Evaluation of a Satellite Multispectral VIS–IR Daytime Statistical Rain-Rate Classifier and Comparison with Passive Microwave Rainfall Estimates

Abstract

A daytime surface rain-rate classifier, based on artificial neural networks (ANNs), is proposed for the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the Meteosat-8 geostationary satellite. It is developed over the British Isles and surrounding waters, where the Met Office radar network provided the ?ground precipitation truth? for training and validation. The algorithm classifies rain rate in five classes at 15 min and 5 km of time and spatial resolution, and is applied on daytime hours in a summer and winter database. A further ANN application is restricted to hours between 1200 and 1400 UTC for which the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) on board the Aqua polar-orbiting satellite scans the U.K. area: ANN-classifier algorithms for the SEVIRI and AMSR-E data have been developed and the results have been compared. A reliable validation procedure is adopted to quantify the performance in view of the operational application of the daytime classifier and to investigate the relative skills of passive microwave and visible?infrared radiances in sensing precipitation if processed with equivalent algorithms. The key statistical parameters used are the equitable threat score (ETS) and the bias for rain?no rain classes and the Heidke skill score (HSS) for rain-rate classes. The SEVIRI daytime classifier shows, for mean seasonal conditions, the best performance in summer, with ETS = 47% and HSS = 22%, and in winter ETS = 36% and HSS = 17% were found. The comparison between AMSR-E and SEVIRI noon classifiers reveals a similar overall skill: in detecting rain areas, SEVIRI is slightly better than AMSR-E, while the opposite is true for rain-rate classification.