nv-dfm-lib-weather#

Pre-built adapters for weather and climate data workflows.

Installation#

pip install nv-dfm-lib-weather

AI model adapters require GPU hardware and are installed via extras:

pip install nv-dfm-lib-weather[sfno]     # SFNO model
pip install nv-dfm-lib-weather[cbottle]  # cBottle (Climate in a Bottle) models
pip install nv-dfm-lib-weather[all]      # all optional dependencies

Data Loaders#

Adapter	Description
`LoadGfsEra5Data`	GFS data via Earth2Studio (AWS backend)
`LoadEcmwfEra5Data`	ECMWF ERA5 reanalysis data
`LoadHrrrEra5Data`	HRRR high-resolution data
`LoadCmip6Data`	CMIP6 climate model data

XArray Processing#

Adapter	Description
`VariableNorm`	Normalize xarray variables
`ConvertToUint8`	Convert to uint8 for visualization
`RenderUint8ToImages`	Render datasets to PNG textures

AI Models (optional extras)#

Adapter	Extra	Description
`SfnoPrognostic`	`sfno`	Spherical Fourier Neural Operator for weather prediction
`CbottleVideo`	`cbottle`	video prognostic Climate in a Bottle
`CbottleInfilling`	`cbottle`	Climate in a bottle infill diagnostic
`CbottleSuperResolution`	`cbottle`	cBottle super-resolution
`CBottleTropicalCycloneGuidance`	`cbottle`	cBottle tropical cyclone guidance diagnostic
`CbottleDataGen`	`cbottle`	CBottle3D synthetic climate data generator

Adapter Configurations#

Each adapter includes a YAML configuration file (under nv_dfm_lib_weather/configs/) that declares its operation signature — parameters, types, and return values. These configs can be referenced directly from your federation’s .dfm.yaml to expose adapters as operations.