diff --git a/README.md b/README.md
index bb1ec370..c005a1b4 100644
--- a/README.md
+++ b/README.md
@@ -78,6 +78,10 @@ Once you have a username and password, download using
 [`scripts/download_UK_Met_Office_NWPs_from_CEDA.sh`](https://github.com/openclimatefix/nowcasting_dataset/tree/main/scripts/download_UK_Met_Office_NWPs_from_CEDA.sh).
 Please see the comments at the top of the script for instructions.
 
+Then convert the `grib` files to Zarr using `scripts/convert_NWP_grib_to_zarr.py`.  Run that script
+with `--help` to see how to operate it.  See the comments at the top of the script to learn how
+the script works.
+
 Detailed docs of the Met Office data is available [here](http://cedadocs.ceda.ac.uk/1334/1/uk_model_data_sheet_lores1.pdf).
 
 
diff --git a/notebooks/plot_NWP_zarr.ipynb b/notebooks/plot_NWP_zarr.ipynb
new file mode 100644
index 00000000..dcf74244
--- /dev/null
+++ b/notebooks/plot_NWP_zarr.ipynb
@@ -0,0 +1,550 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "cf9b8624-4cc8-42fa-aaee-b17df33e7438",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d1873561-4059-4493-ab0c-77c9982b2b90",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ZARR_PATH = \"/mnt/storage_ssd/data/ocf/solar_pv_nowcasting/nowcasting_dataset_pipeline/NWP/UK_Met_Office/UKV/zarr/test.zarr\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b366603a-3421-4621-a6bb-33910956d893",
+   "metadata": {},
+   "source": [
+    "## Load from Zarr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "633fb9bd-da4b-4e06-acec-bc09bd41b214",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds_from_zarr = xr.open_dataset(ZARR_PATH, mode=\"r\", engine=\"zarr\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "82e01ee2-c24e-44ed-a085-67cc35fbc06f",
+   "metadata": {},
+   "outputs": [
+    {
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+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
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+       "  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",
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+       ".xr-icon-database,\n",
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:    (variable: 17, init_time: 7810, step: 37, y: 704, x: 548)\n",
+       "Coordinates:\n",
+       "  * init_time  (init_time) datetime64[ns] 2016-03-22T15:00:00 ... 2018-12-09\n",
+       "  * step       (step) timedelta64[ns] 00:00:00 01:00:00 ... 1 days 12:00:00\n",
+       "  * variable   (variable) &lt;U6 &#x27;cdcb&#x27; &#x27;lcc&#x27; &#x27;mcc&#x27; ... &#x27;wdir10&#x27; &#x27;prmsl&#x27; &#x27;prate&#x27;\n",
+       "  * x          (x) int64 -239000 -237000 -235000 ... 851000 853000 855000\n",
+       "  * y          (y) int64 -183000 -181000 -179000 ... 1219000 1221000 1223000\n",
+       "Data variables:\n",
+       "    UKV        (variable, init_time, step, y, x) float32 ...</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-be6daa9d-77fb-4ea0-a508-392c0bd205ad' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-be6daa9d-77fb-4ea0-a508-392c0bd205ad' 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'>variable</span>: 17</li><li><span class='xr-has-index'>init_time</span>: 7810</li><li><span class='xr-has-index'>step</span>: 37</li><li><span class='xr-has-index'>y</span>: 704</li><li><span class='xr-has-index'>x</span>: 548</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-02d6efce-abea-40a5-8ed5-439328b369a3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-02d6efce-abea-40a5-8ed5-439328b369a3' 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'>init_time</span></div><div class='xr-var-dims'>(init_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2016-03-22T15:00:00 ... 2018-12-09</div><input id='attrs-d737054e-570f-482e-b4a0-5a1cdaeebce3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d737054e-570f-482e-b4a0-5a1cdaeebce3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22513b5c-e776-42e2-8d3e-a6ed8d0ae24a' class='xr-var-data-in' type='checkbox'><label for='data-22513b5c-e776-42e2-8d3e-a6ed8d0ae24a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>initial time of forecast</dd><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2016-03-22T15:00:00.000000000&#x27;, &#x27;2016-03-22T18:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-03-22T21:00:00.000000000&#x27;, ..., &#x27;2018-12-08T18:00:00.000000000&#x27;,\n",
+       "       &#x27;2018-12-08T21:00:00.000000000&#x27;, &#x27;2018-12-09T00:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>step</span></div><div class='xr-var-dims'>(step)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>00:00:00 ... 1 days 12:00:00</div><input id='attrs-d136f022-7d7e-45ec-afc3-2ae633f5f99f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d136f022-7d7e-45ec-afc3-2ae633f5f99f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-acc22622-affb-488f-bf8e-6091a69a91cd' class='xr-var-data-in' type='checkbox'><label for='data-acc22622-affb-488f-bf8e-6091a69a91cd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time since forecast_reference_time</dd><dt><span>standard_name :</span></dt><dd>forecast_period</dd></dl></div><div class='xr-var-data'><pre>array([              0,   3600000000000,   7200000000000,  10800000000000,\n",
+       "        14400000000000,  18000000000000,  21600000000000,  25200000000000,\n",
+       "        28800000000000,  32400000000000,  36000000000000,  39600000000000,\n",
+       "        43200000000000,  46800000000000,  50400000000000,  54000000000000,\n",
+       "        57600000000000,  61200000000000,  64800000000000,  68400000000000,\n",
+       "        72000000000000,  75600000000000,  79200000000000,  82800000000000,\n",
+       "        86400000000000,  90000000000000,  93600000000000,  97200000000000,\n",
+       "       100800000000000, 104400000000000, 108000000000000, 111600000000000,\n",
+       "       115200000000000, 118800000000000, 122400000000000, 126000000000000,\n",
+       "       129600000000000], dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>variable</span></div><div class='xr-var-dims'>(variable)</div><div class='xr-var-dtype'>&lt;U6</div><div class='xr-var-preview xr-preview'>&#x27;cdcb&#x27; &#x27;lcc&#x27; ... &#x27;prmsl&#x27; &#x27;prate&#x27;</div><input id='attrs-0d2479c1-3043-48d5-be31-e2f08379c71e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0d2479c1-3043-48d5-be31-e2f08379c71e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8fcd3083-b121-422e-826e-1470c528ddd5' class='xr-var-data-in' type='checkbox'><label for='data-8fcd3083-b121-422e-826e-1470c528ddd5' 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;cdcb&#x27;, &#x27;lcc&#x27;, &#x27;mcc&#x27;, &#x27;hcc&#x27;, &#x27;sde&#x27;, &#x27;hcct&#x27;, &#x27;dswrf&#x27;, &#x27;dlwrf&#x27;, &#x27;h&#x27;, &#x27;t&#x27;,\n",
+       "       &#x27;r&#x27;, &#x27;dpt&#x27;, &#x27;vis&#x27;, &#x27;si10&#x27;, &#x27;wdir10&#x27;, &#x27;prmsl&#x27;, &#x27;prate&#x27;], dtype=&#x27;&lt;U6&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-239000 -237000 ... 853000 855000</div><input id='attrs-18867d58-fc30-4256-b462-bebf4a2fbb1a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-18867d58-fc30-4256-b462-bebf4a2fbb1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c06b975-fe71-4c52-a0a1-aa5b6dafd1ff' class='xr-var-data-in' type='checkbox'><label for='data-7c06b975-fe71-4c52-a0a1-aa5b6dafd1ff' 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([-239000, -237000, -235000, ...,  851000,  853000,  855000])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-183000 -181000 ... 1221000 1223000</div><input id='attrs-9ab462c8-e3ee-4ab2-9758-430f47990988' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9ab462c8-e3ee-4ab2-9758-430f47990988' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ccdf38d7-1f26-4482-b7fc-b6f35a4db890' class='xr-var-data-in' type='checkbox'><label for='data-ccdf38d7-1f26-4482-b7fc-b6f35a4db890' 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([-183000, -181000, -179000, ..., 1219000, 1221000, 1223000])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2ae155a8-11fa-4325-8303-d0fc3e0173ea' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2ae155a8-11fa-4325-8303-d0fc3e0173ea' class='xr-section-summary' >Data variables: <span>(1)</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>UKV</span></div><div class='xr-var-dims'>(variable, init_time, step, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-239d9413-50db-42c8-be13-d5080074dfd1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-239d9413-50db-42c8-be13-d5080074dfd1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af5cc4c4-db0f-4358-b4b7-5e73e8eb8549' class='xr-var-data-in' type='checkbox'><label for='data-af5cc4c4-db0f-4358-b4b7-5e73e8eb8549' 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>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>GRIB_centre :</span></dt><dd>egrr</dd><dt><span>GRIB_centreDescription :</span></dt><dd>U.K. Met Office - Exeter</dd><dt><span>GRIB_edition :</span></dt><dd>2</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>institution :</span></dt><dd>U.K. Met Office - Exeter</dd></dl></div><div class='xr-var-data'><pre>[1895199342080 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-72343cc2-385b-4b18-bebd-9324401b85bf' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-72343cc2-385b-4b18-bebd-9324401b85bf' 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:    (variable: 17, init_time: 7810, step: 37, y: 704, x: 548)\n",
+       "Coordinates:\n",
+       "  * init_time  (init_time) datetime64[ns] 2016-03-22T15:00:00 ... 2018-12-09\n",
+       "  * step       (step) timedelta64[ns] 00:00:00 01:00:00 ... 1 days 12:00:00\n",
+       "  * variable   (variable) <U6 'cdcb' 'lcc' 'mcc' ... 'wdir10' 'prmsl' 'prate'\n",
+       "  * x          (x) int64 -239000 -237000 -235000 ... 851000 853000 855000\n",
+       "  * y          (y) int64 -183000 -181000 -179000 ... 1219000 1221000 1223000\n",
+       "Data variables:\n",
+       "    UKV        (variable, init_time, step, y, x) float32 ..."
