diff --git a/validation/v0.8.1/spatial_avg_min_weight_test.ipynb b/validation/v0.8.1/spatial_avg_min_weight_test.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Test for weight threshold option for spatial averaging\n",
+    "\n",
+    "https://github.com/xCDAT/xcdat/pull/672"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Prepare sample input data\n",
+    "\n",
+    "HadISST from obs4MIPs: NaN over land (time-fixed) and sea-ice (time-varying)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xcdat as xc\n",
+    "import xarray as xr\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputfile = \"ts_mon_HadISST-1-1_PCMDI_gn_187001-202501.nc\"  # data from obs4MIPs\n",
+    "data_var = \"ts\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = xc.open_dataset(inputfile).isel(time=slice(0, 12))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
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+       "\n",
+       ".xr-attrs dd {\n",
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+       "\n",
+       ".xr-icon-database,\n",
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+       "  stroke: currentColor;\n",
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 3MB\n",
+       "Dimensions:    (time: 12, bnds: 2, lat: 180, lon: 360)\n",
+       "Coordinates:\n",
+       "  * lat        (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5\n",
+       "  * lon        (lon) float64 3kB 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5\n",
+       "  * time       (time) object 96B 1870-01-16 12:00:00 ... 1870-12-16 12:00:00\n",
+       "Dimensions without coordinates: bnds\n",
+       "Data variables:\n",
+       "    time_bnds  (time, bnds) object 192B ...\n",
+       "    lat_bnds   (lat, bnds) float64 3kB ...\n",
+       "    lon_bnds   (lon, bnds) float64 6kB ...\n",
+       "    ts         (time, lat, lon) float32 3MB ...\n",
+       "Attributes: (12/33)\n",
+       "    Conventions:            CF-1.11; ODS-2.5\n",
+       "    activity_id:            obs4MIPs\n",
+       "    contact:                nick.rayner@METOFFICE.GOV\n",
+       "    creation_date:          2025-04-15T18:42:24Z\n",
+       "    data_specs_version:     ODS-2.5\n",
+       "    external_variables:     areacella\n",
+       "    ...                     ...\n",
+       "    tracking_id:            hdl:21.14102/a5a2c670-b960-49c2-9833-7b40ff4fc0ec\n",
+       "    variable_id:            ts\n",
+       "    variant_info:           obs4MIPs-compliant product prepared by PCMDI (ODS...\n",
+       "    variant_label:          PCMDI\n",
+       "    license:                Data in this file is licensed under a Creative Co...\n",
+       "    cmor_version:           3.9.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b62baa31-5ca3-422a-bcea-2e1d5fb662d9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b62baa31-5ca3-422a-bcea-2e1d5fb662d9' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 12</li><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lat</span>: 180</li><li><span class='xr-has-index'>lon</span>: 360</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-416415c2-2241-41d1-a026-d2891b72ba68' class='xr-section-summary-in' type='checkbox'  checked><label for='section-416415c2-2241-41d1-a026-d2891b72ba68' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.5 -88.5 -87.5 ... 88.5 89.5</div><input id='attrs-255e9b8c-af7e-4964-9d4f-debe1de74417' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-255e9b8c-af7e-4964-9d4f-debe1de74417' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4965e5c2-c8d0-40b6-8de1-dd5c986aa76a' class='xr-var-data-in' type='checkbox'><label for='data-4965e5c2-c8d0-40b6-8de1-dd5c986aa76a' 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>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
+       "       -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,\n",
+       "       -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,\n",
+       "       -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,\n",
+       "       -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,\n",
+       "       -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,\n",
+       "       -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,\n",
+       "       -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,\n",
+       "        -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,\n",
+       "         0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
+       "        10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,\n",
+       "        20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,\n",
+       "        30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,\n",
