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"}\n",
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".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
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"</style><pre class='xr-text-repr-fallback'><xarray.Dataset>\n",
"Dimensions: (cloud_layer: 3, range_levels: 770, sky_condition_layer: 5, time: 5426)\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 2023-05-07T00:05:22.2...\n",
" * range_levels (range_levels) int32 1 2 3 4 ... 768 769 770\n",
" * cloud_layer (cloud_layer) int32 1 2 3\n",
" * sky_condition_layer (sky_condition_layer) int32 1 2 3 4 5\n",
"Data variables: (12/15)\n",
" day_of_year (time) float32 127.0 127.0 ... 164.7 164.7\n",
" year (time) int32 2023 2023 2023 ... 2023 2023 2023\n",
" month (time) int32 5 5 5 5 5 5 5 5 ... 6 6 6 6 6 6 6\n",
" day (time) int32 7 7 7 7 7 7 ... 13 13 13 13 13 13\n",
" hour (time) int32 0 0 0 0 0 0 ... 15 15 15 15 16 16\n",
" minute (time) int32 5 15 25 35 45 ... 35 45 55 5 15\n",
" ... ...\n",
" vertical_visibility (time) float32 nan nan nan nan ... nan nan nan\n",
" highest_detected_signal (time) float32 nan nan nan nan ... nan nan nan\n",
" cloud_base_altitude (time, cloud_layer) float32 836.0 ... nan\n",
" sky_condition_cloud_fraction (time, sky_condition_layer) float32 7.0 ......\n",
" sky_condition_cloud_altitude (time, sky_condition_layer) float32 775.0 ....\n",
" backscatter_profile (time, range_levels) float32 0.0002657 ... ...\n",
"Attributes: (12/27)\n",
" Conventions: CF-1.8\n",
" source: Ceilometer\n",
" instrument_model: Vaisala Ceilometer CL31\n",
" creator_name: Sonja Murto\n",
" creator_email: sonja.murto@misu.su.se\n",
" creator_url: https://orcid.org/0000-0002-4966-9077\n",
" ... ...\n",
" time_coverage_start: 2023-05-07T00:05:22\n",
" time_coverage_end: 2023-06-13T16:15:07\n",
" geospatial_bounds: 80.52392166666667N, -3.87377499999...\n",
" platform_altitude: Located at approximately 25 m a.s.l\n",
" location_keywords: Oden, Arctic Ocean, Fram Strait, a...\n",
" comments: This file consists of 10 min avera...</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-0de8e853-eac7-49fb-913d-902a31a83705' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0de8e853-eac7-49fb-913d-902a31a83705' 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'>cloud_layer</span>: 3</li><li><span class='xr-has-index'>range_levels</span>: 770</li><li><span class='xr-has-index'>sky_condition_layer</span>: 5</li><li><span class='xr-has-index'>time</span>: 5426</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5beb8c37-6306-40f8-b92e-1859c60fd356' class='xr-section-summary-in' type='checkbox' checked><label for='section-5beb8c37-6306-40f8-b92e-1859c60fd356' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2023-05-07T00:05:22.260333 ... 2...</div><input id='attrs-cfa487ed-fb71-4b63-8143-32418a5917af' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cfa487ed-fb71-4b63-8143-32418a5917af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf0f4676-0b16-48be-bd52-ed959c9f40b4' class='xr-var-data-in' type='checkbox'><label for='data-bf0f4676-0b16-48be-bd52-ed959c9f40b4' 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(['2023-05-07T00:05:22.260333000', '2023-05-07T00:15:37.251150000',\n",
" '2023-05-07T00:25:37.240749000', ..., '2023-06-13T15:55:07.325650000',\n",
" '2023-06-13T16:05:07.314251000', '2023-06-13T16:15:07.309900000'],\n",
" dtype='datetime64[ns]')</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range_levels</span></div><div class='xr-var-dims'>(range_levels)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 ... 766 767 768 769 770</div><input id='attrs-820a0f3e-15c8-497a-b876-d08557dd3923' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-820a0f3e-15c8-497a-b876-d08557dd3923' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c05f87a-090d-42eb-a28a-a27c56b5e617' class='xr-var-data-in' type='checkbox'><label for='data-7c05f87a-090d-42eb-a28a-a27c56b5e617' 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([ 1, 2, 3, ..., 768, 769, 770], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>cloud_layer</span></div><div class='xr-var-dims'>(cloud_layer)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3</div><input id='attrs-d5b74fb3-7e8f-44f7-9cd7-d5317f9e04ad' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d5b74fb3-7e8f-44f7-9cd7-d5317f9e04ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e964b9f1-d273-4c9a-b18f-ec44251eba25' class='xr-var-data-in' type='checkbox'><label for='data-e964b9f1-d273-4c9a-b18f-ec44251eba25' 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([1, 2, 3], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sky_condition_layer</span></div><div class='xr-var-dims'>(sky_condition_layer)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4 5</div><input id='attrs-6b0d5355-cc3a-42ee-afc6-5d57ba2a9e0a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6b0d5355-cc3a-42ee-afc6-5d57ba2a9e0a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3bbafa4-2e08-4762-b750-9f3d333a9dd8' class='xr-var-data-in' type='checkbox'><label for='data-c3bbafa4-2e08-4762-b750-9f3d333a9dd8' 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([1, 2, 3, 4, 5], dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-63484bfa-0ca5-4245-ad54-06252bea5666' class='xr-section-summary-in' type='checkbox' ><label for='section-63484bfa-0ca5-4245-ad54-06252bea5666' class='xr-section-summary' >Data variables: <span>(15)</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>day_of_year</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>127.0 127.0 127.0 ... 164.7 164.7</div><input id='attrs-8adef85e-eb26-4a78-8de7-9470a54b3040' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8adef85e-eb26-4a78-8de7-9470a54b3040' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-509411ef-6c81-4ceb-a397-d21f318c43ad' class='xr-var-data-in' type='checkbox'><label for='data-509411ef-6c81-4ceb-a397-d21f318c43ad' 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>type :</span></dt><dd>float32</dd><dt><span>dimension :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Day of Year</dd><dt><span>description :</span></dt><dd>time as decimal day of year</dd></dl></div><div class='xr-var-data'><pre>array([127.00373, 127.01085, 127.01779, ..., 164.66328, 164.67023,\n",
" 164.67717], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>year</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>2023 2023 2023 ... 2023 2023 2023</div><input id='attrs-d80c27b2-d933-4e1c-82c5-14d4471e533e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d80c27b2-d933-4e1c-82c5-14d4471e533e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-585bacc6-7667-4258-a8db-f008ab92375c' class='xr-var-data-in' type='checkbox'><label for='data-585bacc6-7667-4258-a8db-f008ab92375c' 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>type :</span></dt><dd>int32</dd><dt><span>dimension :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Year</dd></dl></div><div class='xr-var-data'><pre>array([2023, 2023, 2023, ..., 2023, 2023, 2023], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>month</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>5 5 5 5 5 5 5 5 ... 6 6 6 6 6 6 6 6</div><input id='attrs-89606c92-36a3-4e4e-acec-4838f6453d90' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-89606c92-36a3-4e4e-acec-4838f6453d90' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c489efaa-7ff9-487f-a5a0-bb57cb1cf5aa' class='xr-var-data-in' type='checkbox'><label for='data-c489efaa-7ff9-487f-a5a0-bb57cb1cf5aa' 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>type :</span></dt><dd>int32</dd><dt><span>dimension :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Month</dd></dl></div><div class='xr-var-data'><pre>array([5, 5, 5, ..., 6, 6, 6], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>day</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>7 7 7 7 7 7 7 ... 13 13 13 13 13 13</div><input id='attrs-b08ab301-3694-4184-bd8b-23567512b7ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b08ab301-3694-4184-bd8b-23567512b7ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31e79e37-2c34-4dc2-b509-b2b9e4de4013' class='xr-var-data-in' type='checkbox'><label for='data-31e79e37-2c34-4dc2-b509-b2b9e4de4013' 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>type :</span></dt><dd>int32</dd><dt><span>dimension :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Day</dd></dl></div><div class='xr-var-data'><pre>array([ 7, 7, 7, ..., 13, 13, 13], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hour</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 1 ... 15 15 15 15 16 16</div><input id='attrs-75fc1e53-ae04-478b-bc8b-90beeb59e838' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-75fc1e53-ae04-478b-bc8b-90beeb59e838' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df0f316e-5a45-4e9f-a6a3-4ac1f86c96bc' class='xr-var-data-in' type='checkbox'><label for='data-df0f316e-5a45-4e9f-a6a3-4ac1f86c96bc' 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>type :</span></dt><dd>int32</dd><dt><span>dimension :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Hour</dd></dl></div><div class='xr-var-data'><pre>array([ 0, 0, 0, ..., 15, 16, 16], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>minute</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>5 15 25 35 45 55 ... 35 45 55 5 15</div><input id='attrs-e401b26a-8af5-4d28-bda7-00df4a7fbffa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e401b26a-8af5-4d28-bda7-00df4a7fbffa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee279056-d4f0-4f1f-9798-721648f371d8' class='xr-var-data-in' type='checkbox'><label for='data-ee279056-d4f0-4f1f-9798-721648f371d8' 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>type :</span></dt><dd>int32</dd><dt><span>dimension :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Minute</dd></dl></div><div class='xr-var-data'><pre>array([ 5, 15, 25, ..., 55, 5, 15], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>second</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>22 37 37 37 37 37 ... 7 7 7 7 7 7</div><input id='attrs-1a8b8d50-6ae5-420a-8f14-7572d5eb9b98' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a8b8d50-6ae5-420a-8f14-7572d5eb9b98' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-484fae0e-e414-4f83-981d-5afaf841fa6a' class='xr-var-data-in' type='checkbox'><label for='data-484fae0e-e414-4f83-981d-5afaf841fa6a' 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>type :</span></dt><dd>int32</dd><dt><span>dimension :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Second</dd><dt><span>description :</span></dt><dd>Time averaged to closest second</dd></dl></div><div class='xr-var-data'><pre>array([22, 37, 37, ..., 7, 7, 7], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ceilometer_range</span></div><div class='xr-var-dims'>(range_levels)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>35 45 55 65 ... 7695 7705 7715 7725</div><input id='attrs-a8cc8705-aa0c-4d41-8a6f-4b993c2f38f5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a8cc8705-aa0c-4d41-8a6f-4b993c2f38f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3fde38ee-3881-4534-b05f-d693e5f764af' class='xr-var-data-in' type='checkbox'><label for='data-3fde38ee-3881-4534-b05f-d693e5f764af' 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>type :</span></dt><dd>int32</dd><dt><span>dimension :</span></dt><dd>range_levels</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Ceilometer Range</dd><dt><span>description :</span></dt><dd>Ranges for the ceilometer backscatter profile, including the instrument height</dd></dl></div><div class='xr-var-data'><pre>array([ 35, 45, 55, 65, 75, 85, 95, 105, 115, 125, 135,\n",
3196
3197
3198
3199
3200
3201
3202
3203
3204
3205
3206
3207
3208
3209
3210
3211
3212
3213
3214
3215
3216
3217
3218
3219
3220
3221
3222
3223
3224
3225
3226
3227
3228
3229
3230
3231
3232
3233
3234
" 145, 155, 165, 175, 185, 195, 205, 215, 225, 235, 245,\n",
" 255, 265, 275, 285, 295, 305, 315, 325, 335, 345, 355,\n",
" 365, 375, 385, 395, 405, 415, 425, 435, 445, 455, 465,\n",
" 475, 485, 495, 505, 515, 525, 535, 545, 555, 565, 575,\n",
" 585, 595, 605, 615, 625, 635, 645, 655, 665, 675, 685,\n",
" 695, 705, 715, 725, 735, 745, 755, 765, 775, 785, 795,\n",
" 805, 815, 825, 835, 845, 855, 865, 875, 885, 895, 905,\n",
" 915, 925, 935, 945, 955, 965, 975, 985, 995, 1005, 1015,\n",
" 1025, 1035, 1045, 1055, 1065, 1075, 1085, 1095, 1105, 1115, 1125,\n",
" 1135, 1145, 1155, 1165, 1175, 1185, 1195, 1205, 1215, 1225, 1235,\n",
" 1245, 1255, 1265, 1275, 1285, 1295, 1305, 1315, 1325, 1335, 1345,\n",
" 1355, 1365, 1375, 1385, 1395, 1405, 1415, 1425, 1435, 1445, 1455,\n",
" 1465, 1475, 1485, 1495, 1505, 1515, 1525, 1535, 1545, 1555, 1565,\n",
" 1575, 1585, 1595, 1605, 1615, 1625, 1635, 1645, 1655, 1665, 1675,\n",
" 1685, 1695, 1705, 1715, 1725, 1735, 1745, 1755, 1765, 1775, 1785,\n",
" 1795, 1805, 1815, 1825, 1835, 1845, 1855, 1865, 1875, 1885, 1895,\n",
" 1905, 1915, 1925, 1935, 1945, 1955, 1965, 1975, 1985, 1995, 2005,\n",
" 2015, 2025, 2035, 2045, 2055, 2065, 2075, 2085, 2095, 2105, 2115,\n",
" 2125, 2135, 2145, 2155, 2165, 2175, 2185, 2195, 2205, 2215, 2225,\n",
"...\n",
" 5645, 5655, 5665, 5675, 5685, 5695, 5705, 5715, 5725, 5735, 5745,\n",
" 5755, 5765, 5775, 5785, 5795, 5805, 5815, 5825, 5835, 5845, 5855,\n",
" 5865, 5875, 5885, 5895, 5905, 5915, 5925, 5935, 5945, 5955, 5965,\n",
" 5975, 5985, 5995, 6005, 6015, 6025, 6035, 6045, 6055, 6065, 6075,\n",
" 6085, 6095, 6105, 6115, 6125, 6135, 6145, 6155, 6165, 6175, 6185,\n",
" 6195, 6205, 6215, 6225, 6235, 6245, 6255, 6265, 6275, 6285, 6295,\n",
" 6305, 6315, 6325, 6335, 6345, 6355, 6365, 6375, 6385, 6395, 6405,\n",
" 6415, 6425, 6435, 6445, 6455, 6465, 6475, 6485, 6495, 6505, 6515,\n",
" 6525, 6535, 6545, 6555, 6565, 6575, 6585, 6595, 6605, 6615, 6625,\n",
" 6635, 6645, 6655, 6665, 6675, 6685, 6695, 6705, 6715, 6725, 6735,\n",
" 6745, 6755, 6765, 6775, 6785, 6795, 6805, 6815, 6825, 6835, 6845,\n",
" 6855, 6865, 6875, 6885, 6895, 6905, 6915, 6925, 6935, 6945, 6955,\n",
" 6965, 6975, 6985, 6995, 7005, 7015, 7025, 7035, 7045, 7055, 7065,\n",
" 7075, 7085, 7095, 7105, 7115, 7125, 7135, 7145, 7155, 7165, 7175,\n",
" 7185, 7195, 7205, 7215, 7225, 7235, 7245, 7255, 7265, 7275, 7285,\n",
" 7295, 7305, 7315, 7325, 7335, 7345, 7355, 7365, 7375, 7385, 7395,\n",
" 7405, 7415, 7425, 7435, 7445, 7455, 7465, 7475, 7485, 7495, 7505,\n",
" 7515, 7525, 7535, 7545, 7555, 7565, 7575, 7585, 7595, 7605, 7615,\n",
" 7625, 7635, 7645, 7655, 7665, 7675, 7685, 7695, 7705, 7715, 7725],\n",
" dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>flag_cloudcode</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 1 1 1 1 1 2 1 ... 1 1 1 1 1 1 1 1</div><input id='attrs-26ff05a0-f974-4ce5-b2d7-a0ccdf1bb2d8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-26ff05a0-f974-4ce5-b2d7-a0ccdf1bb2d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9245e2d-0b96-47f1-86c2-3cdfe14646c5' class='xr-var-data-in' type='checkbox'><label for='data-b9245e2d-0b96-47f1-86c2-3cdfe14646c5' 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>type :</span></dt><dd>int32</dd><dt><span>dimension :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Data flag: Cloudcode</dd><dt><span>flag_values :</span></dt><dd>-1,0,1,2,3,4</dd><dt><span>flag_meanings :</span></dt><dd>missing_data\n",
"no_significant_backscatter\n",
"one_cloud_base_detected\n",
"two_cloud_bases_detected\n",
"three_cloud_bases_detected\n",
"full_obscuration</dd><dt><span>description :</span></dt><dd>Code for number of cloud bases detected; see Readme document for more information</dd></dl></div><div class='xr-var-data'><pre>array([1, 1, 1, ..., 1, 1, 1], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vertical_visibility</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-1a94d2ce-6821-49fb-899d-6f7e58e12680' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a94d2ce-6821-49fb-899d-6f7e58e12680' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-917eaa33-53b6-473b-bbe4-c3ee817c562f' class='xr-var-data-in' type='checkbox'><label for='data-917eaa33-53b6-473b-bbe4-c3ee817c562f' 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>type :</span></dt><dd>float32</dd><dt><span>dimension :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Vertical Visibilility</dd><dt><span>description :</span></dt><dd>Vertical visibility given in case of obscured cloud base (at flag_cloudcode 4), else