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geoscience-data

Reads, transforms and summarizes gridded and geospatial data without losing the metadata that gives it meaning, covering NetCDF and HDF5 through xarray and netCDF4 with CF conventions, dimensions versus coordinates, time encodings and calendars and fill values, GeoTIFF and other rasters through rasterio and GDAL with nodata and resampling choices, coordinate reference systems and reprojection with pyproj and EPSG codes, geodesic versus planar distances and areas, vector data through geopandas, and unit handling with area weighting on latitude-longitude grids. Use for climate, weather, hydrology, remote sensing, land use and any map-based analysis; use the physics-fitting or spectral-analysis skills once the data is a clean array.

backend/cli/skills/physics/geoscience-data/SKILL.md

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# Geoscience data

Gridded and geospatial data carry their meaning in metadata: units, fill values,
calendars, the coordinate reference system and the cell geometry. Most wrong answers come
from ignoring one of them, such as averaging over a latitude-longitude grid without
weights, treating `_FillValue` as zero, or mixing degrees with metres.

## NetCDF and HDF5

1. `xr.open_dataset(path)` decodes CF metadata: `scale_factor` and `add_offset` unpack
   integers, `_FillValue` and `missing_value` become NaN, and time is decoded from
   `units` ("days since 1850-01-01") with the `calendar` attribute. Use
   `decode_times=False` to see raw numbers, `use_cftime=True` for `noleap` or `360_day`
   calendars and for dates outside the range of nanosecond `datetime64`. Multi-file: