Evaluating Light Rain from Satellite- and Ground-Based Remote Sensing Data over the Subtropical North Atlantic

Abstract

hree state-of-the-art satellite climatologies are analyzed for their ability to observe light rain from predominantly shallow, warm clouds over the subtropical North Atlantic Ocean trade winds (1998?2005). HOAPS composite (HOAPS-C), version 3.2; TMPA, version 7; and GPCP 1 Degree Daily (1DD), version 1.2, are compared with ground-based S-Pol radar data from the Rain in Cumulus over the Ocean (RICO; winter 2004/05) campaign and Micro Rain Radar data from the Barbados Cloud Observatory (2010?12). Winter rainfall amounts to one-third of annual rainfall, whereby light rain from warm clouds dominates. Daily rain occurrence and rain intensity during RICO largely differ among the satellite climatologies. TMPA best captures the frequent light rain events, only missing 7% of days on which the S-Pol radar detects rain, whereas HOAPS-C misses 33% and GPCP 1DD misses 56%. Algorithm constraints mainly cause these differences. In HOAPS-C also few available passive microwave (PMW) sensor overpasses limit its performance. TMPA outperforms HOAPS-C when only comparing nonmissing time steps, yet HOAPS-C can detect rain for S-Pol rain-covered areas down to 2%. In GPCP 1DD?s algorithm, the underestimated rain occurrence derived from PMW scanners is linked to the overestimated rain intensity, being constrained by the GPCP monthly satellite?gauge combination, whereby IR sensors determine the timing. Algorithm improvements in version 1.2 increased the rain occurrence by 50% relative to version 1.1. In version 7 of TMPA, algorithm corrections in PMW sounder data largely improved the rain detection relative to version 6. TMPA best represents light rain in the North Atlantic trades, followed by HOAPS-C and GPCP 1DD.