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds_from_zarr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6473bbca-70e9-4d86-94af-1df3005bde18",
+   "metadata": {},
+   "source": [
+    "Variables perhaps to avoid:\n",
+    "\n",
+    "hcct?  Looks like a static map of the UK?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "3d48a95b-7b89-4ce4-8151-e5aef7a840fc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f8396dfeaf0>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ds_from_zarr['UKV'].sel(variable='t').isel(step=0, init_time=7).plot.imshow()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "f00995ad-5c1f-41fe-87ba-7ab678258add",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'chunks': (17, 1, 1, 352, 274),\n",
+       " 'preferred_chunks': {'variable': 17,\n",
+       "  'init_time': 1,\n",
+       "  'step': 1,\n",
+       "  'y': 352,\n",
+       "  'x': 274},\n",
+       " 'compressor': Blosc(cname='zstd', clevel=5, shuffle=SHUFFLE, blocksize=0),\n",
+       " 'filters': None,\n",
+       " '_FillValue': nan,\n",
+       " 'dtype': dtype('float32')}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds_from_zarr['UKV'].encoding"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cab8ba76-7aa3-4431-be48-1a561f56e3d4",
+   "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/scripts/convert_NWP_grib_to_zarr.py b/scripts/convert_NWP_grib_to_zarr.py
new file mode 100755
index 00000000..9748c35a
--- /dev/null
+++ b/scripts/convert_NWP_grib_to_zarr.py
@@ -0,0 +1,674 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+"""Convert numerical weather predictions from the UK Met Office "UKV" model to Zarr.
+
+This script uses multiple processes to speed up the conversion.  On leonardo (Open Climate Fix's
+on-premises Threadripper 1920x server) this takes about 3 hours 15 minutes of processing per year
+of NWP data (when using just Wholesale1 and Wholesale2 files).
+
+Useful links:
+
+* Met Office's data docs: http://cedadocs.ceda.ac.uk/1334/1/uk_model_data_sheet_lores1.pdf
+
+Some basic background to NWPs:  The UK Met Office run their "UKV" model 8 times per day,
+although, to save space on disk, we only use 4 of those runs per day (at 00, 06, 12, and 18 hours
+past midnight UTC).  The time the model started running is called the "init_time".
+
+Note that the UKV data is split into multiple files per NWP initialisation time.
+
+Known differences between the old Zarr
+(UKV__2018-01_to_2019-12__chunks__variable10__init_time1__step1__x548__y704__.zarr)
+and the new Zarr:
+
+* Images in the old zarr were top-to-bottom.  Images in the new Zarr follow the ordering in the
+  grib files: bottom-to-top.
+* The x and y coordinates are different by 1km each.
+* The new Zarr has 17 variables.  The old Zarr had 10 variables.
+
+How this script works:
+
+1) The script finds all the .grib filenames specified by `source_path_and_search_pattern`
+2) Group the .grib filenames by the NWP initialisation datetime (which is the datetime given
+   in the grib filename).  For example, this groups together all the Wholesale1 and
+   Wholesale2 files.
+3) If the `destination_zarr_path` exists then find the last NWP init time in the Zarr,
+   and ignore all grib files with init times before that time.  This allows the script to
+   re-start where it left off if it crashes, or if new grib files are downloaded.
+4) Use multiple worker processes.  Each worker process is given a list of
+   grib files associated with a single NWP init datetime.  The worker process loads these grib
+   files and does some simple post-processing, and then the worker process appends to the
+   destination zarr.
+
+The multi-processing aspect of the code is engineered to satisfy several constraints:
+1) Only a single process can append to a Zarr store at once.  And the appends must happen in strict
+   order of the NWP initialisation time.
+2) Passing large objects (such as a few hundred megabytes of NWP data) between processes via a
+   multiprocessing.Queue is *really* slow:  It takes about 7 seconds to pass an xr.Dataset
+   representing Wholesale1 and Wholesale2 NWP data through a multiprocessing.Queue.  This slowness
+   is what motivates us to avoid passing the loaded Dataset between processes.  Instead, we don't
+   pass large objects between processes:  A single process loads the grib files, processes,
+   and writes the data to Zarr.  Each xr.Dataset stays in one process.
+
+The code guarantees that the processes write to disk in order of the NWP init time by using a
+"chain of locks", kind of like a linked list. The iterable passed into multiprocessing.Pool.map
+is a tuple of (<the list of grib filenames for one NWP init time>, <a "previous_lock">, and
+<a "next_lock">).  Just before appending to the Zarr, each process blocks until the "previous_lock"
+is released when the process working on the previous NWP init time finishes writing to the Zarr.
+The "next_lock" for task n is the "previous_lock" for task n+1:
+
+  |------TASK 0------|    |------TASK 1------|    |------TASK 2------|
+  prev_lock, next_lock == prev_lock, next_lock == prev_lock, next_lock
+
+TROUBLESHOOTING
+If you get any errors regarding .idx files then try deleting all *.idx files and trying again.
+
+"""
+import datetime
+import glob
+import logging
+import multiprocessing
+import re
+from pathlib import Path
+from typing import Optional, Union
+
+import cfgrib
+import click
+import numcodecs
+import numpy as np
+import pandas as pd
+import xarray as xr
+
+logger = logging.getLogger(__name__)
+
+_LOG_LEVELS = ("DEBUG", "INFO", "WARNING", "ERROR")
+
+# Define geographical domain for UKV.
+# Taken from page 4 of http://cedadocs.ceda.ac.uk/1334/1/uk_model_data_sheet_lores1.pdf
+# To quote the PDF:
+#     "The United Kingdom domain is a 1,096km x 1,408km ~2km resolution grid."
