We are proposing a statistical technique to analyze the best fit of the histogram of infrared brightness temperature of convective cloud pixels. For this we have utilized the infrared brightness temperatures (IRTB) of Kalpana-1 (8 km resolution) and globally merged infrared brightness temperatures of Climate Prediction Centre NCEP/NWS (4 km resolution, merged from all the available geostationary satellites GOES-8/10, METEOSAT-7/5 and GMS), for both deep convective and non-deep convective (shallow cloud) cases. It is observed that Johnson SB function is the best continuous distribution function in explaining the histogram of infrared brightness temperatures of the convective clouds. The best fit is confirmed by Kolmogorov–Smirnov statistic. Johnson SB’s distribution of histogram of infrared brightness temperatures clearly discriminates the cloud pixels of deep convective and non-deep convective cases. It also captures the asymmetric nature in histogram of infrared brightness temperatures. We also observed that Johnson SB distribution of infrared brightness temperatures for deep convective systems is different in each of the pre-monsoon, monsoon and post-monsoon seasons. And Johnson SB parameters are observed to be best in discriminating the Johnson SB distribution of infrared brightness temperatures of deep convective systems for each season. Due to these properties of Johnson SB function, it can be utilized in the modelling of the histogram of infrared brightness temperature of deep convective and non-deep convective systems. It focuses a new perspective on the infrared brightness temperature that will be helpful in cloud detection, classification and modelling.
Volume 130, 2021
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