NaN</dd></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>highest_detected_signal</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-4e291541-9b36-491a-ae30-9d9a86b9bcee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e291541-9b36-491a-ae30-9d9a86b9bcee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16333c46-1f04-4e3e-8d33-17259e0eaf02' class='xr-var-data-in' type='checkbox'><label for='data-16333c46-1f04-4e3e-8d33-17259e0eaf02' 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>type :</span></dt><dd>float32</dd><dt><span>dimension :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Highest Signal Detected</dd><dt><span>description :</span></dt><dd>Highest signal detected given in case of obscured cloud base (at flag_cloudcode 4), else NaN</dd></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cloud_base_altitude</span></div><div class='xr-var-dims'>(time, cloud_layer)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>836.0 950.0 nan ... 366.0 nan nan</div><input id='attrs-59711047-cbd8-425a-bc01-c46079fd9a31' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-59711047-cbd8-425a-bc01-c46079fd9a31' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2f44738-9530-48a6-b76a-c08043cae787' class='xr-var-data-in' type='checkbox'><label for='data-f2f44738-9530-48a6-b76a-c08043cae787' 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>type :</span></dt><dd>float32</dd><dt><span>dimension :</span></dt><dd>time, cloud_layer</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Cloud Base Altitude</dd><dt><span>description :</span></dt><dd>cloud base height of 1-3 cloud layers; NaN if no layer detected. Instrument height incorporated.</dd></dl></div><div class='xr-var-data'><pre>array([[ 835.9524, 950. , nan],\n",
" [ 728.5 , 915. , nan],\n",
" [ 741.5 , 1001.25 , 1235. ],\n",
" ...,\n",
" [ 530. , 755. , nan],\n",
" [ 457.5 , nan, nan],\n",
" [ 366. , nan, nan]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sky_condition_cloud_fraction</span></div><div class='xr-var-dims'>(time, sky_condition_layer)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>7.0 8.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-c5b1e43f-0126-4ce2-82df-b29a674bc981' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c5b1e43f-0126-4ce2-82df-b29a674bc981' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12afca6c-4131-4923-a4d1-a8a896b0956d' class='xr-var-data-in' type='checkbox'><label for='data-12afca6c-4131-4923-a4d1-a8a896b0956d' 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>type :</span></dt><dd>float32</dd><dt><span>dimension :</span></dt><dd>time, sky_condition_layer</dd><dt><span>units :</span></dt><dd>octal</dd><dt><span>long_name :</span></dt><dd>Sky Condition Cloud Fraction</dd><dt><span>description :</span></dt><dd>Cloud fraction calculated with the sky condition algorithm. 0-8 = cloud coverage of up to 5 levels; 9 = obscuration. NaN = missing data or no detected layer.</dd></dl></div><div class='xr-var-data'><pre>array([[7., 8., 0., 0., 0.],\n",
" [7., 8., 0., 0., 0.],\n",
" [8., 0., 0., 0., 0.],\n",
" ...,\n",
" [8., 0., 0., 0., 0.],\n",
" [8., 0., 0., 0., 0.],\n",
" [8., 0., 0., 0., 0.]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sky_condition_cloud_altitude</span></div><div class='xr-var-dims'>(time, sky_condition_layer)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>775.0 1.525e+03 nan ... nan nan nan</div><input id='attrs-1a2e12a9-91b5-4eb9-9cc8-25bd16acdc93' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a2e12a9-91b5-4eb9-9cc8-25bd16acdc93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dab8b02c-2e27-426d-a4b5-80fd6772af05' class='xr-var-data-in' type='checkbox'><label for='data-dab8b02c-2e27-426d-a4b5-80fd6772af05' 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>type :</span></dt><dd>float32</dd><dt><span>dimension :</span></dt><dd>time, sky_condition_layer</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Sky Condition Cloud Altitude</dd><dt><span>description :</span></dt><dd>Cloud layer height calculated with the sky condition algorithm. Cloud layer height given for 1-5 sky condition layers; NaN if no layer detected. Vertical visibility is reported as height if obscuration (at sky_condition_cloud_fraction 9). Instrument height incorporated.</dd></dl></div><div class='xr-var-data'><pre>array([[ 775., 1525., nan, nan, nan],\n",
" [ 725., 1525., nan, nan, nan],\n",
" [ 675., nan, nan, nan, nan],\n",
" ...,\n",
" [ 235., nan, nan, nan, nan],\n",
" [ 435., nan, nan, nan, nan],\n",
" [ 365., nan, nan, nan, nan]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>backscatter_profile</span></div><div class='xr-var-dims'>(time, range_levels)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0002657 0.0002267 ... 8.85e-05</div><input id='attrs-5f4912d1-f30a-49ca-a74d-f11536780c47' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f4912d1-f30a-49ca-a74d-f11536780c47' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8d8cd40-7b51-48a4-9b80-617dac476603' class='xr-var-data-in' type='checkbox'><label for='data-c8d8cd40-7b51-48a4-9b80-617dac476603' 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>type :</span></dt><dd>float32</dd><dt><span>dimension :</span></dt><dd>time, range_levels</dd><dt><span>units :</span></dt><dd>1 km-1 steradians-1</dd><dt><span>long_name :</span></dt><dd>Backscatter Profile</dd><dt><span>description :</span></dt><dd>backscatter coefficient profile</dd></dl></div><div class='xr-var-data'><pre>array([[ 2.6571428e-04, 2.2666667e-04, 2.3238095e-04, ...,\n",
" -8.8095236e-05, 9.3809525e-05, 7.9285714e-04],\n",
" [ 8.9550001e-04, 6.9900003e-04, 6.8750000e-04, ...,\n",
" 2.9600001e-04, 7.1100000e-04, 1.6650000e-04],\n",
" [ 1.3195000e-03, 1.2290000e-03, 1.2585000e-03, ...,\n",
" -1.2700001e-04, -8.0500002e-04, -4.8250001e-04],\n",
" ...,\n",
" [ 4.4109998e-03, 2.7594999e-03, 3.5110000e-03, ...,\n",
" -5.7500001e-05, -4.5450000e-04, -5.8300002e-04],\n",
" [ 1.7119501e-02, 9.5734997e-03, 1.1017000e-02, ...,\n",
" -2.1500000e-05, 1.4850000e-04, -8.4350002e-04],\n",
" [ 1.3062000e-02, 9.7855004e-03, 1.6995000e-02, ...,\n",
" -5.5699999e-04, 9.7999997e-05, 8.8499997e-05]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-066c0be0-1d78-4e17-88fd-4564023fd3ae' class='xr-section-summary-in' type='checkbox' ><label for='section-066c0be0-1d78-4e17-88fd-4564023fd3ae' class='xr-section-summary' >Attributes: <span>(27)</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-1.8</dd><dt><span>source :</span></dt><dd>Ceilometer</dd><dt><span>instrument_model :</span></dt><dd>Vaisala Ceilometer CL31</dd><dt><span>creator_name :</span></dt><dd>Sonja Murto</dd><dt><span>creator_email :</span></dt><dd>sonja.murto@misu.su.se</dd><dt><span>creator_url :</span></dt><dd>https://orcid.org/0000-0002-4966-9077</dd><dt><span>institution :</span></dt><dd>Stockholm University</dd><dt><span>processing_software :</span></dt><dd>Matlab (for creating the matlab file) and a jupyter notebook script (netCDF)</dd><dt><span>sampling_interval :</span></dt><dd>original 30s; here 10min averages</dd><dt><span>product_version :</span></dt><dd>v01</dd><dt><span>last_revised_date :</span></dt><dd>2024-05-31T15:00:00</dd><dt><span>project :</span></dt><dd>ARTofMELT</dd><dt><span>project_principal_investigator :</span></dt><dd>Michael Tjernström</dd><dt><span>project_principal_investigator_email :</span></dt><dd>michaelt@misu.su.se</dd><dt><span>project_principal_investigator_url :</span></dt><dd>https://orcid.org/0000-0002-6908-7410</dd><dt><span>acknowledgement :</span></dt><dd> Knut och Alice Wallenbergs Stiftelse, Grant 2016-0024</dd><dt><span>platform :</span></dt><dd>Swedish Icebreaker Oden</dd><dt><span>platform_type :</span></dt><dd>On Oden's 7th deck above the bridge</dd><dt><span>deployment_mode :</span></dt><dd>ship</dd><dt><span>title :</span></dt><dd>Ceilometer cloud base height, vertical visibility and backscatter profiles</dd><dt><span>feature_type :</span></dt><dd>time series</dd><dt><span>time_coverage_start :</span></dt><dd>2023-05-07T00:05:22</dd><dt><span>time_coverage_end :</span></dt><dd>2023-06-13T16:15:07</dd><dt><span>geospatial_bounds :</span></dt><dd>80.52392166666667N, -3.8737749999999997E, 78.04355166666666N, 15.660881666666667E</dd><dt><span>platform_altitude :</span></dt><dd>Located at approximately 25 m a.s.l</dd><dt><span>location_keywords :</span></dt><dd>Oden, Arctic Ocean, Fram Strait, atmosphere, on the ship</dd><dt><span>comments :</span></dt><dd>This file consists of 10 min averages of ceilometer data measured with the Vaisala Ceilometer CL31 that was located on the 7th deck, above the bridge (at approximately 25m).The sky condition measurements are time averages to represent an area average. The vertical resolution is 10m * 770, but the measurement height (25m) is included in the backscatter profile ranges, as well as in the cloud base heights (cloud_base_altitude and sky_condition_cloud_altitude). Geospatial bounds are taken from the gps location of the weather station dataset located on Oden. Time variables month, day, hour, minute and second are approximated to the nearest second. Data produced by Sonja Murto. See the document - Readme_CL.txt - for more details.</dd></dl></div></li></ul></div></div>"
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],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (cloud_layer: 3, range_levels: 770, sky_condition_layer: 5, time: 5426)\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 2023-05-07T00:05:22.2...\n",
" * range_levels (range_levels) int32 1 2 3 4 ... 768 769 770\n",
" * cloud_layer (cloud_layer) int32 1 2 3\n",
" * sky_condition_layer (sky_condition_layer) int32 1 2 3 4 5\n",
"Data variables: (12/15)\n",
" day_of_year (time) float32 127.0 127.0 ... 164.7 164.7\n",
" year (time) int32 2023 2023 2023 ... 2023 2023 2023\n",
" month (time) int32 5 5 5 5 5 5 5 5 ... 6 6 6 6 6 6 6\n",
" day (time) int32 7 7 7 7 7 7 ... 13 13 13 13 13 13\n",
" hour (time) int32 0 0 0 0 0 0 ... 15 15 15 15 16 16\n",
" minute (time) int32 5 15 25 35 45 ... 35 45 55 5 15\n",
" ... ...\n",
" vertical_visibility (time) float32 nan nan nan nan ... nan nan nan\n",
" highest_detected_signal (time) float32 nan nan nan nan ... nan nan nan\n",
" cloud_base_altitude (time, cloud_layer) float32 836.0 ... nan\n",
" sky_condition_cloud_fraction (time, sky_condition_layer) float32 7.0 ......\n",
" sky_condition_cloud_altitude (time, sky_condition_layer) float32 775.0 ....\n",
" backscatter_profile (time, range_levels) float32 0.0002657 ... ...\n",
"Attributes: (12/27)\n",
" Conventions: CF-1.8\n",
" source: Ceilometer\n",
" instrument_model: Vaisala Ceilometer CL31\n",
" creator_name: Sonja Murto\n",
" creator_email: sonja.murto@misu.su.se\n",
" creator_url: https://orcid.org/0000-0002-4966-9077\n",
" ... ...\n",
" time_coverage_start: 2023-05-07T00:05:22\n",
" time_coverage_end: 2023-06-13T16:15:07\n",
" geospatial_bounds: 80.52392166666667N, -3.87377499999...\n",
" platform_altitude: Located at approximately 25 m a.s.l\n",
" location_keywords: Oden, Arctic Ocean, Fram Strait, a...\n",
" comments: This file consists of 10 min avera..."