+DY_METERS = DX_METERS = 2_000
+#     "The OS National Grid corners of the domain are:"
+NORTH = 1223_000
+SOUTH = -185_000
+WEST = -239_000
+EAST = 857_000
+# Note that the UKV NWPs y is top-to-bottom, hence step is negative.
+NORTHING = np.arange(start=NORTH, stop=SOUTH, step=-DY_METERS, dtype=np.int32)
+EASTING = np.arange(start=WEST, stop=EAST, step=DX_METERS, dtype=np.int32)
+NUM_ROWS = len(NORTHING)
+NUM_COLS = len(EASTING)
+
+# Define a set of grib variables to delete in load_grib_file().
+VARS_TO_DELETE = (
+    "unknown",
+    "valid_time",
+    "heightAboveGround",
+    "heightAboveGroundLayer",
+    "atmosphere",
+    "cloudBase",
+    "surface",
+    "meanSea",
+    "level",
+)
+
+
+@click.command()
+@click.option(
+    "--source_grib_path_and_search_pattern",
+    default=(
+        "/mnt/storage_b/data/ocf/solar_pv_nowcasting/nowcasting_dataset_pipeline/NWP/"
+        "UK_Met_Office/UKV/native/*/*/*/*Wholesale[12].grib"
+    ),
+    help=(
+        "Optional.  The directory and the search pattern for the source grib files."
+        "  For example /foo/bar/*/*/*/*Wholesale[12].grib"
+    ),
+)
+@click.option(
+    "--destination_zarr_path",
+    help="The output Zarr path to write to.  Will be appended to if already exists.",
+)
+@click.option(
+    "--n_processes",
+    default=8,
+    help=(
+        "Optional.  Defaults to 8.  The number of processes to use for loading grib"
+        " files in parallel."
+    ),
+)
+@click.option(
+    "--log_level",
+    default="DEBUG",
+    type=click.Choice(_LOG_LEVELS),
+    help="Optional.  Set the log level.",
+)
+@click.option(
+    "--log_filename",
+    default=None,
+    help=(
+        "Optional.  If not set then will default to `destination_zarr_path` with the"
+        " suffix replaced with '.log'"
+    ),
+)
+@click.option(
+    "--n_grib_files_per_nwp_init_time",
+    default=2,
+    help=(
+        "Optional. Defaults to 2. The number of grib files expected per NWP initialisation time."
+        "  For example, if the search pattern includes Wholesale1 and Wholesale2 files, then set"
+        " n_grib_files_per_nwp_init_time to 2."
+    ),
+)
+def main(
+    source_grib_path_and_search_pattern: str,
+    destination_zarr_path: str,
+    n_processes: int,
+    log_level: str,
+    log_filename: Optional[str],
+    n_grib_files_per_nwp_init_time: int,
+):
+    """The entry point into the script."""
+    destination_zarr_path = Path(destination_zarr_path)
+
+    # Set up logging.
+    if log_filename is None:
+        log_filename = destination_zarr_path.parent / (destination_zarr_path.stem + ".log")
+    configure_logging(log_level=log_level, log_filename=log_filename)
+    filter_eccodes_logging()
+
+    # Get all filenames.
+    logger.info(f"Getting list of all filenames in {source_grib_path_and_search_pattern}...")
+    filenames = glob.glob(source_grib_path_and_search_pattern)
+    filenames = [Path(filename) for filename in filenames]
+    logger.info(f"Found {len(filenames):,d} grib filenames.")
+    if len(filenames) == 0:
+        logger.warning(
+            "No files found!  Are you sure the source_grib_path_and_search_pattern is correct?"
+        )
+        return
+
+    # Decode and group the grib filenames:
+    map_datetime_to_grib_filename = decode_and_group_grib_filenames(
+        filenames=filenames, n_grib_files_per_nwp_init_time=n_grib_files_per_nwp_init_time
+    )
+
+    # Remove grib filenames which have already been processed:
+    map_datetime_to_grib_filename = select_grib_filenames_still_to_process(
+        map_datetime_to_grib_filename, destination_zarr_path
+    )
+
+    # The main event!
+    process_grib_files_in_parallel(
+        map_datetime_to_grib_filename=map_datetime_to_grib_filename,
+        destination_zarr_path=destination_zarr_path,
+        n_processes=n_processes,
+    )
+
+
+def configure_logging(log_level: str, log_filename: str) -> None:
+    """Configure logger for this script.
+
+    Args:
+      log_level: String like "DEBUG".
+      log_filename: The full filename of the log file.
+    """
+    assert log_level in _LOG_LEVELS
+    log_level = getattr(logging, log_level)  # Convert string to int.
+    logger = logging.getLogger(__name__)
+    logger.setLevel(log_level)
+    formatter = logging.Formatter("%(asctime)s %(levelname)s processID=%(process)d %(message)s")
+
+    handlers = [logging.StreamHandler(), logging.FileHandler(log_filename, mode="a")]
+
+    for handler in handlers:
+        handler.setLevel(log_level)
+        handler.setFormatter(formatter)
+        logger.addHandler(handler)
+
+
+def filter_eccodes_logging():
+    """Filter out "ecCodes provides no latitudes/longitudes for gridType='transverse_mercator'"
+
+    Filter out this warning because it is not useful, and just adds noise to the log.
