image_size_pixels=satellite_image_size_pixels,
+                number_satellite_channels=len(
+                    configuration.input_data.satellite.satellite_channels
+                ),
+            ),
             nwp=nwp_fake(
                 batch_size=batch_size,
                 seq_length_60=configuration.input_data.nwp.seq_length_60_minutes,
@@ -127,7 +139,6 @@ def save_netcdf(self, batch_i: int, path: Path):
             path: the path where it will be saved. This can be local or in the cloud.
 
         """
-
         with futures.ThreadPoolExecutor() as executor:
             # Submit tasks to the executor.
             for data_source in self.data_sources:
@@ -162,13 +173,19 @@ def load_netcdf(
                 local_netcdf_filename = os.path.join(
                     local_netcdf_path, data_source_name, get_netcdf_filename(batch_idx)
                 )
-
-                # submit task
-                future_examples = executor.submit(
-                    xr.load_dataset,
-                    filename_or_obj=local_netcdf_filename,
-                )
-                future_examples_per_source.append([data_source_name, future_examples])
+                # If the file exists, load it, otherwise data source isn't used
+                if os.path.isfile(local_netcdf_filename):
+                    # submit task
+                    future_examples = executor.submit(
+                        xr.load_dataset,
+                        filename_or_obj=local_netcdf_filename,
+                    )
+                    future_examples_per_source.append([data_source_name, future_examples])
+                else:
+                    _LOG.error(
+                        f"{local_netcdf_filename} does not exists,"
+                        f"this is for {data_source_name} data source"
+                    )
 
         # Collect results from each thread.
         for data_source_name, future_examples in future_examples_per_source:
@@ -198,6 +215,7 @@ class Example(BaseModel):
     satellite: Optional[Satellite]
     hrvsatellite: Optional[HRVSatellite]
     topographic: Optional[Topographic]
+    opticalflow: Optional[OpticalFlow]
     pv: Optional[PV]
     sun: Optional[Sun]
     gsp: Optional[GSP]
@@ -209,6 +227,7 @@ def data_sources(self):
         return [
             self.satellite,
             self.hrvsatellite,
+            self.opticalflow,
             self.topographic,
             self.pv,
             self.sun,
diff --git a/nowcasting_dataset/dataset/xr_utils.py b/nowcasting_dataset/dataset/xr_utils.py
index 79c7c75b..def4a11e 100644
--- a/nowcasting_dataset/dataset/xr_utils.py
+++ b/nowcasting_dataset/dataset/xr_utils.py
@@ -123,3 +123,28 @@ def validate_data_vars(cls, v: Any) -> Any:
                 data_var in data_var_names
             ), f"{data_var} is not in all data_vars ({data_var_names}) in {cls.__name__}!"
         return v
+
+
+def convert_arrays_to_uint8(*arrays: tuple[np.ndarray]) -> tuple[np.ndarray]:
+    """Convert multiple arrays to uint8, using the same min and max to scale all arrays."""
+    # First, stack into a single numpy array so we can work on all images at the same time:
+    stacked = np.stack(arrays)
+
+    # Convert to float64 for normalisation:
+    stacked = stacked.astype(np.float64)
+
+    # Rescale pixel values to be in the range [0, 1]:
+    stacked -= stacked.min()
+    stacked_max = stacked.max()
+    if stacked_max > 0.0:
+        # If there is still an invalid value then we want to know about it!
+        # Adapted from https://stackoverflow.com/a/33701974/732596
+        with np.errstate(all="raise"):
+            stacked /= stacked.max()
+
+    # Convert to uint8 (uint8 can represent integers in the range [0, 255]):
+    stacked *= 255
+    stacked = stacked.round()
+    stacked = stacked.astype(np.uint8)
+
+    return tuple(stacked)
diff --git a/nowcasting_dataset/filesystem/utils.py b/nowcasting_dataset/filesystem/utils.py
index c563b4e1..e5b05f1f 100644
--- a/nowcasting_dataset/filesystem/utils.py
+++ b/nowcasting_dataset/filesystem/utils.py
@@ -7,7 +7,7 @@
 import numpy as np
 from pathy import Pathy
 
-_LOG = logging.getLogger("nowcasting_dataset")
+_LOG = logging.getLogger(__name__)
 
 
 def upload_and_delete_local_files(dst_path: Union[str, Path], local_path: Union[str, Path]):
@@ -97,6 +97,8 @@ def check_path_exists(path: Union[str, Path]):
 