+       "        40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,\n",
+       "        50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,\n",
+       "        60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,\n",
+       "        70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,\n",
+       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5 1.5 2.5 ... 357.5 358.5 359.5</div><input id='attrs-69ed8b3f-0793-42b0-aa10-94115a7226f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-69ed8b3f-0793-42b0-aa10-94115a7226f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-439cb572-7baf-489c-9ebc-19218a16c258' class='xr-var-data-in' type='checkbox'><label for='data-439cb572-7baf-489c-9ebc-19218a16c258' 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>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([  0.5,   1.5,   2.5, ..., 357.5, 358.5, 359.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1870-01-16 12:00:00 ... 1870-12-...</div><input id='attrs-d47f6da7-2ef1-4113-8697-09d64f0462dc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d47f6da7-2ef1-4113-8697-09d64f0462dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6274ce25-349c-441c-a234-d484fcdceabc' class='xr-var-data-in' type='checkbox'><label for='data-6274ce25-349c-441c-a234-d484fcdceabc' 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>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeGregorian(1870, 1, 16, 12, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(1870, 2, 15, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(1870, 3, 16, 12, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(1870, 4, 16, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(1870, 5, 16, 12, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(1870, 6, 16, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(1870, 7, 16, 12, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(1870, 8, 16, 12, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(1870, 9, 16, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(1870, 10, 16, 12, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(1870, 11, 16, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(1870, 12, 16, 12, 0, 0, 0, has_year_zero=False)],\n",
+       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-28a54b80-adb5-4d65-9c53-a1a94c5c571c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-28a54b80-adb5-4d65-9c53-a1a94c5c571c' class='xr-section-summary' >Data variables: <span>(4)</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>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-119eda82-fbf5-4b7b-acb7-b8badef9d98f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-119eda82-fbf5-4b7b-acb7-b8badef9d98f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc3cbf5a-a9f9-4d83-bc41-52865c35f2e8' class='xr-var-data-in' type='checkbox'><label for='data-cc3cbf5a-a9f9-4d83-bc41-52865c35f2e8' 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>[24 values with dtype=object]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(lat, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-71093b25-a8d1-490d-9389-bf6727f82609' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-71093b25-a8d1-490d-9389-bf6727f82609' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35dab053-2343-4d77-89f6-d8c89c35e4ee' class='xr-var-data-in' type='checkbox'><label for='data-35dab053-2343-4d77-89f6-d8c89c35e4ee' 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>[360 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-843b8d42-2264-47b3-b6da-1dd3cce68803' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-843b8d42-2264-47b3-b6da-1dd3cce68803' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f41abbc7-b89a-409d-bcb7-7daeb466aa10' class='xr-var-data-in' type='checkbox'><label for='data-f41abbc7-b89a-409d-bcb7-7daeb466aa10' 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>[720 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ts</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5a047c55-7b73-474e-bb22-24333cfcc9a1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5a047c55-7b73-474e-bb22-24333cfcc9a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33003373-cb22-49fd-882a-e8ae2676ce96' class='xr-var-data-in' type='checkbox'><label for='data-33003373-cb22-49fd-882a-e8ae2676ce96' 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>standard_name :</span></dt><dd>surface_temperature</dd><dt><span>long_name :</span></dt><dd>Surface Temperature</dd><dt><span>comment :</span></dt><dd>Temperature of the lower boundary of the atmosphere</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>history :</span></dt><dd>2025-04-15T18:42:24Z altered by CMOR: Inverted axis: lat.</dd></dl></div><div class='xr-var-data'><pre>[777600 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6b19a36b-234c-4abd-8261-129b123af8e4' class='xr-section-summary-in' type='checkbox'  ><label for='section-6b19a36b-234c-4abd-8261-129b123af8e4' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-4447b291-d5a6-4d32-85f4-7e61ae0e0ff3' class='xr-index-data-in' type='checkbox'/><label for='index-4447b291-d5a6-4d32-85f4-7e61ae0e0ff3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
