Working with input datasets

Working with input datasets#

This page documents the main input datasets used in Open Climate Risk and shows how to access them programmatically via the ocr dataset catalog. Treat this as a technical reference for dataset names, example usage, and ingestion notes. Information about data provenance is available on the Data sources page.

Accessing the catalog

from ocr import catalog

# List datasets
print(catalog)

# Load a dataset as an xarray / geopandas object
# rps_30 = catalog.get_dataset('riley-et-al-2025-2047-30m-4326').to_xarray()

Tensor data (raster / Zarr)#

These are n-dimensional raster datasets stored in Zarr/Icechunk stores.

USFS Wildfire Risk to Communities#

  • Source: Scott et al. 2024

  • Ingested to: ocr/input_datasets/tensor/usfs_scott_2024.py

  • Typical usage:

from ocr import catalog
crps = catalog.get_dataset('scott-et-al-2024-30m-4326').to_xarray()

USFS climate runs (2011 / 2047)#

  • Source: Riley et al. 2025

  • These are stored as zipped archives that the ingestion scripts expand into Icechunk stores.

climate_run_2011 = catalog.get_dataset('riley-et-al-2025-2011-30m-4326').to_xarray()
climate_run_2047 = catalog.get_dataset('riley-et-al-2025-2047-30m-4326').to_xarray()

Wind datasets#

  • Source: Rasmussen et al. 2023

  • Wind datasets and versions may change; if you add or switch wind sources, update the ingestion script under input-data/ and register the new dataset with the ocr catalog.

Vector data#

Vector data are building footprints, administrative boundaries, and other GIS vector layers used for exposure and aggregation.

Overture buildings#

conus_buildings = catalog.get_dataset('conus-overture-buildings')

Accessing private data

The catalog relies on privately hosted data. Operationalizing our codebase without access to these data will currently require an update to the catalog. See Issue #367 for more detail.