]
},
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds_all"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"#save to netCDF\n",
"ds_all.to_netcdf(load_data + 'CL31_ceilometer_ARTofMELT_20230507_20230613_10min_v01.nc')"
]
},
{
"cell_type": "code",
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"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#test plot\n",
"ds_all.sel(cloud_layer=1).cloud_base_altitude.plot(color='r',label='cloud_layer = 1')\n",
"ds_all.sel(cloud_layer=2).cloud_base_altitude.plot(color='b',label='cloud_layer = 2')\n",
"ds_all.sel(cloud_layer=3).cloud_base_altitude.plot(color='y',label='cloud_layer = 3')\n",
"\n",
"plt.title('')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create netCDF from 20 min average files - remove times with no data points"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"CL_SM_avg = scipy.io.loadmat(load_data + 'CL31_ceilometer_ARTofMELT_20230507_20230613_20min_v01.mat',struct_as_record=True)"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dict_keys(['__header__', '__version__', '__globals__', 'cl31_av'])"
]
},
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"CL_SM_avg.keys()"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('mday', 'doy', 'cloudcode', 'base_ht', 'vert_vis', 'high_sig', 'sc_frac', 'sc_ht', 'bs_prof', 'ceil_range', 'time')\n"
]
}
],
"source": [
"# get the data in the mat file\n",
"Names=CL_SM_avg['cl31_av'].dtype.names\n",
"ndata = {n: CL_SM_avg['cl31_av'][n][0, 0] for n in Names}\n",
"print(Names)"
]
},
{
"cell_type": "code",
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"metadata": {},
"outputs": [],
"source": [
"# create pandas dataframe from the data\n",
"\n",
"# 1D variables\n",
"\n",
"DF_20min=pd.DataFrame(index=range(len(ndata['doy'])),)\n",
"DF_20min['doy']=np.array(list(itertools.chain.from_iterable(ndata['doy'])),dtype=float)\n",
"DF_20min['cloudcode']=np.array(list(itertools.chain.from_iterable(ndata['cloudcode'])),dtype=float)\n",
"DF_20min['vert_vis']=np.array(list(itertools.chain.from_iterable(ndata['vert_vis'])),dtype=float)\n",
"DF_20min['high_sig']=np.array(list(itertools.chain.from_iterable(ndata['high_sig'])),dtype=float)\n",
"\n",
"\n",
"#2D cloudlayer\n",
"DF_20min_cloudbh=pd.DataFrame(index=range(len(ndata['doy'])),data=np.array(ndata['base_ht'],dtype=float))\n",
"\n",
"#2D sc_layer_ht\n",
"DF_20min_scht=pd.DataFrame(index=range(len(ndata['doy'])),data=np.array(ndata['sc_ht'],dtype=float))\n",
"\n",
"#2D sc_layer_frac\n",
"DF_20min_scfrac=pd.DataFrame(index=range(len(ndata['doy'])),data=np.array(ndata['sc_frac'],dtype=float))\n",
"\n",
"#2D ranges\n",
"DF_20min_range=pd.DataFrame(index=range(len(ndata['doy'])),data=np.array(ndata['bs_prof'],dtype=float))"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2713"
]
},
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(DF_20min)"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2713 0\n"
]
}
],
"source": [
"#check for NaNs - no nans in 20min avg data\n",
"selection = DF_20min.iloc[DF_20min[~DF_20min.doy.isna()].index].index.values # size with no nans\n",
"idx_nan = DF_20min.iloc[DF_20min[DF_20min.doy.isna()].index].index.values # length of nan times\n",
"print(len(selection),len(idx_nan))"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"#select only no-nan times; for all 5 pandas dataframes\n",
"DF_20min=DF_20min.iloc[selection].reset_index(drop=True)\n",
"DF_20min_cloudbh=DF_20min_cloudbh.iloc[selection].reset_index(drop=True)\n",
"DF_20min_scht=DF_20min_scht.iloc[selection].reset_index(drop=True)\n",
"DF_20min_scfrac=DF_20min_scfrac.iloc[selection].reset_index(drop=True)\n",
"DF_20min_range=DF_20min_range.iloc[selection].reset_index(drop=True)"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2713 2713 2713 2713 2713\n"
]
}
],
"source": [
"#check for right length (same as in the beginning)\n",
"print(len(DF_20min),len(DF_20min_cloudbh),len(DF_20min_scfrac),len(DF_20min_scht),len(DF_20min_range))"
]
},
{
"cell_type": "code",
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"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>doy</th>\n",
" <th>cloudcode</th>\n",
" <th>vert_vis</th>\n",
" <th>high_sig</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [doy, cloudcode, vert_vis, high_sig]\n",
"Index: []"
]
},
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"DF_20min.iloc[DF_20min[DF_20min.doy.isna()].index] #check again for NaNs - empty!"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"# set time dimension\n",
"Time_steps=returndatetime_fromdoy(np.array(DF_20min.doy.values,dtype=float))\n",
"Times_nomicrosec=[pd.to_datetime(T).round('1s') for T in Time_steps]\n",
"Time_steps_dt64_org=[np.datetime64(t) for t in Time_steps]\n",
"Time_steps_dt64_org=np.array(Time_steps_dt64_org,dtype='datetime64[ns]')"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"# add date values\n",
"DF_20min['year']=np.array([Times_nomicrosec[i].year for i in range(len(Time_steps))],dtype=int)\n",
"DF_20min['month']=np.array([Times_nomicrosec[i].month for i in range(len(Time_steps))],dtype=int)\n",
"DF_20min['day']=np.array([Times_nomicrosec[i].day for i in range(len(Time_steps))],dtype=int)\n",
"DF_20min['hour']=np.array([Times_nomicrosec[i].hour for i in range(len(Time_steps))],dtype=int)\n",
"DF_20min['minute']=np.array([Times_nomicrosec[i].minute for i in range(len(Time_steps))],dtype=int)\n",
