STAR Satellite Rainfall Estimates
Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR)
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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 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. SCaMPR 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 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. 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 SCaMPR 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 envioronments.
In its current form, SCaMPR uses GOES bands 3 (6.7 microns) and 4 (10.7 microns) brightness temperatures (and the difference between the two) as predictors, plus a pair of measures of local texture of the GOES band 4 field. These predictors are calibrated against the Climate Prediction Center (CPC) combined microwave (MWCOMB) data set. In the GOES-R era, this algorithm will use predictors from two water vapor bands (6.2 and 7.3 microns) and the IR window bands at 8.5, 10.8, and 12.0 microns, which will enhance its skill at detecting thin cirrus and rain from warmer clouds.
Additional details on SCaMPR can be found in the following reference:
Kuligowski, R. J., 2002: A self-calibrating real-time GOES rainfall algorithm for short-term rainfall estimates. J. Hydrometeor., 3, 112-130.
Kuligowski, R. J., 2010: Rainfall Rate (QPE) Algorithm Theoretical Basis Document, NOAA / NESDIS / STAR., 44 pp.
Kuligowski, R. J., Y. Li, and Y. Zhang, 2013: Impact of TRMM data on a low-latency, high-resolution precipitation algorithm for flash flood forecasting. J. Appl. Meteor. Cli., 52, 1379-1393.
Please use the links at left to view current and archived SCaMPR imagery. For archived digital files, please check the validation pages.