STAR Satellite Rainfall Estimates
Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR)

SCaMPR Precipitation Estimates

The 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 (4 km) and temporal (15 min over the CONUS) resolution, 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. SCaMPR uses GOES IR data as a source of predictor information (thus optimizing the temporal and spatial resolution 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 SCaMPR: 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.

The SCaMPR framework is quite flexible: although GOES IR data are the only predictors in the real-time version, experiments have shown positive impact for moisture information from numerical weather models and for lightning data, and these data will be incorporated into the real-time version of SCaMPR in the near future. In addition, although SCaMPR is currently applied only over the CONUS using GOES-11 and -12 data, an expansion to global coverage is under development using data from METEOSAT, MSG, and MTSAT.

Additional details on SCaMPR can be found in the following reference:

Kuligowski, R. J., 2002: (presentation link needs to be replaced) A self-calibrating real-time GOES rainfall algorithm for short-term rainfall estimates. J. Hydrometeor., 3, 112-130.

Please use the links at left to view current and archived SCaMPR imagery. For archived digital files, please check the validation pages.