"DF_20min['second']=np.array([Times_nomicrosec[i].second for i in range(len(Time_steps))],dtype=int)"
]
},
{
"cell_type": "code",
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"metadata": {},
"outputs": [],
"source": [
"# create xr DataArrays\n",
"\n",
"#1D: dimension time\n",
"\n",
"da_doy=xr.DataArray(data=np.array(DF_20min['doy']).astype('float32'),name=\"day_of_year\",\n",
" dims=[\"time\"],coords=dict(time=Time_steps_dt64_org),\n",
" attrs=dict(type=\"float32\",dimension=\"time\",units=\"1\",long_name=\"Day of Year\",\n",
" description=\"time as decimal day of year\"),) #added as 1D\n",
"\n",
"#date and time in separate arrays; microseconds approximated to seconds\n",
"da_year=xr.DataArray(data=np.array(DF_20min['year']).astype('int32'),name=\"year\",dims=[\"time\"],\n",
" coords=dict(time=Time_steps_dt64_org),\n",
" attrs=dict(type=\"int32\",dimension=\"time\",units=\"1\",long_name=\"Year\"),) #added as 1D\n",
"\n",
"da_month=xr.DataArray(data=np.array(DF_20min['month']).astype('int32'),name=\"month\",dims=[\"time\"],\n",
" coords=dict(time=Time_steps_dt64_org),\n",
" attrs=dict(type=\"int32\",dimension=\"time\",units=\"1\", long_name=\"Month\"),) #added as 1D\n",
"\n",
"da_day=xr.DataArray(data=np.array(DF_20min['day']).astype('int32'),name=\"day\",dims=[\"time\"],\n",
" coords=dict(time=Time_steps_dt64_org),\n",
" attrs=dict(type=\"int32\",dimension=\"time\",units=\"1\",long_name=\"Day\"),) #added as 1D\n",
"\n",
"da_hour=xr.DataArray(data=np.array(DF_20min['hour']).astype('int32'),name=\"hour\",dims=[\"time\"],\n",
" coords=dict(time=Time_steps_dt64_org),\n",
" attrs=dict(type=\"int32\",dimension=\"time\",units=\"1\",long_name=\"Hour\"),) #added as 1D\n",
"\n",
"da_min=xr.DataArray(data=np.array(DF_20min['minute']).astype('int32'),name=\"minute\",dims=[\"time\"],\n",
" coords=dict(time=Time_steps_dt64_org),\n",
" attrs=dict(type=\"int32\",dimension=\"time\",units=\"1\",long_name=\"Minute\"),) #added as 1D\n",
"\n",
"da_sec=xr.DataArray(data=np.array(DF_20min['second']).astype('int32'),name=\"second\",dims=[\"time\"],\n",
" coords=dict(time=Time_steps_dt64_org),\n",
" attrs=dict(type=\"int32\",dimension=\"time\",units=\"1\",long_name=\"Second\",\n",
" description=\"Time averaged to closest second\"),) #added as 1D\n",
"\n",
"#add cloudcode flag\n",
"da_cloudcode=xr.DataArray(data=np.array(DF_20min['cloudcode']).astype('int32'),name=\"flag_cloudcode\",dims=[\"time\"],\n",
" coords=dict(time=Time_steps_dt64_org),\n",
" attrs=dict(type=\"int32\",dimension=\"time\",units=\"1\",\n",
" long_name='Data flag: Cloudcode',\n",
" flag_values=\"-1,0,1,2,3,4\", \n",
" flag_meanings=\"missing_data\\nno_significant_backscatter\\none_cloud_base_detected\\ntwo_cloud_bases_detected\\nthree_cloud_bases_detected\\nfull_obscuration\",\n",
" description=\"Code for number of cloud bases detected; see Readme document for more information\"),)\n",
"\n",
"da_vertvis=xr.DataArray(data=np.array(DF_20min['vert_vis']).astype('float32'),\n",
" name=\"vertical_visibility\",dims=[\"time\"],coords=dict(time=Time_steps_dt64_org),\n",
" attrs=dict(type=\"float32\",dimension=\"time\", units=\"m\",\n",
" long_name=\"Vertical Visibilility\",\n",
" description=\"Vertical visibility given in case of obscured cloud base (at flag_cloudcode 4), else NaN\"),) #added as 1D\n",
"\n",
"da_high_sig=xr.DataArray(data=np.array(DF_20min['high_sig']).astype('float32'),\n",
" name=\"highest_detected_signal\",dims=[\"time\"],coords=dict(time=Time_steps_dt64_org),\n",
" attrs=dict(type=\"float32\",dimension=\"time\", units=\"m\",\n",
" long_name=\"Highest Signal Detected\",\n",
" description=\"Highest signal detected given in case of obscured cloud base (at flag_cloudcode 4), else NaN\"),) #added as 1D\n",
"\n",
"\n",
"#1D: dimension range_levels\n",
"\n",
"da_ceilrange=xr.DataArray(data=np.array(list(itertools.chain.from_iterable(ndata['ceil_range'])),dtype=int).astype('int32'),\n",
" name=\"ceilometer_range\",dims=[\"range_levels\"],coords=dict(range_levels=range_levs),\n",
" attrs=dict(type=\"int32\",dimension=\"range_levels\",units=\"m\",\n",
" long_name=\"Ceilometer Range\",\n",
" description=\"Ranges for the ceilometer backscatter profile, including the instrument height\",),) #added as 1D\n",
"\n",
"\n",
"#2D: dimension time/cloud_layer\n",
"\n",
"\n",
"da_baseht=xr.DataArray(data=np.array(DF_20min_cloudbh,dtype=float).astype('float32'),\n",
" name=\"cloud_base_altitude\",dims=[\"time\",\"cloud_layer\"],\n",
" coords=dict(time=Time_steps_dt64_org,cloud_layer=cloud_layer_levs),\n",
" attrs=dict(type=\"float32\",dimension=\"time, cloud_layer\", units=\"m\",\n",
" long_name=\"Cloud Base Altitude\",\n",
" description=\"cloud base height of 1-3 cloud layers; NaN if no layer detected. \" +\\\n",
" \"Instrument height incorporated.\"),) #added as 2D\n",
"\n",
"#2D: dimension time/sky_condition_layer\n",
"\n",
"\n",
"\n",
"da_scfrac=xr.DataArray(data=np.array(DF_20min_scfrac,dtype=float).astype('float32'),\n",
" name=\"sky_condition_cloud_fraction\",dims=[\"time\",\"sky_condition_layer\"],\n",
" coords=dict(time=Time_steps_dt64_org,sky_condition_layer=sc_layer_levs),\n",
" attrs=dict(type=\"float32\",dimension=\"time, sky_condition_layer\", units=\"octal\",\n",
" long_name=\"Sky Condition Cloud Fraction\",\n",
" description=\"Cloud fraction calculated with the sky condition algorithm. \"+\\\n",
" \"0-8 = cloud coverage of up to 5 levels; 9 = obscuration. \" +\\\n",