     `path` can include wildcards.
     """
+    if not path:
+        raise FileNotFoundError("Not a valid path!")
     filesystem = get_filesystem(path)
     if not filesystem.exists(path):
         # Now try using `glob`.  Maybe `path` includes a wildcard?
diff --git a/nowcasting_dataset/manager.py b/nowcasting_dataset/manager.py
index 50644f8b..a2d20274 100644
--- a/nowcasting_dataset/manager.py
+++ b/nowcasting_dataset/manager.py
@@ -2,6 +2,7 @@
 
 import logging
 import multiprocessing
+from functools import partial
 from pathlib import Path
 from typing import Optional, Union
 
@@ -33,7 +34,6 @@ class Manager:
         geospatial locations of each example.
       save_batches_locally_and_upload: bool: Set to True by `load_yaml_configuration()` if
         `config.process.upload_every_n_batches > 0`.
-      local_temp_path: Path: `config.process.local_temp_path` with `~` expanded.
     """
 
     def __init__(self) -> None:  # noqa: D107
@@ -47,8 +47,6 @@ def load_yaml_configuration(self, filename: str) -> None:
         self.config = config.load_yaml_configuration(filename)
         self.config = config.set_git_commit(self.config)
         self.save_batches_locally_and_upload = self.config.process.upload_every_n_batches > 0
-
-        self.local_temp_path = self.config.process.local_temp_path
         logger.debug(f"config={self.config}")
 
     def save_yaml_configuration(self):
@@ -87,6 +85,7 @@ def configure_loggers(
             log_filename = self.config.output_data.filepath / f"{data_source_name}.log"
             nd_utils.configure_logger(
                 log_level=log_level,
+                # TODO: Fix bug #467: satellite.log file is not being appended to.
                 logger_name=f"nowcasting_dataset.data_sources.{data_source_name}",
                 handlers=[logging.FileHandler(log_filename, mode="a")],
             )
@@ -94,7 +93,7 @@ def configure_loggers(
     def initialise_data_sources(
         self, names_of_selected_data_sources: Optional[list[str]] = ALL_DATA_SOURCE_NAMES
     ) -> None:
-        """Initialise DataSources specified in the InputData configuration.
+        """Initialize DataSources specified in the InputData configuration.
 
         For each key in each DataSource's configuration object, the string `<data_source_name>_`
         is removed from the key before passing to the DataSource constructor.  This allows us to
@@ -234,7 +233,7 @@ def _locations_csv_file_exists(self) -> bool:
         try:
             nd_fs_utils.check_path_exists(filename)
         except FileNotFoundError:
-            logging.info(f"{filename} does not exist!")
+            logger.info(f"{filename} does not exist!")
             return False
         else:
             logger.info(f"{filename} exists!")
@@ -371,16 +370,18 @@ def _check_if_more_batches_are_required_for_split(
         return False
 
     def _find_splits_which_need_more_batches(
-        self, first_batches_to_create: dict[split.SplitName, dict[str, int]]
+        self,
+        first_batches_to_create: dict[split.SplitName, dict[str, int]],
     ) -> list[split.SplitName]:
         """Returns list of SplitNames which need more batches to be produced."""
-        splits_which_need_more_batches = []
-        for split_name in split.SplitName:
+        return [
+            split_name
+            for split_name in split.SplitName
             if self._check_if_more_batches_are_required_for_split(
-                split_name, first_batches_to_create
-            ):
-                splits_which_need_more_batches.append(split_name)
-        return splits_which_need_more_batches
+                split_name=split_name,
+                first_batches_to_create=first_batches_to_create,
+            )
+        ]
 
     def create_batches(self, overwrite_batches: bool) -> None:
         """Create batches (if necessary).
@@ -394,6 +395,7 @@ def create_batches(self, overwrite_batches: bool) -> None:
             previously been written to disk. If False then check which batches have previously been
             written to disk, and only create any batches which have not yet been written to disk.
         """
+        logger.debug("Entering Manager.create_batches...")
         first_batches_to_create = self._get_first_batches_to_create(overwrite_batches)
 
         # Check if there's any work to do.
@@ -405,7 +407,7 @@ def create_batches(self, overwrite_batches: bool) -> None:
             ]
         else:
             splits_which_need_more_batches = self._find_splits_which_need_more_batches(
-                first_batches_to_create
+                first_batches_to_create=first_batches_to_create
             )
             if len(splits_which_need_more_batches) == 0:
                 logger.info("All batches have already been created!  No work to do!")
@@ -436,6 +438,7 @@ def create_batches(self, overwrite_batches: bool) -> None:
         for split_name, locations_for_split in locations_for_each_example_of_each_split.items():
             with multiprocessing.Pool(processes=n_data_sources) as pool:
                 async_results_from_create_batches = []
+                an_error_has_occured = multiprocessing.Event()
                 for worker_id, (data_source_name, data_source) in enumerate(
                     self.data_sources.items()
                 ):
@@ -452,7 +455,7 @@ def create_batches(self, overwrite_batches: bool) -> None:
 
                     # TODO: Issue 455: Guarantee that local temp path is unique and empty.
                     local_temp_path = (
-                        self.local_temp_path
+                        self.config.process.local_temp_path
                         / split_name.value
                         / data_source_name
                         / f"worker_{worker_id}"
@@ -474,13 +477,24 @@ def create_batches(self, overwrite_batches: bool) -> None:
                     )
 
                     # Logger messages for callbacks:
-                    callback_msg = (
-                        f"{data_source_name} has finished created batches for {split_name}!"
-                    )
-                    error_callback_msg = (
-                        f"Exception raised by {data_source_name} whilst creating batches for"
-                        f" {split_name}:\n"
-                    )
+                    def _callback(result):
+                        """Create callback for 'pool.apply_async'"""
+                        logger.info(
+                            f"{data_source_name} has finished created batches for {split_name}!"
+                        )
+
+                    def _error_callback(exception, data_source_name):
+                        """Create error callback for 'pool.apply_async'
+
+                        Need to pass in data_source_name rather than rely on data_source_name
+                        in the outer scope, because otherwise the error message will contain
+                        the wrong data_source_name (due to stuff happening concurrently!)
+                        """
+                        logger.exception(
+                            f"Exception raised by {data_source_name} whilst creating batches for"
+                            f" {split_name.value}\n{exception.__class__.__name__}: {exception}"
+                        )
+                        an_error_has_occured.set()
 
                     # Submit data_source.create_batches task to the worker process.
                     logger.debug(
@@ -489,15 +503,20 @@ def create_batches(self, overwrite_batches: bool) -> None:
                     async_result = pool.apply_async(
                         data_source.create_batches,
                         kwds=kwargs_for_create_batches,
-                        callback=lambda result: logger.info(callback_msg),
-                        error_callback=lambda exception: logger.error(
-                            error_callback_msg + str(exception)
-                        ),
+                        callback=_callback,
+                        error_callback=partial(_error_callback, data_source_name=data_source_name),
                     )
                     async_results_from_create_batches.append(async_result)
 
                 # Wait for all async_results to finish:
                 for async_result in async_results_from_create_batches:
                     async_result.wait()
+                    if an_error_has_occured.is_set():
+                        # An error has occurred but, at this point in the code, we don't know which
+                        # worker process raised the exception.  But, with luck, the worker process
+                        # will have logged an informative exception via the _error_callback func.
+                        raise RuntimeError(
+                            f"A worker process raised an exception whilst working on {split_name}!"
+                        )
 
                 logger.info(f"Finished creating batches for {split_name}!")
diff --git a/nowcasting_dataset/utils.py b/nowcasting_dataset/utils.py
index 00a630c0..a7f61337 100644
--- a/nowcasting_dataset/utils.py
+++ b/nowcasting_dataset/utils.py
@@ -3,6 +3,8 @@
 import os
 import re
 import tempfile
+import threading
+from concurrent import futures
 from functools import wraps
 
 import fsspec.asyn
@@ -149,9 +151,7 @@ def arg_logger(func):
     # Adapted from https://stackoverflow.com/a/23983263/732596
     @wraps(func)
     def inner_func(*args, **kwargs):
-        logger.debug(
-            f"Arguments passed into function `{func.__name__}`:" f" args={args}; kwargs={kwargs}"
-        )
+        logger.debug(f"Arguments passed into function `{func.__name__}`: {args=}; {kwargs=}")
         return func(*args, **kwargs)
 
     return inner_func
@@ -169,7 +169,7 @@ def configure_logger(log_level: str, logger_name: str, handlers=list[logging.Han
     log_level = getattr(logging, log_level)  # Convert string to int.
 
