A Cloud-Patch Technique for Identification and Removal of No-Rain Clouds from Satellite Infrared Imagery

Abstract

A new cloud-patch method for the identification and removal of no-rain cold clouds from infrared (IR) imagery is presented. A cloud patch is defined as a cluster of connected IR imagery pixels that are colder than a given IR brightness temperature threshold. The threshold is derived through a combination of the rainfall field estimated from microwave observations and the IR data closely coincident with microwave sensor satellite overpasses. Seven cloud-patch features are used to describe cloud-top properties, including six IR based and one VIS based. The ID3 algorithm is used to extract structural knowledge from a training dataset and to produce classification rules expressed explicitly on the values of various patch features; these rules can be used to explain the physical principles underlying the cloud classification. The method was evaluated for the Japanese islands and surrounding oceans using AIP/1 data for June (training period) and July?August (evaluation period) 1989. The results of identifying no-rain cloud patches are very good for both periods in spite of the change in rainfall regime from frontal to subtropical convective. Nearly 20% of the total pixels and 60% of the no-rain cloud pixels were removed with negligible rain losses due to misclassification. Moreover, visible data were found to be useful for enhancing the no-rain cold patch identification and thereby reducing the rain loss.