+    """
+    # The warning originates from here:
+    # https://github.com/ecmwf/cfgrib/blob/master/cfgrib/dataset.py#L402
+    class FilterEccodesWarning(logging.Filter):
+        def filter(self, record) -> bool:
+            """Inspect `record`. Return True to log `record`. Return False to ignore `record`."""
+            return not record.getMessage() == (
+                "ecCodes provides no latitudes/longitudes for gridType='transverse_mercator'"
+            )
+
+    logging.getLogger("cfgrib.dataset").addFilter(FilterEccodesWarning())
+
+
+def grib_filename_to_datetime(full_grib_filename: Path) -> datetime.datetime:
+    """Parse the grib filename and return the datetime encoded in the filename.
+
+    Returns a datetime.
+      For example, if the filename is '/foo/202101010000_u1096_ng_umqv_Wholesale1.grib',
+      then the returned datetime will be datetime(year=2021, month=1, day=1, hour=0, minute=0).
+
+    Raises RuntimeError if the filename does not match the expected regex pattern.
+    """
+    # Get the base_filename, which will be of the form '202101010000_u1096_ng_umqv_Wholesale1.grib'
+    base_filename = full_grib_filename.name
+
+    # Use regex to match the year, month, day, hour, and minute.
+    # That is, group the filename as shown in these brackets:
+    #   (2021)(01)(01)(00)(00)_u1096_ng_umqv_Wholesale1.grib
+    # A quick guide to the relevant regex operators:
+    #   ^ matches the beginning of the string.
+    #   () defines a group.
+    #   (?P<name>...) names the group.  We can access the group with regex_match.groupdict()[<name>]
+    #   \d matches a single digit.
+    #   {n} matches the preceding item n times.
+    #   . matches any character.
+    #   $ matches the end of the string.
+    regex_pattern_string = (
+        "^"  # Match the beginning of the string.
+        "(?P<year>\d{4})"  # noqa: W605
+        "(?P<month>\d{2})"  # noqa: W605
+        "(?P<day>\d{2})"  # noqa: W605
+        "(?P<hour>\d{2})"  # noqa: W605
+        "(?P<minute>\d{2})"  # noqa: W605
+        "_u1096_ng_umqv_Wholesale\d\.grib$"  # noqa: W605. Match the end of the string.
+    )
+    regex_pattern = re.compile(regex_pattern_string)
+    regex_match = regex_pattern.match(base_filename)
+    if regex_match is None:
+        msg = (
+            f"Filename '{full_grib_filename}' does not conform to expected"
+            f" regex pattern '{regex_pattern_string}'!"
+        )
+        logger.error(msg)
+        raise RuntimeError(msg)
+
+    # Convert strings to ints:
+    regex_groups = {key: int(value) for key, value in regex_match.groupdict().items()}
+
+    return datetime.datetime(**regex_groups)
+
+
+def decode_and_group_grib_filenames(
+    filenames: list[Path], n_grib_files_per_nwp_init_time: int = 2
+) -> pd.Series:
+    """Returns a pd.Series where the index is the NWP init time.
+
+    And the values are the full_grib_filename of each grib file.
+
+    Throws away any groups where there are not exactly n_grib_files_per_nwp_init_time.
+    """
+    # Create a 1D array of NWP initialisation times (decoded from the grib filenames):
+    n_filenames = len(filenames)
+    nwp_init_datetimes = np.full(shape=n_filenames, fill_value=np.NaN, dtype="datetime64[ns]")
+    for i, filename in enumerate(filenames):
+        nwp_init_datetimes[i] = grib_filename_to_datetime(filename)
+
+    # Create a pd.Series where each row maps an NWP init datetime to the full grib filename:
+    map_datetime_to_filename = pd.Series(
+        filenames, index=nwp_init_datetimes, name="full_grib_filename"
+    )
+    del nwp_init_datetimes
+    map_datetime_to_filename.index.name = "nwp_init_datetime_utc"
+
+    # Select only rows where there are exactly n_grib_files_per_nwp_init_time:
+    def _filter_func(group):
+        return group.count() == n_grib_files_per_nwp_init_time
+
+    map_datetime_to_filename = map_datetime_to_filename.groupby(level=0).filter(_filter_func)
+
+    return map_datetime_to_filename.sort_index()
+
+
+def select_grib_filenames_still_to_process(
+    map_datetime_to_grib_filename: pd.Series, destination_zarr_path: Path
+) -> pd.Series:
+    """Remove grib filenames for NWP init times that already exist in Zarr."""
+    if destination_zarr_path.exists():
+        last_nwp_init_datetime_in_zarr = get_last_nwp_init_datetime_in_zarr(destination_zarr_path)
+        logger.info(
+            f"{destination_zarr_path} exists.  The last NWP init datetime (UTC) in the Zarr is"
+            f" {last_nwp_init_datetime_in_zarr}"
+        )
+        nwp_init_datetimes_utc = map_datetime_to_grib_filename.index
+        map_datetime_to_grib_filename = map_datetime_to_grib_filename[
+            nwp_init_datetimes_utc > last_nwp_init_datetime_in_zarr
+        ]
+    return map_datetime_to_grib_filename
+
+
+def get_last_nwp_init_datetime_in_zarr(zarr_path: Path) -> datetime.datetime:
+    """Get the last NWP init datetime in the Zarr."""