     formatter = logging.Formatter(
-        "%(asctime)s %(levelname)s processID=%(process)d %(message)s | %(pathname)s#L%(lineno)d"
+        "%(asctime)s:%(levelname)s:%(module)s#L%(lineno)d:PID=%(process)d:%(message)s"
     )
 
     local_logger = logging.getLogger(logger_name)
@@ -196,3 +196,38 @@ def get_start_and_end_example_index(batch_idx: int, batch_size: int) -> (int, in
     end_example_idx = (batch_idx + 1) * batch_size
 
     return start_example_idx, end_example_idx
+
+
+class DummyExecutor(futures.Executor):
+    """Drop-in replacement for ThreadPoolExecutor or ProcessPoolExecutor
+
+    This is currently not used in any code, but very useful when debugging.
+
+    Adapted from https://stackoverflow.com/a/10436851/732596
+    """
+
+    def __init__(self, *args, **kwargs):
+        """Initialise DummyExecutor."""
+        self._shutdown = False
+        self._shutdownLock = threading.Lock()
+
+    def submit(self, fn, *args, **kwargs):
+        """Submit task to DummyExecutor."""
+        with self._shutdownLock:
+            if self._shutdown:
+                raise RuntimeError("cannot schedule new futures after shutdown")
+
+            f = futures.Future()
+            try:
+                result = fn(*args, **kwargs)
+            except BaseException as e:
+                f.set_exception(e)
+            else:
+                f.set_result(result)
+
+            return f
+
+    def shutdown(self, wait=True):
+        """Shutdown dummy executor."""
+        with self._shutdownLock:
+            self._shutdown = True
diff --git a/requirements.txt b/requirements.txt
index 02bffdb5..683efc61 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -22,4 +22,5 @@ black
 pre-commit
 fsspec
 pathy
+opencv-contrib-python-headless
 gitpython
diff --git a/tests/config/nwp_size_test.yaml b/tests/config/nwp_size_test.yaml
index 176a08a5..9bb3ea4e 100644
--- a/tests/config/nwp_size_test.yaml
+++ b/tests/config/nwp_size_test.yaml
@@ -22,6 +22,14 @@ input_data:
     sun_zarr_path: tests/data/sun/test.zarr
   topographic:
     topographic_filename: tests/data/europe_dem_2km_osgb.tif
+  opticalflow:
+    history_minutes: 5
+    forecast_minutes: 30
+    opticalflow_zarr_path: tests/data/sat_data.zarr
+    opticalflow_input_image_size_pixels: 32
+    opticalflow_output_image_size_pixels: 8
+    opticalflow_channels:
+      - IR_016
 output_data:
   filepath: not used by unittests!
 process:
diff --git a/tests/config/test.yaml b/tests/config/test.yaml
index 688d6e52..3af1bc16 100644
--- a/tests/config/test.yaml
+++ b/tests/config/test.yaml
@@ -32,6 +32,14 @@ input_data:
     sun_zarr_path: tests/data/sun/test.zarr
   topographic:
     topographic_filename: tests/data/europe_dem_2km_osgb.tif
+  opticalflow:
+    history_minutes: 5
+    forecast_minutes: 30
+    opticalflow_zarr_path: tests/data/sat_data.zarr
+    opticalflow_input_image_size_pixels: 32
+    opticalflow_output_image_size_pixels: 8
+    opticalflow_channels:
+      - IR_016
 output_data:
   filepath: not used by unittests!
 process:
diff --git a/tests/data_sources/optical_flow/test_optical_flow_data_source.py b/tests/data_sources/optical_flow/test_optical_flow_data_source.py
new file mode 100644
index 00000000..ac89ae04
--- /dev/null
+++ b/tests/data_sources/optical_flow/test_optical_flow_data_source.py
@@ -0,0 +1,48 @@
+"""Test Optical Flow Data Source"""
+from pathlib import Path
+
+import pandas as pd
+import pytest
+
+from nowcasting_dataset.config.model import Configuration, InputData
+from nowcasting_dataset.data_sources.optical_flow.optical_flow_data_source import (
+    OpticalFlowDataSource,
+)
+
+
+@pytest.fixture
+def optical_flow_configuration():  # noqa: D103
+    con = Configuration()
+    con.input_data = InputData.set_all_to_defaults()
+    con.process.batch_size = 4
+    con.input_data.satellite.forecast_minutes = 60
+    con.input_data.satellite.history_minutes = 30
+    return con
+
+
+def _get_optical_flow_data_source(
+    sat_filename: Path, history_minutes: int = 5
+) -> OpticalFlowDataSource:
+    return OpticalFlowDataSource(
+        zarr_path=sat_filename,
+        channels=("IR_016",),
+        history_minutes=history_minutes,
+        forecast_minutes=120,
+        input_image_size_pixels=64,
+        output_image_size_pixels=32,
+    )
+
+
+@pytest.mark.parametrize("history_minutes", [5, 15])
+def test_optical_flow_get_example(
+    optical_flow_configuration, sat_filename: Path, history_minutes: int
+):  # noqa: D103
+    optical_flow_datasource = _get_optical_flow_data_source(
+        sat_filename=sat_filename, history_minutes=history_minutes
+    )
+    optical_flow_datasource.open()
+    t0_dt = pd.Timestamp("2020-04-01T13:00")
+    example = optical_flow_datasource.get_example(
+        t0_dt=t0_dt, x_meters_center=10_000, y_meters_center=10_000
+    )
+    assert example["data"].shape == (24, 32, 32, 1)  # timesteps, height, width, channels
diff --git a/tests/data_sources/optical_flow/test_optical_flow_model.py b/tests/data_sources/optical_flow/test_optical_flow_model.py
new file mode 100644
index 00000000..5eb405a9
--- /dev/null
+++ b/tests/data_sources/optical_flow/test_optical_flow_model.py
@@ -0,0 +1,31 @@
+"""Test Optical Flow model."""
+import os
+import tempfile
+
+import numpy as np
+import pytest
+
+from nowcasting_dataset.data_sources.fake import optical_flow_fake
+from nowcasting_dataset.data_sources.optical_flow.optical_flow_model import OpticalFlow
+
+
+def test_optical_flow_init():  # noqa: D103
+    _ = optical_flow_fake()
+
+
+def test_optical_flow_validation():  # noqa: D103
+    sat = optical_flow_fake()
+
+    OpticalFlow.model_validation(sat)
+
+    sat.data[0, 0] = np.nan
+    with pytest.raises(Exception):
+        OpticalFlow.model_validation(sat)
+
+
+def test_optical_flow_save():  # noqa: D103
+
+    with tempfile.TemporaryDirectory() as dirpath:
+        optical_flow_fake().save_netcdf(path=dirpath, batch_i=0)
+
+        assert os.path.exists(f"{dirpath}/opticalflow/000000.nc")
diff --git a/tests/data_sources/test_data_source.py b/tests/data_sources/test_data_source.py
deleted file mode 100644
index e4a51f16..00000000
--- a/tests/data_sources/test_data_source.py
+++ /dev/null
@@ -1,10 +0,0 @@
-from nowcasting_dataset.data_sources.data_source import ImageDataSource
-
-
-def test_image_data_source():
-    _ = ImageDataSource(
-        image_size_pixels=64,
-        meters_per_pixel=2000,
-        history_minutes=30,
-        forecast_minutes=60,
-    )
diff --git a/tests/data_sources/test_topographic_data_source.py b/tests/data_sources/test_topographic_data_source.py
index 109328b6..348486f8 100644
--- a/tests/data_sources/test_topographic_data_source.py
+++ b/tests/data_sources/test_topographic_data_source.py
@@ -41,6 +41,36 @@ def test_get_example_2km(x, y, left, right, top, bottom):
     assert np.isclose(bottom, topo_data.y.values[-1], atol=size)
 
