nexradFind NEXRAD radar stations, discover and download bounded recent or historical Archive II observations, decode measured radar fields and coordinates, and build radar analyses or applications from NOAA data.
Independent GitHub snapshot · Automated classification · Original documentation governs
Use this skill for measured weather radar data. Establish location/station, UTC interval, requested fields and output before choosing observations. Find nearby stations with scripts/stations.py --lat LAT --lon LON; distance is geographic proximity, not a guarantee of coverage or operational status.
Use Python 3.11 and network access for station lookup and archive discovery/download. Resolve all helper paths relative to this installed skill directory, regardless of the working directory. Discovery uses only Python's standard library. For decoding create a project virtual environment with python3.11 -m venv .venv (Windows: py -3.11 -m venv .venv), then run that environment's Python with -m pip install -r /absolute/path/to/nexrad/scripts/requirements.txt. See references/example.md for executable setup and usage.
Read references/data-access.md for source contracts and historical/recent selection. Run scripts/archive.py --station KTLX --start 2024-05-06T00:00:00Z --end 2024-05-06T00:10:00Z to return JSON. Add --download OUTPUT --max-files 1 --max-bytes 50000000 to download the earliest selected observation. Adjust caps deliberately for the request. The helper supports up to seven days per discovery; split larger intervals. It lists complete archive objects, not live chunks. Empty results are not clear weather.
Run scripts/decode.py FILE --npz OUTPUT.npz with the decoding environment's Python. JSON reports available fields, units, dimensions, masks and time origin. NPZ preserves reader arrays, masks, ray times, sweep boundaries and coordinates. Read references/decoded-data.md before using the output in analyses or applications. Enumerate actual fields; never promise moments absent from a particular volume. This helper delegates parsing to Py-ART, not a homegrown full-format decoder.
Use references/example.md for a tiny real-data workflow. Build the requested analysis or app around these outputs: retain provenance, units, timestamp meaning and missing masks; make any interpolation, gridding, filtering or visual thresholds explicit. Slant range and beam height do not equal surface distance or terrain clearance. Reflectivity is not visible cloud density; velocity is radial, not a complete wind vector. Consult official warnings for hazardous-weather decisions.