Spatial debiasing is a post-processing step applied to 0.25deg native forecasts and ERA5. It provides a better estimate for the weather for a specific ****lat/lon or a list of grid points in a location_file . Debiasing helps correct inherent biases present in spatial data like ERA5 Reanalysis, a source of  training data for Salient’s models. Salient uses two methodologies depending on the timescale via an API debiasing option(debias=True).

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Note: Currently, debiased forecasts are not available in Cloudflare.

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Debiasing Model - Daily Resolution

The debiasing model applies to:

• Salient GEM
• Salient Blend Daily Downscale
• ERA 5 Daily Downscale

The debiasing solution uses a generalized model that uses training data to learn the complex relationship between ERA5 and local environment to calculate a bias correction factor.

• This is a post-processing model that downscales 0.25deg ERA5-style data to a specific location within a grid cell at a granularity of 100m
• Uses properties of the local environment like the land surface attributes and proximity to water, plus other features of the current weather, to debias the forecast
• This has the biggest impact in coastal regions, mountains, and urban areas, and has a bigger gain for daily high temperatures than for low temperatures
• Should only be used when validating against station data rather than gridded reanalysis data, since the outputs are specific to a precise coordinate

Supported Variables

• Temperature
• Miniumum Temperature
• Maximum Temperature
• HDD
• CDD
• Wind Speed 10m (coming in a future release)
• Precipitation (coming to a future release)