 
+@pytest.mark.parametrize(
+    "x, y, left, right, top, bottom",
+    [
+        (0, 0, -128_000, 126_000, 128_000, -126_000),
+        (10, 0, -126_000, 128_000, 128_000, -126_000),
+        (30, 0, -126_000, 128_000, 128_000, -126_000),
+        (1000, 0, -126_000, 128_000, 128_000, -126_000),
+        (0, 1000, -128_000, 126_000, 128_000, -126_000),
+        (1000, 1000, -126_000, 128_000, 128_000, -126_000),
+        (2000, 2000, -126_000, 128_000, 130_000, -124_000),
+        (2000, 1000, -126_000, 128_000, 128_000, -126_000),
+        (2001, 2001, -124_000, 130_000, 130_000, -124_000),
+    ],
+)
+def test_get_batch_2km(x, y, left, right, top, bottom):
+    size = 2000  # meters
+    topo_source = TopographicDataSource(
+        filename="tests/data/europe_dem_2km_osgb.tif",
+        image_size_pixels=128,
+        meters_per_pixel=size,
+        forecast_minutes=300,
+        history_minutes=10,
+    )
+    x = np.array([x] * 32)
+    y = np.array([y] * 32)
+    t0_datetimes = pd.date_range("2021-01-01", freq="5T", periods=32) + pd.Timedelta("30T")
+    topo_data = topo_source.get_batch(t0_datetimes=t0_datetimes, x_locations=x, y_locations=y)
+    assert "x_index_index" not in topo_data.dims
+
+
 @pytest.mark.skip("CD does not have access to GCS")
 def test_get_example_gcs():
     """Note this test takes ~5 seconds as the topo data has to be downloaded locally"""
diff --git a/tests/test_manager.py b/tests/test_manager.py
index a1f8635d..db7a747d 100644
--- a/tests/test_manager.py
+++ b/tests/test_manager.py
@@ -72,12 +72,11 @@ def test_load_yaml_configuration():  # noqa: D103
     filename = local_path / "tests" / "config" / "test.yaml"
 
