Three algorithm intercomparison experiments have recently been conducted as part of the Global Precipitation Climatology Project with the goal of (a) assessing the skill of current satellite rainfall algorithms, (b) understanding the differences between them, and (c) moving toward improved algorithms. The results of these experiments are summarized and intercompared in this paper.

It was found that the skill of satellite rainfall algorithms depends on the regime being analyzed, with algorithms producing very good results in the tropical western Pacific and over Japan and its surrounding waters during summer, but relatively poor rainfall estimates over western Europe during late winter. Monthly rainfall was estimated most accurately by algorithms using geostationary infrared data, but algorithms using polar data [Advanced Very High Resolution Radiometer and Special Sensor Microwave/Imager (SSM/I)] were also able to produce good monthly rainfall estimates when data from two satellites were available. In most cases, SSM/I algorithms showed significantly greater skill than IR-based algorithms in estimating instantaneous rain rates.

This content is only available as a PDF.

Footnotes

*Bureau of Meteorology Research Centre, Melbourne, Australia.

+National Centers for Environmental Prediction, Washington, D.C.

#U.K. Meteorological Office, Bracknell, Berkshire, United Kingdom.

@NOAA/NESDIS, Washington, D.C.