Programmatic access to ERA5 and the Copernicus Climate Data Store — the world's reanalysis workhorse.
ERA5 / ERA5-Land
ActiveECMWF's hourly global reanalysis — the default gap-free climate forcing record for ungauged catchments worldwide.
by ECMWF / Copernicus
- Pricing
- Free
- License
- Copernicus data licence (free registration required)
- Commercial use
- Permitted
- Ecosystem
- cdsapi, Python, xarray
- Platform
- Windows, macOS, Linux
About ERA5 / ERA5-Land
ERA5 is ECMWF's fifth-generation atmospheric reanalysis: hourly, global, 0.25° (ERA5-Land refines the land surface to 0.1°), with a consistent physically-based record from 1950 to near-present across dozens of variables — precipitation, temperature, wind, radiation, soil moisture and more. It is served through the Copernicus Climate Data Store, with the cdsapi Python client as the standard access route.
For the large majority of the world's catchments that lack dense gauge networks, ERA5 is frequently the difference between "no forcing data" and a runnable model, and it has become the default reanalysis product cited in modern hydrological literature.
Where it fits in the workflow
TerrainCatchmentRainfallRunoffRoutingHydraulicsMappingReporting
Appears in collections
- Satellite Data for Hydrology & Water Engineering
The satellite and reanalysis datasets H&H engineers actually reach for — DEM to GRACE.
Alternatives to ERA5 / ERA5-Land
Ranked by the topics, categories and workflow stages they share with ERA5 / ERA5-Land.
MERRA-2
Gov-Public
NASA's satellite-era atmospheric reanalysis — hourly global fields from 1980, the default alternative to ERA5.
NOAA CFSR / CFSv2
Gov-Public
NCEP's coupled atmosphere-ocean-land reanalysis, sub-daily, 1979–present — strong where ocean coupling matters.
Caravan
Open Source
The open global large-sample hydrology dataset — thousands of catchments with forcing, attributes and streamflow.
NEOPRENE
Open Source
Neyman-Scott stochastic rainfall generation in Python — synthetic series and disaggregation, single or multi-site.
RainyDay
Open Source
Stochastic storm transposition in Python — probabilistic extreme rainfall scenarios from remote-sensing data.