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. In June it is shown that a combined satellite-model method clearly yields improved retrievals of rainfall relative to those obtained by using either satellite data or model forecasts alone at both monthly and hourly time scales. However, in July and August, satellite retrievals largely underestimated monthly rainfall, and the model produced poor hourly forecasts. Generally, a good model forecast of rainfall can enhance the satellite estimate, while a poor forecast will degrade it. If results obtained during June are found to be valid for other regions of the globe, such a method could be used to develop rainfall climatologies. It could also be used in a real-time operational numerical weather prediction environment since it is computationally rapid, with only geostationary satellite observations and model-predicted fields needed to derive the estimates.