Detection of Deep Convective Clouds Using AMSU-B and GOES-9 Data

Methods to detect the deep convective clouds using NOAA-16/AMSU-B and GOES-9 data are provided, and a serial of algorithms of detection and discrimination are presented and tested, which include the microwave brightness temperatures detection from the two window channels, water vapor channel microwave brightness differences identification based on the AMSU-B data, infrared brightness thresholds detection of cloud top temperatures, the water vapor and infrared window temperature differences determination, and the classification of cumulonimbus clouds correlating with deep convective clouds with infrared/water vapor spectral features. By detecting and analyzing deep convective clouds in the images on 16 June in 2004, the techniques are investigated, and by matching surface conventional data the results of various methods are validated. The results show that microwave brightness temperatures from window channels can discriminate deep convective clouds efficiently, differences between three water vapor channels can identify the deep convective clouds well and depend on the thresholds less. The GOES-9 different infrared brightness thresholds are given the detection regions are more or less. The water vapor and infrared window temperature differences determination areas are smaller. The stepwise cluster can identify cumulonimbus clouds correlating with deep convective clouds applying with infrared/water vapor spectral features, the detection areas are coincident with AMSU-B detection areas, and the surface conventional data can validate the results, which include hazards weather and cumulonimbus clouds.