+    dataset = xr.open_dataset(zarr_path, engine="zarr", mode="r")
+    return dataset.init_time[-1].values
+
+
+def load_grib_file(full_grib_filename: Union[Path, str], verbose: bool = False) -> xr.Dataset:
+    """Merges and loads all contiguous xr.Datasets for a single grib file.
+
+    Removes unnecessary variables.  Picks heightAboveGround = 1 meter for temperature.
+
+    Returns an xr.Dataset which has been loaded from disk.  Loading from disk at this point
+    takes about 2 seconds for a 250 MB grib file, but speeds up reshape_1d_to_2d
+    from about 7 seconds to 0.5 seconds :)
+
+    Args:
+      full_grib_filename:  The full filename (including the path) of a single grib file.
+      verbose:  If True then print out some useful debugging information.
+    """
+    # The grib files are "heterogeneous" so we cannot use xr.open_dataset().
+    # Instead we use cfgrib.open_datasets() which returns a *list* of contiguous xr.Datasets.
+    # See https://github.com/ecmwf/cfgrib#automatic-filtering
+    logger.debug(f"Opening {full_grib_filename}...")
+    datasets_from_grib: list[xr.Dataset] = cfgrib.open_datasets(full_grib_filename)
+    n_datasets = len(datasets_from_grib)
+
+    # Get each dataset into the right shape for merging:
+    # We use `for i in range(n_datasets)` instead of `for ds in datasets_from_grib`
+    # because the loop modifies each dataset:
+    for i in range(n_datasets):
+        ds = datasets_from_grib[i]
+
+        if verbose:
+            print("\nDataset", i, "before processing:\n", ds, "\n")
+
+        # We want the temperature at 1 meter above ground, not at 0 meters above ground.
+        # In the early NWPs (definitely in the 2016-03-22 NWPs), `heightAboveGround` only has
+        # 1 entry ("1" meter above ground) and `heightAboveGround` isn't set as a dimension for `t`.
+        # In later NWPs, 'heightAboveGround' has 2 values (0, 1) is a dimension for `t`.
+        if "t" in ds and "heightAboveGround" in ds["t"].dims:
+            ds = ds.sel(heightAboveGround=1)
+
+        # Delete unnecessary variables.
+        for var_name in VARS_TO_DELETE:
+            try:
+                del ds[var_name]
+            except KeyError as e:
+                if verbose:
+                    print("var name not in dataset:", e)
+            else:
+                if verbose:
+                    print("Deleted", var_name)
+
+        if verbose:
+            print("\nDataset", i, "after processing:\n", ds, "\n")
+            print("**************************************************")
+
+        datasets_from_grib[i] = ds
+        del ds  # Save memory.
+
+    merged_ds = xr.merge(datasets_from_grib)
+    del datasets_from_grib  # Save memory.
+    logger.debug(f"Loading {full_grib_filename}...")
+    return merged_ds.load()
+
+
+def reshape_1d_to_2d(dataset: xr.Dataset) -> xr.Dataset:
+    """Convert 1D into 2D array.
+
+    In the grib files, the pixel values are in a flat 1D array (indexed by the `values` dimension).
+    The ordering of the pixels in the grib are left to right, bottom to top.
+
+    This function replaces the `values` dimension with an `x` and `y` dimension,
+    and, for each step, reshapes the images to be 2D.
+    """
+    # Adapted from https://stackoverflow.com/a/62667154
+
+    # Don't reshape yet.  Instead just create new coordinates,
+    # which give the `x` and `y` position for each position in the `values` dimension:
+    dataset = dataset.assign_coords(
+        {
+            "x": ("values", np.tile(EASTING, reps=NUM_ROWS)),
+            "y": ("values", np.repeat(NORTHING, repeats=NUM_COLS)),
+        }
+    )
+
+    # Now set `values` to be a MultiIndex, indexed by `y` and `x`:
+    dataset = dataset.set_index(values=("y", "x"))
+
+    # Now unstack.  This gets rid of the `values` dimension and indexes
+    # the data variables using `y` and `x`.
+    return dataset.unstack("values")
+
+
+def dataset_has_variables(dataset: xr.Dataset) -> bool:
+    """Return True if `dataset` has at least one variable."""
+    return len(dataset.variables) > 0
+
+
+def post_process_dataset(dataset: xr.Dataset) -> xr.Dataset:
+    """Get the Dataset ready for saving to Zarr.
+
+    Convert the Dataset (with differet DataArrays for each NWP variable)
+    to a single DataArray with a `variable` dimension.  We do this so each
+    Zarr chunk can hold multiple NWP variables (which is useful because
+    we often load all the NWP variables at once).
+
+    Rename `time` to `init_time` (because `time` is ambiguous.  NWPs have two "times":
+    the initialisation time and the target time).
+
+    Rechunk the Dataset.  Rechunking at this step (instead of specifying chunks using the
+    `dataset.to_zarr(encoding=...)`) has two advantages:  1) We can name the dimensions; and
+    2) Chunking at this stage converts the Dataset into a Dask dataset, which adds a second
+    level of parallelism.
+    """
+    logger.debug("Post-processing dataset...")
