The Passive microwave Empirical cold Surface Classification Algorithm (PESCA): application to GMI and ATMS

Abstract This paper describes a new Passive microwave Empirical cold Surface Classification Algorithm (PESCA) developed for snow cover detection and characterization by using passive satellite measurements. The main goal of PESCA is to support the retrieval falling snow, as several studies have highlighted influence radiative properties on signature. methodology based exploitation lower frequency channels (< 90 GHz), common most radiometers. applied conically scanning GMI cross-track ATMS described in this paper. decision tree an empirical method verified AutoSnow product built from algorithm performance appears be robust both sensors dry conditions (TPW < 10 mm), mean surface elevation 2500 m, independently cloud cover. shows very good temperatures (2 m temperature below 270 K) with rapid decrease capabilities between K 280 K, where assumed maximum limit [overall statistics: POD=0.98(0.92), FAR=0.01(0.08), HSS=0.72(0.69) ATMS(GMI)]. Some inconsistencies found categories identified two radiometers are related their different viewing geometry, spatial resolution, temporal sampling. spectral signatures classes appear also at high (>90GHz), indicating potential impact snowfall retrieval. can other cross track including future operational EPS-SG mission