The Hydrology Algorithm Team of the GOES-R Algorithm Working Group (AWG) has developed the GOES-R version of the Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) algorithm as the operational rainfall rate algorithm for GOES-R. SCaMPR is an effort to combine the relative strengths of infrared (IR)-based and microwave (MW)-based estimates of precipitation. The GOES-R version of SCaMPR was developed over Europe and Africa and surrounding oceans using the METEOSAT Spinning Enhanced Visible Infra-Red Imager (SEVIRI) data as a proxy for the GOES-R Advanced Baseline Imager (ABI).

The algorithm produces a field of instantaneous rainfall rate; therefore, radar (both space-based and ground-based) is the only available source of data for validation. The GOES-R Rainfall Rate algorithm is being validated prior to launch against Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) data and Nimrod radar data over Western Europe obtained from the British Atmospheric Data Centre (BADC). Validation is focused on evaluating performance and identifying systematic biases / weaknesses in the algorithm for potential improvement. Routine validation will focus on the former and deep-dive validation will focus on the latter by examining the predictor and target data along with the calibration to determine the reasons for any anomalies in performance.

This presentation will introduce the deep-dive validation methodology for estimating systematic biases / weaknesses in the GOES-R Rainfall rate algorithm and discuss quantitative results from the analysis.