" \"NaN = missing data or no detected layer.\"),) #added as 2D\n",
"\n",
"da_scht=xr.DataArray(data=np.array(DF_20min_scht,dtype=float).astype('float32'),\n",
" name=\"sky_condition_cloud_altitude\",dims=[\"time\",\"sky_condition_layer\"],\n",
" coords=dict(time=Time_steps_dt64_org,sky_condition_layer=sc_layer_levs),\n",
" attrs=dict(type=\"float32\",dimension=\"time, sky_condition_layer\", units=\"m\",\n",
" description = \"Cloud layer height calculated with the sky condition algorithm. \"+\\\n",
" \"Cloud layer height given for 1-5 sky condition layers; NaN if no layer detected. \"+\\\n",
" \"Vertical visibility is reported as height if obscuration (at sky_condition_cloud_fraction 9). \"+\\\n",
" \"Instrument height incorporated.\"),) #added as 2D\n",
"\n",
"\n",
"\n",
"#2D: dimension time/range\n",
"\n",
"da_bsprof=xr.DataArray(data=np.array(DF_20min_range,dtype=float).astype('float32'),\n",
" name=\"backscatter_profile\",dims=[\"time\",\"range_levels\"],\n",
" coords=dict(time=Time_steps_dt64_org,range_levels=range_levs),\n",
" attrs=dict(type=\"float32\",dimension=\"time, range_levels\", units=\"1 km-1 steradians-1\",\n",
" long_name=\"Backscatter Profile\", description=\"backscatter coefficient profile\"),) #added as 2D\n"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"15"
]
},
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#merge all arrays into one\n",
"\n",
"ds_all=xr.merge([da_doy,da_year,da_month,da_day,da_hour,da_min,da_sec,\n",
" da_ceilrange,da_cloudcode,da_vertvis,da_high_sig,\n",
" da_baseht,da_scfrac,da_scht,da_bsprof])\n",
"\n",
"len(ds_all)\n"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Timestamp('2023-05-07 00:10:22')"
]
},
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#get time range - start\n",
"Times_nomicrosec[0]"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Timestamp('2023-06-13 16:10:07')"
]
},
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#get time range - end\n",
"Times_nomicrosec[-1]"
]
},
{
"cell_type": "code",
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"metadata": {},
"outputs": [],
"source": [
"# modify attributes (start/end time and text in comments); \n",
"# note again geospatial bounds from the weather station data\n",
"\n",
"ds_all.attrs = {\"Conventions\" :\"CF-1.8\",\n",
" \"source\" : \"Ceilometer\",\n",
" \"instrument_model\" : \"Vaisala Ceilometer CL31\",\n",
" \"creator_name\" : \"Sonja Murto\",\n",
" \"creator_email\" : \"sonja.murto@misu.su.se\",\n",
" \"creator_url\" : \"https://orcid.org/0000-0002-4966-9077\",\n",
" \"institution\" : \"Stockholm University\", \n",
" \"processing_software\" : \"Matlab (for creating the matlab file) and a jupyter notebook script (netCDF)\",\n",
" \"sampling_interval\": \"original 30s; here 20min averages\", \n",
" \"product_version\" : \"v01\",\n",
" \"last_revised_date\" : \"2024-05-31T15:00:00\", \n",
" \"project\" : \"ARTofMELT\",\n",
" \"project_principal_investigator\" : \"Michael Tjernström\",\n",
" \"project_principal_investigator_email\" : \"michaelt@misu.su.se\",\n",
" \"project_principal_investigator_url\" : \"https://orcid.org/0000-0002-6908-7410\", \n",
" \"acknowledgement\" : \" Knut och Alice Wallenbergs Stiftelse, Grant 2016-0024\",\n",
" \"platform\" : \"Swedish Icebreaker Oden\",\n",
" \"platform_type\" : \"On Oden's 7th deck above the bridge\",\n",
" \"deployment_mode\" : \"ship\",\n",
" \"title\" : \"Ceilometer cloud base height, vertical visibility and backscatter profiles\",\n",
" \"feature_type\" : \"time series\", \n",
" \"time_coverage_start\" : \"2023-05-07T00:10:22\",\n",
" \"time_coverage_end\" : \"2023-06-13T16:10:07\",\n",
" \"geospatial_bounds\" : \"80.52392166666667N, -3.8737749999999997E, 78.04355166666666N, 15.660881666666667E\",\n",
" \"platform_altitude\" : \"Located at approximately 25 m a.s.l\",\n",
" \"location_keywords\": \"Oden, Arctic Ocean, Fram Strait, atmosphere, on the ship\",\n",
" \"comments\" : \"This file consists of 20 min averages of ceilometer data \" +\\\n",
" \"measured with the Vaisala Ceilometer CL31 that was located on the 7th deck, \"+\\\n",
" \"above the bridge (at approximately 25m).\" + \\\n",
" \"The sky condition measurements are time averages to represent an area average. \" + \\\n",
" \"The vertical resolution is 10m * 770, but the measurement height (25m) is included in the backscatter profile ranges, \" + \\\n",
" \"as well as in the cloud base heights (cloud_base_altitude and sky_condition_cloud_altitude). \" + \\\n",
" \"Geospatial bounds are taken from the gps location of the weather station dataset located on Oden. \" +\\\n",
" \"Time variables month, day, hour, minute and second are approximated to the nearest second. \" +\\\n",
" \"Data produced by Sonja Murto. See the document - Readme_CL.txt - for more details.\"}\n",
" "
]
},
{
"cell_type": "code",
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"metadata": {},
"outputs": [
{
"data": {
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"</symbol>\n",
"<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
"<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
"<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
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"</symbol>\n",
"</defs>\n",
"</svg>\n",
"<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
" *\n",
" */\n",
"\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",