Classification-based rainfall estimation using satellite data and numerical forecast model fields

Abstract

Using Global Precipitation Climatology Project data gathered during June, July, and August 1989 over Japan, rainfall estimates are examined from both geostationary satellite imagery using a multifeature classification approach, and from short-term weather prediction model fields. Additionally, the utility of combining model forecast information within such a classifier to improve the final estimate is investigated. During both months satellite estimates are superior to model forecasts in detecting heavy rain events associated with extremely cold cloud tops, and in identifying cloud-free regions. Model estimates are superior to satellite retrievals in terms of dynamic range and regional bias. Addition of visible data to an infrared-only scheme improved monthly rainfall estimates during June, and hourly estimates during both months. -from Authors