On the Use of Split Window Data in Deriving the Cloud Motion Vector for Filling the Gap of Passive Microwave Rainfall Estimation

Abstract

High temporal resolution rainfall estimation from satellite is required for hydrology, especially for flood warning. Recently, rainfall estimation from passive microwave has been widely used. However, the sampling time is limited because the sensor is onboard low earth orbit satellite (LEO). Currently, the cloud motion vector derived from the infrared data of geostationary satellites is used to fill the observation gap by LEO, and a better cloud motion vector is essential for better rainfall estimation.Optically thick cloud area defined by the split window (11 µm and 12 µm) generally corresponds well to the rainfall area compared to the cloud area defined by single infrared data. We surveyed the effectiveness of useing split window data for deriving the cloud motion vector. We studied 30 rainfall cases during September 2003, and split window data indicated better score in 11 cases out of 30. The correlation values are improved by 45% at most for the 11 cases.