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"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. Data are available as NetCDF files over http. These can be downloaded or acessed directly.\n",
"The following examples show how to access started using them with `python` and `xarray`."
]
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"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-v0.6/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))"
]
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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",
" coords=\"minimal\",\n",
" compat=\"override\",\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 remove duplicate data, \n",
"# we remove overlapping scans by keeping only the last occurrence of each duplicate.\n",
"# Sorting ensures scans are in chronological order, allowing selection by time slices.\n",
"ds = ds.sel(scan=~ds.get_index('scan').duplicated(keep='last'))\n",
"ds = ds.sortby('scan')"
]
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"cell_type": "markdown",
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"When working with a large number of files, the overhead from parsing file metadata over HTTP using `open_mfdataset` can become significant.\n",
"For such uses, consider downloading locally first:\n",
"```bash\n",
"wget --recursive --no-parent --no-host-directories http://data.clouds-and-precip.group/chip-aws/chip-aws-v0.6/level2/2025/05/\n",
"```"
]
},
{
"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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{
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"
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 brightness temperature value out of range [80, 330] (K).\n",
"bad_satellite_altitude: Set if satellite altitude is out of range [400, 800] (km).\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 [0, 360] (deg) or zenith angle [0, 180] (deg) is out of range.\n",
"padding: Indicates repeated scans to ensure consistent number of scans between level 2 files.
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fwp_ccic
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float32
dask.array<chunksize=(3450, 88), meta=np.ndarray>
units :
kg/m2
long_name :
Frozen water path, co-located values from CCIC
description :
Co-located Frozen water path values from the Chalmers Cloud Ice Climatology (ccic.readthedocs.io), included for comparison.
ccic_files :
ccic_cpcir_202505010600.zarr
ccic_file_versions :
ccic-0.2.0
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title :
CHIP-AWS: Chalmers Hydrometeor Inversion Product from the Arctic Weather Satellite