     manager.load_yaml_configuration(filename=filename)
-    local_temp_path = manager.local_temp_path
 
     manager.initialise_data_sources()
-    assert len(manager.data_sources) == 7
+    assert len(manager.data_sources) == 8
     assert isinstance(manager.data_source_which_defines_geospatial_locations, GSPDataSource)
-    assert isinstance(local_temp_path, Path)
+    assert isinstance(manager.config.process.local_temp_path, Path)
 
 
 def test_get_daylight_datetime_index():
@@ -110,10 +109,12 @@ def test_get_daylight_datetime_index():
 
 def test_batches():
     """Test that batches can be made"""
-    filename = Path(nowcasting_dataset.__file__).parent.parent / "tests" / "data" / "sat_data.zarr"
+    sat_filename = (
+        Path(nowcasting_dataset.__file__).parent.parent / "tests" / "data" / "sat_data.zarr"
+    )
 
     sat = SatelliteDataSource(
-        zarr_path=filename,
+        zarr_path=sat_filename,
         history_minutes=30,
         forecast_minutes=60,
         image_size_pixels=24,
@@ -121,11 +122,11 @@ def test_batches():
         channels=("IR_016",),
     )
 
-    filename = (
+    hrv_filename = (
         Path(nowcasting_dataset.__file__).parent.parent / "tests" / "data" / "hrv_sat_data.zarr"
     )
     hrvsat = SatelliteDataSource(
-        zarr_path=filename,
+        zarr_path=hrv_filename,
         history_minutes=30,
         forecast_minutes=60,
         image_size_pixels=64,
@@ -133,12 +134,12 @@ def test_batches():
         channels=("HRV",),
     )
 
-    filename = (
+    gsp_filename = (
         Path(nowcasting_dataset.__file__).parent.parent / "tests" / "data" / "gsp" / "test.zarr"
     )
 
     gsp = GSPDataSource(
-        zarr_path=filename,
+        zarr_path=gsp_filename,
         start_datetime=datetime(2020, 4, 1),
         end_datetime=datetime(2020, 4, 2),
         history_minutes=30,
@@ -160,8 +161,8 @@ def test_batches():
         manager.config.output_data.filepath = Path(dst_path)
         manager.local_temp_path = Path(local_temp_path)
 
-        # just set satellite as data source
-        manager.data_sources = {"gsp": gsp, "sat": sat, "hrvsat": hrvsat}
+        # Set data sources
+        manager.data_sources = {"gsp": gsp, "satellite": sat, "hrvsatellite": hrvsat}
         manager.data_source_which_defines_geospatial_locations = gsp
 
         # make file for locations
@@ -173,11 +174,11 @@ def test_batches():
         assert os.path.exists(f"{dst_path}/train")
         assert os.path.exists(f"{dst_path}/train/gsp")
         assert os.path.exists(f"{dst_path}/train/gsp/000000.nc")
-        assert os.path.exists(f"{dst_path}/train/sat/000000.nc")
         assert os.path.exists(f"{dst_path}/train/gsp/000001.nc")
-        assert os.path.exists(f"{dst_path}/train/sat/000001.nc")
-        assert os.path.exists(f"{dst_path}/train/hrvsat/000001.nc")
-        assert os.path.exists(f"{dst_path}/train/hrvsat/000000.nc")
+        assert os.path.exists(f"{dst_path}/train/satellite/000000.nc")
+        assert os.path.exists(f"{dst_path}/train/satellite/000001.nc")
+        assert os.path.exists(f"{dst_path}/train/hrvsatellite/000001.nc")
+        assert os.path.exists(f"{dst_path}/train/hrvsatellite/000000.nc")
 
 
 def test_save_config():