An objective regional cloud mask algorithm for GOES infrared imager radiance assimilation

Abstract

A local, regime-dependent cloud mask (CM) algorithm is developed for isolating cloud-free pixels from cloudy pixels for Geostationary Operational Environmental Satellite (GOES) imager radiance assimilation using mesoscale forecast models. In this CM algorithm, thresholds for six different CM tests are determined by a one-dimensional optimization approach based on probability distribution functions of the nearby cloudy and clear-sky pixels within a 10° × 10° box centered at a target pixel. It is shown that the optimizal thresholds over land are, in general, larger and display more spatial variations than over ocean. The performance of the proposed CM algorithm is compared with Moderate Resolution Imaging Spectroradiometer (MODIS) CM for a 1 week period from 19 to 23 May 2008. Based on MODIS CM results, the average Probability of Correct Typing reaches 92.94% and 91.50% over land and ocean, respectively.