+       "       ...\n",
+       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=180))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-64c3dcc0-8825-4857-b62c-404fddd3f212' class='xr-index-data-in' type='checkbox'/><label for='index-64c3dcc0-8825-4857-b62c-404fddd3f212' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
+       "       ...\n",
+       "       350.5, 351.5, 352.5, 353.5, 354.5, 355.5, 356.5, 357.5, 358.5, 359.5],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=360))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-55baa166-ab75-47fa-b1ce-f281a8f53c8c' class='xr-index-data-in' type='checkbox'/><label for='index-55baa166-ab75-47fa-b1ce-f281a8f53c8c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([1870-01-16 12:00:00, 1870-02-15 00:00:00, 1870-03-16 12:00:00,\n",
+       "             1870-04-16 00:00:00, 1870-05-16 12:00:00, 1870-06-16 00:00:00,\n",
+       "             1870-07-16 12:00:00, 1870-08-16 12:00:00, 1870-09-16 00:00:00,\n",
+       "             1870-10-16 12:00:00, 1870-11-16 00:00:00, 1870-12-16 12:00:00],\n",
+       "            dtype=&#x27;object&#x27;, length=12, calendar=&#x27;standard&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-45b7f9a7-a3e2-4036-a4ad-7b4dd9369fc0' class='xr-section-summary-in' type='checkbox'  ><label for='section-45b7f9a7-a3e2-4036-a4ad-7b4dd9369fc0' class='xr-section-summary' >Attributes: <span>(33)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.11; ODS-2.5</dd><dt><span>activity_id :</span></dt><dd>obs4MIPs</dd><dt><span>contact :</span></dt><dd>nick.rayner@METOFFICE.GOV</dd><dt><span>creation_date :</span></dt><dd>2025-04-15T18:42:24Z</dd><dt><span>data_specs_version :</span></dt><dd>ODS-2.5</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>further_info_url :</span></dt><dd>.</dd><dt><span>grid :</span></dt><dd>1x1 degree latitude x longitude</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>history :</span></dt><dd>2025-04-15T18:42:24Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.11; ODS-2.5 and CF standards.</dd><dt><span>institution :</span></dt><dd>Met Office Hadley Centre</dd><dt><span>institution_id :</span></dt><dd>MOHC</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>nominal_resolution :</span></dt><dd>250 km</dd><dt><span>originData_URL :</span></dt><dd>https://www.metoffice.gov.uk/hadobs/hadisst/</dd><dt><span>product :</span></dt><dd>observations</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>references :</span></dt><dd>Rayner, N. A., Parker, D. E., Horton, E. B., Folland, C. K., Alexander, L. V., Rowell, D. P., Kent, E. C., Kaplan, A.  Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century J. Geophys. Res .Vol. 108, No. D14, 4407 10.1029/2002JD002670</dd><dt><span>region :</span></dt><dd>global_ocean</dd><dt><span>source :</span></dt><dd>HadISST-1-1 1-1 (2016): HadISST 1.1 monthly average sea surface temperature</dd><dt><span>source_id :</span></dt><dd>HadISST-1-1</dd><dt><span>source_type :</span></dt><dd>satellite_blended</dd><dt><span>source_version_number :</span></dt><dd>1-1</dd><dt><span>table_id :</span></dt><dd>obs4MIPs_Amon</dd><dt><span>table_info :</span></dt><dd>Creation Date:(18 November 2020) MD5:32d27e9b12684c6b7fed2f785177cdfe</dd><dt><span>title :</span></dt><dd>HadISST1.1 prepared for obs4MIPs (ODS2.5)</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14102/a5a2c670-b960-49c2-9833-7b40ff4fc0ec</dd><dt><span>variable_id :</span></dt><dd>ts</dd><dt><span>variant_info :</span></dt><dd>obs4MIPs-compliant product prepared by PCMDI (ODS2.5)</dd><dt><span>variant_label :</span></dt><dd>PCMDI</dd><dt><span>license :</span></dt><dd>Data in this file is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (https://creativecommons.org/licenses)</dd><dt><span>cmor_version :</span></dt><dd>3.9.0</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 3MB\n",
+       "Dimensions:    (time: 12, bnds: 2, lat: 180, lon: 360)\n",
+       "Coordinates:\n",
+       "  * lat        (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5\n",
+       "  * lon        (lon) float64 3kB 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5\n",
+       "  * time       (time) object 96B 1870-01-16 12:00:00 ... 1870-12-16 12:00:00\n",
+       "Dimensions without coordinates: bnds\n",
+       "Data variables:\n",
