Enterprise Rainfall Rate (ERR) Resources
(previously Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR))

ERR data and visualizations can be downloaded from the following locations:

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NOAA Office of Satellite Products and Operations

The Enterprise Rain Rate / SCaMPR visualizations served from OSPO can be found:
https://www.ospo.noaa.gov/products/atmosphere/err/

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Amazon Web Services (AWS)

Amazon Web Services (AWS) hosts a repository for NetCDF product data files for the Enterprise Rain Rate.

Details

• Location:
  https://noaa-enterprise-rainrate-pds.s3.amazonaws.com/index.html
• Data flow started in 2025;
• Extent: Longitude: global; Latitude: from 70°N to 60°S;
• Cadence: updated every 10 minutes;
• Product type: stores only the instantaneous rain rate;
• Satellite data sources: GOES, Meteosat (Europe), and Himawari (Japan);

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STAR Resources

STAR produces & shares a small repository of recent ERR images for reference.

Details:

• Location:
  https://www.star.nesdis.noaa.gov/data/smcd1/ff/scampr/
• Retention:
  7 day rolling window of images;
• Views:
  Global (70°N to 60°S), CONUS, and the DC area
• Product type: contains both the instantaneous and 1 hour rain rate images;
• Cadence: updated every 10 minutes;
• Satellite data sources: GOES, Meteosat (Europe), and Himawari (Japan);

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SCaMPR Precipitation Estimates

The Enterprise Rain Rate (ERR) / Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) algorithm is an effort to combine the relative strengths of infrared (IR)- based and microwave (MW)-based estimates of precipitation. In particular, IR data are available at high spatial (2 km) and temporal (10 min) resolution with very low latency (minutes), but raining clouds are opaque in the IR and thus precipitation information must be inferred from cloud-top properties such as temperature and texture. In contrast, raining clouds are semitransparent at MW frequencies, and thus MW radiances are sensitive to the amount of water and ice in a cloud, resulting in a more robust relationship with precipitation rates. However, MW data are available only from low-earth-orbit platforms, and thus are available infrequently (e.g., approximately twice per day for a polar orbit).

Numerous approaches have been taken to combine IR and MW data for rain rate estimation. The ERR uses GOES IR data as a source of predictor information (thus optimizing the temporal resolution, refresh rate, and latency of the estimates), and calibrates them against MW-based rain rates (thus optimizing the accuracy). The selection of predictors and calibration are performed in two steps by the ERR: rain/no rain discrimination using discriminant analysis, and precipitation rate calibration using regression. Nonlinear transformations of the predictors are also performed to optimize the regression fits. Since regression assumes a normal distribution and tends to distort non-normally distributed data, a final processing step involves computing the rain rates on the calibration data and matching the Cumulative Distribution Function (CDF) with that of the microwave rain rates to create a lookup table (LUT) that adjustes the distribution of the ERR rain rates to match that of the calibration microwave rain rates. Finally, to account for the effects of evaporation of hydrometeors below the cloud base, the average relative humidity (RH) over the lower third of the atmosphere from the Global Forecast System (GFS) model is used to adjust the rainfall rates downward in dry environments.

In its current form, the ERR uses predictors from two water vapor bands (6.2 and 7.3 microns) and three IR window bands at 8.5, 10.8, and 12.0 microns, which enhance its skill at detecting thin cirrus and rain from warmer clouds. It also uses the the difference between selected pairs of bands plus a pair of measures of local texture of the 10.8-micron. These predictors are calibrated against the Climate Prediction Center (CPC) combined microwave (MWCOMB) data set

Additional details on SCaMPR can be found in the links below: