Correction of sub-cloud effects on rainfall rate estimation from the Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) algorithm

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. The current real-time version of SCaMPR does not account for sub-cloud effects on precipitation and thus does not contain all of the information relevant to rainfall retrieval from satellite data. In order to reduce the error in rainfall rate estimation from the SCaMPR algorithm, a correction is developed that accounts for moisture availability and/or sub-cloud evaporation of hydrometeors from the North American Mesoscale (NAM) model by using three moisture variables: total column Precipitable Water (PW), Mean Relative Humidity (RH) over the bottom third of the model domain, and the product of the two quantities.

The development data in this study are for GOES and NAM data over the United States. Then the correction will be applied to the GOES-R version of SCaMPR – currently run over Europe and Africa using SEVIRI data as a proxy and the Global Forecast System (GFS) in place of the NAM – to evaluate the impact on skill.