+    return (
+        dataset.to_array(dim="variable", name="UKV")
+        .to_dataset()
+        .rename({"time": "init_time"})
+        .chunk(
+            {
+                "init_time": 1,
+                "step": 1,
+                "y": len(dataset.y) // 2,
+                "x": len(dataset.x) // 2,
+                "variable": -1,
+            }
+        )
+    )
+
+
+def append_to_zarr(dataset: xr.Dataset, zarr_path: Union[str, Path]):
+    """If zarr_path already exists then append to the init_time dim.  Else create a new Zarr.
+
+    If creating a new Zarr, then this function sets the units for representing time to
+    "nanoseconds since 1970-01-01" (which is the encoding used by `numpy.datetime64[ns]`) otherwise,
+    by default, xarray defaults representing time as an integer numbers of *days* and hence cannot
+    represent sub-day temporal resolution and corrupts the `init_time` values when we
+    append to Zarr.  See:
+        https://github.com/pydata/xarray/issues/5969   and
+        http://xarray.pydata.org/en/stable/user-guide/io.html#time-units
+
+    Also sets the compressor to `numcodecs.Blosc(cname="zstd", clevel=5)` which has been shown
+    to provide a good balance of speed and small file sizes in empirical testing.
+    """
+    zarr_path = Path(zarr_path)
+    if zarr_path.exists():
+        # Append to existing Zarr store.
+        to_zarr_kwargs = dict(
+            append_dim="init_time",
+        )
+    else:
+        # Create new Zarr store.
+        to_zarr_kwargs = dict(
+            encoding={
+                "init_time": {"units": "nanoseconds since 1970-01-01"},
+                "UKV": {
+                    "compressor": numcodecs.Blosc(cname="zstd", clevel=5),
+                },
+            },
+        )
+
+    dataset.to_zarr(zarr_path, **to_zarr_kwargs)
+
+
+def load_grib_files_for_single_nwp_init_time(
+    full_grib_filenames: list[Path], task_number: int
+) -> Union[xr.Dataset, None]:
+    """Returns processed Dataset merging all grib files specified by full_grib_filenames.
+
+    Returns None if any of the grib files are invalid.
+    """
+    assert len(full_grib_filenames) > 0
+    datasets_for_nwp_init_datetime = []
+    for full_grib_filename in full_grib_filenames:
+        logger.debug(f"Task #{task_number}: Opening {full_grib_filename}")
+        try:
+            dataset_for_filename = load_grib_file(full_grib_filename)
+        except EOFError as e:
+            logger.warning(f"{e}. Filesize = {full_grib_filename.stat().st_size:,d} bytes")
+            # If any of the files associated with this nwp_init_datetime is broken then
+            # skip all, because we don't want incomplete data for an init_datetime.
+            return
+        else:
+            if dataset_has_variables(dataset_for_filename):
+                datasets_for_nwp_init_datetime.append(dataset_for_filename)
+            else:
+                logger.warning(f"{full_grib_filename} has no variables!")
+                return
+    logger.debug(f"Task #{task_number}: Merging datasets...")
+    dataset_for_nwp_init_datetime = xr.merge(datasets_for_nwp_init_datetime)
+    del datasets_for_nwp_init_datetime  # Save memory.
+    logger.debug(f"Task #{task_number}: Reshaping datasets...")
+    dataset_for_nwp_init_datetime = reshape_1d_to_2d(dataset_for_nwp_init_datetime)
+    dataset_for_nwp_init_datetime = dataset_for_nwp_init_datetime.expand_dims("time", axis=0)
+    dataset_for_nwp_init_datetime = post_process_dataset(dataset_for_nwp_init_datetime)
+    return dataset_for_nwp_init_datetime
+
+
+def load_grib_files_and_save_zarr_with_lock(task: dict[str, object]) -> None:
+    """A wrapper arouund load_grib_files_for_single_nwp_init_time but with locking logic.
+
+    See the docstring at the top of this script for more information about how we use
+    a chain of multiprocessing.Locks to guarantee that only one process writes to Zarr at once,
+    and that the writing is done in strict order of the NWP init time.
+
+    Args:
+        task: A dict which contains the arguments for loading grib files.  We use a dict
+              because multiprocessing.Pool.map() requires a single iterable (and we use
+              a single iterable of dicts).  Task must contains these keys:
+              - row:  The pd.Series listing the full grib filenames to load.
+              - previous_lock: The multiprocessing.Lock which will be released by the process
+                working on the previous task.  This process will block until previous_lock
+                is released.
+              - next_lock: The multiprocessing.Lock which this process will release after
+                appending to the Zarr.  Releasing this lock will allow the next process to proceed.
+              - destination_zarr_path: The path of the Zarr to append to.
+              - start_time: The datetime at which this script started processing.  Just used to
+                log the time taken so far, and the number of seconds per task.
+    """
+    full_grib_filenames = task["row"]
+    previous_lock = task["previous_lock"]
+    next_lock = task["next_lock"]
+    task_number = task["task_number"]
+    destination_zarr_path = task["destination_zarr_path"]
+    start_time = task["start_time"]
+
+    TIMEOUT_SECONDS = 120
+    dataset = load_grib_files_for_single_nwp_init_time(full_grib_filenames, task_number=task_number)
+    if dataset is not None:
+        logger.debug(f"Task #{task_number}: Before previous_lock.acquire()")
+        # Block waiting for previous processes to complete.  This ensures that the reader processes
+        # don't get ahead of the writing process; and ensures that only one process writes to the
+        # Zarr at once; and ensures that data is appended in order of the NWP init time.