+       "    time_bnds  (time, bnds) object 192B ...\n",
+       "    lat_bnds   (lat, bnds) float64 3kB ...\n",
+       "    lon_bnds   (lon, bnds) float64 6kB ...\n",
+       "    ts         (time, lat, lon) float32 3MB ...\n",
+       "Attributes: (12/33)\n",
+       "    Conventions:            CF-1.11; ODS-2.5\n",
+       "    activity_id:            obs4MIPs\n",
+       "    contact:                nick.rayner@METOFFICE.GOV\n",
+       "    creation_date:          2025-04-15T18:42:24Z\n",
+       "    data_specs_version:     ODS-2.5\n",
+       "    external_variables:     areacella\n",
+       "    ...                     ...\n",
+       "    tracking_id:            hdl:21.14102/a5a2c670-b960-49c2-9833-7b40ff4fc0ec\n",
+       "    variable_id:            ts\n",
+       "    variant_info:           obs4MIPs-compliant product prepared by PCMDI (ODS...\n",
+       "    variant_label:          PCMDI\n",
+       "    license:                Data in this file is licensed under a Creative Co...\n",
+       "    cmor_version:           3.9.0"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x159775940>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ds[data_var].isel(time=0).plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Spatial Average\n",
+    "\n",
+    "### 2.1 Default average"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "291.17218278759884"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds.isel(time=0).spatial.average(data_var)[data_var].item()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.2 Find data coverage by number of grid point\n",
+    "\n",
+    "Limit by fraction of data coverage calculated by number of grid points supposed to NOT work, because actual grid area weight is considered."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total number of values (by grid point): 64800\n",
+      "Number of NaN values (by grid point): 22454\n",
+      "Data coverage fraction (by grid point): 0.6534876543209877\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Count nan in the data\n",
+    "ds0 = ds.isel(time=0)\n",
+    "data = ds0[data_var]\n",
+    "\n",
+    "total_values = data.size\n",
+    "nan_count = data.isnull().sum().item()\n",
+    "coverage_fraction = (total_values - nan_count) / total_values\n",
+    "\n",
+    "print(f\"Total number of values (by grid point): {total_values}\")\n",
+    "print(f\"Number of NaN values (by grid point): {nan_count}\")\n",
+    "print(f\"Data coverage fraction (by grid point): {coverage_fraction}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "291.17218278759884"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds.isel(time=0).spatial.average(data_var, min_weight=0.6)[data_var].item()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.3 Check data coverage by area weight"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sum of weights where data is NaN: 11513.040731745683\n",
+      "Sum of all weights: 41253.4848528469\n",
+      "Data coverage fraction (by weight): 0.7209195593338784\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculate weights based on the area represented by each grid point\n",
+    "weights = np.cos(np.deg2rad(data[\"lat\"]))  # Cosine of latitude (in radians)\n",
+    "\n",
+    "# Add weights as a DataArray\n",
+    "weights_da = xr.DataArray(weights, coords={\"lat\": data[\"lat\"]}, dims=[\"lat\"])\n",
+    "\n",
+    "# Broadcast weights across longitude\n",
+    "weights_2d = weights_da.expand_dims(dim={\"lon\": data[\"lon\"]})\n",
+    "\n",
+    "# Mask weights where data is NaN\n",
+    "weights_nan = weights_2d.where(data.isnull())\n",
+    "\n",
+    "# Sum of weights where data is NaN\n",
+    "sum_weights_nan = weights_nan.sum()\n",
+    "\n",
+    "# Sum of weights from all grid points\n",
+    "sum_weights_2d = weights_2d.sum()\n",
+    "\n",
+    "# Coverage fraction\n",
+    "coverage_fraction_by_wgt = (sum_weights_2d.values - sum_weights_nan.values) / sum_weights_2d.values\n",
+    "\n",
+    "print(f\"Sum of weights where data is NaN: {sum_weights_nan.values}\")\n",
+    "print(f\"Sum of all weights: {sum_weights_2d.values}\")\n",
+    "print(f\"Data coverage fraction (by weight): {coverage_fraction_by_wgt}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "291.17218278759884"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds.isel(time=0).spatial.average(data_var, min_weight=0.72)[data_var].item()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "nan"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds.isel(time=0).spatial.average(data_var, min_weight=0.73)[data_var].item()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "xcdat_dev_20250429",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}