{
"cells": [
{
"cell_type": "markdown",
"id": "0",
"metadata": {},
"source": [
"# Getting Started\n",
"The Chalmers Hydrometor Inversion Product from the Arctic Weather Satellite (CHIP-AWS), provides global atmospheric ice mass estimates derived from passive microwave measurements.\n",
"The data are available as NetCDF files, the following examples will show how to get started using them with python and `xarray`."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "1",
"metadata": {
"execution": {
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}
},
"outputs": [],
"source": [
"from datetime import datetime\n",
"import requests\n",
"import xarray as xr\n",
"\n",
"def file_urls_for_timeranges(timeranges: list[slice]) -> list[str]:\n",
" \"\"\"\n",
" Helper function to get a list of level2 urls for the requested timeranges\n",
" \"\"\"\n",
" base_url = \"http://data.clouds-and-precip.group/chip-aws/chip-aws-v0.3/level2\"\n",
" index = requests.get(f\"{base_url}/index.json\").json()\n",
" urls = [\n",
" f\"{base_url}/{item['filepath']}#mode=bytes\"\n",
" for item in index['items']\n",
" for timerange in timeranges\n",
" if datetime.fromisoformat(item['datetime_end']) >= datetime.fromisoformat(timerange.start.replace(\"Z\", \"+00:00\"))\n",
" and datetime.fromisoformat(item['datetime_start']) <= datetime.fromisoformat(timerange.stop.replace(\"Z\", \"+00:00\"))\n",
" ]\n",
" return sorted(set(urls))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "2",
"metadata": {
"execution": {
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"source": [
"# List some interesting scenes\n",
"timeranges = [\n",
" slice('2025-05-01T07:18:00', '2025-05-01T07:28:00'),\n",
" slice('2025-05-01T11:19:00', '2025-05-01T11:28:00'),\n",
" slice('2025-05-01T11:46:00', '2025-05-01T11:55:00'),\n",
"]\n",
"\n",
"# Open the files as a single xarray dataset\n",
"ds = xr.open_mfdataset(\n",
" file_urls_for_timeranges(timeranges),\n",
" combine=\"nested\",\n",
" concat_dim=\"scan\",\n",
" data_vars=\"minimal\",\n",
")\n",
"\n",
"# Each file is generated from a single source level 1 file for self-contained processing.\n",
"# Consecutive source files have short overlaps. To avoid duplicate data, \n",
"# we remove overlapping scans by keeping only the last occurrence of each duplicate.\n",
"# Sorting ensures scans are in chronological order from these overlaps,\n",
"# allowing selection by time slices.\n",
"ds = ds.sortby('scan').sel(scan=~ds.get_index('scan').duplicated(keep='last'))"
]
},
{
"cell_type": "markdown",
"id": "6a9f6679",
"metadata": {},
"source": [
"# Dataset structure\n",
"The main variables are given in the along-track `scan` dimension and the across-track `fov` (field-of-view) dimension.\n",
"The structure of the data is shown below:"
]
},
{
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"id": "ed68302d",
"metadata": {
"execution": {
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"outputs": [
{
"data": {
"text/html": [
"
Quality flag for observation (scan and fov). 0 if value passes basic QA checks. Other if any checks fail. Bit position indicates which check failed.\n",
"\n",
"Bit meanings:\n",
"bad_l1b_flag: Set if any of the following flags from the source L1B are non-nominal: degraded_channels, aws_calibration_flag, aws_nedt_flag, aws_scanline_quality, aws_position_flag_earthview, aws_channel_quality_flag, counts_warmview_processing_flag, counts_coldview_processing_flag, aws_instrument_temperature_quality_flag, aws_calibration_weights_quality_flag_coldview, aws_calibration_weights_quality_flag_warmview, aws_instrument_weights_quality_flag, aws_l1b_processing_info_chan, scan_time_anomaly, aws_cal_mirror_temperature_flag, aws_navigation_status\n",
"bad_ta_value: Set if any channel has unreasonable brightness temperature value\n",
"bad_satellite_altitude: Set if satellite altitude is out of range\n",
"bad_latlon: Set if latitude or longitude is missing\n",
"bad_l1b_file: Set if L1B file is known to have issues\n",
"bad_aa_za: Set if either azimuth or zenith angle is out of range
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Bytes
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577.59 kiB
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288.84 kiB
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Shape
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(6721, 88)
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(3361, 88)
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Dask graph
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2 chunks in 7 graph layers
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Data type
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uint8 numpy.ndarray
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fwp_ccic
(scan, fov)
float32
dask.array<chunksize=(3360, 88), meta=np.ndarray>
units :
kg/m2
long_name :
Co-located FWP from the Chalmers Cloud Ice Climatology (ccic.readthedocs.io) for comparison.