+        previous_lock.acquire(blocking=True, timeout=TIMEOUT_SECONDS)
+        logger.debug(f"Task #{task_number}: After previous_lock.acquire()")
+        logger.debug(
+            f"Task #{task_number}: About to append NWP init time {dataset.init_time.values}"
+            f" to {destination_zarr_path}"
+        )
+        append_to_zarr(dataset, destination_zarr_path)
+        logger.debug(
+            f"Task #{task_number}: Finished appending NWP init time {dataset.init_time.values}"
+            f" to {destination_zarr_path}"
+        )
+    else:
+        logger.warning(
+            f"Task #{task_number}: Dataset is None!  Grib filenames = {full_grib_filenames}"
+        )
+
+    # Allow the next process to append to the Zarr:
+    next_lock.release()
+
+    # Calculate timings.
+    time_taken = pd.Timestamp.now() - start_time
+    seconds_per_task = (time_taken / (task_number + 1)).total_seconds()
+    logger.debug(
+        f"{task_number + 1:,d} tasks (NWP init timesteps) completed in {time_taken}"
+        f". That's {seconds_per_task:,.1f} seconds per NWP init timestep."
+    )
+
+
+def load_grib_files_and_save_zarr_with_lock_wrapper(task: dict[str, object]) -> None:
+    """Simple wrapper around load_grib_files_and_save_zarr_with_lock to catch & log exceptions."""
+    try:
+        task_number = task["task_number"]
+        full_grib_filenames = task["row"]
+        load_grib_files_and_save_zarr_with_lock(task)
+    except Exception:
+        logger.exception(
+            f"Exception raised when processing task number {task_number},"
+            f" loading grib filenames {full_grib_filenames}"
+        )
+        raise
+
+
+def process_grib_files_in_parallel(
+    map_datetime_to_grib_filename: pd.Series,
+    destination_zarr_path: Path,
+    n_processes: int,
+) -> None:
+    """Process grib files in parallel."""
+    # To pass the shared Lock into the worker processes, we must use a Manager():
+    multiprocessing_manager = multiprocessing.Manager()
+
+    # Make note of when this script started.  This is used to compute how many
+    # tasks the script completes in a given time.
+    start_time = pd.Timestamp.now()
+
+    # Create a list of `tasks` which include the grib filenames, the prev_lock & next_lock:
+    tasks: list[dict[str, object]] = []
+    previous_lock = multiprocessing_manager.Lock()  # Lock starts in a "released" state.
+    for task_number, (_, row) in enumerate(map_datetime_to_grib_filename.groupby(level=0)):
+        next_lock = multiprocessing_manager.Lock()
+        next_lock.acquire()
+        tasks.append(
+            dict(
+                row=row,
+                previous_lock=previous_lock,
+                next_lock=next_lock,
+                task_number=task_number,
+                destination_zarr_path=destination_zarr_path,
+                start_time=start_time,
+            )
+        )
+        previous_lock = next_lock
+
+    logger.info(f"About to process {len(tasks):,d} tasks using {n_processes} processes.")
+
+    # Run the processes!
+    with multiprocessing.Pool(processes=n_processes) as pool:
+        result_iterator = pool.map(
+            func=load_grib_files_and_save_zarr_with_lock_wrapper, iterable=tasks, chunksize=1
+        )
+
+    # Loop through the results to trigger any exceptions:
+    logger.debug(
+        "Almost finished! Now running through pool.map iterator to raise any final exceptions."
+    )
+    try:
+        for iterator in result_iterator:
+            pass
+    except Exception:
+        logger.exception()
+        raise
+
+    logger.info("Done!")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/download_UK_Met_Office_NWPs_from_CEDA.sh b/scripts/download_UK_Met_Office_NWPs_from_CEDA.sh
index fb343d08..69073c14 100755
--- a/scripts/download_UK_Met_Office_NWPs_from_CEDA.sh
+++ b/scripts/download_UK_Met_Office_NWPs_from_CEDA.sh
@@ -6,6 +6,10 @@
 # Then call this script with three arguments: <username> <password> <year>.  e.g.:
 # ./download_UK_Met_Office_NWPs_from_CEDA.sh foo bar 2021
 #
+# Call this script in the directory into which you want to download data.  For
+# example, if you want to download data for 2021, create a directory
+# called something like /data/2021/, and run this script from that directory.
+#
 # The Met Office data on CEDA goes back to March 2016.  This script
 # will download about 4 terabytes per year of data.
 #
@@ -13,8 +17,8 @@
 # SSH'ing into a VM or remote server.
 
 wget --user="$1" --password="$2" --recursive -nH --cut-dirs=5 --no-clobber \
---reject-regex "[[:digit:]]{8}(03|09|15|21)00.*\.grib$" \
---reject-regex "T120\.grib$" \
+--reject-regex "[[:digit:]]{8}(03|09|15|21)00.*\.grib$" \  # NOT WORKING.  TODO: Issue #389
+--reject-regex "T120\.grib$" \  # NOT WORKING.  TODO: Issue #389.
 --reject-regex "Wholesale5.*\.grib$" \
 ftp://ftp.ceda.ac.uk/badc/ukmo-nwp/data/ukv-grib/"$3"