Identification of ice-over-water multilayer clouds using multispectral satellite data in an artificial neural network

Abstract

Abstract. An artificial neural network (ANN) algorithm, employing several Aqua MODerate-resolution Imaging Spectroradiometer (MODIS) channels, the retrieved cloud phase and total cloud visible optical depth, and temperature and humidity vertical profiles is trained to detect multilayer (ML) ice-over-water cloud systems identified by matched 2008 CloudSat and CALIPSO (CC) data. The trained multilayer cloud-detection ANN (MCANN) was applied to 2009 MODIS data resulting in combined ML and single layer detection accuracies of 87 % (89 %) and 86 % (89 %) for snow-free (snow-covered) regions during the day and night, respectively. Overall, it detects 55 % and ∼ 30 % of the CC ML clouds over snow-free and snow-covered surfaces, respectively, and has a relatively low false alarm rate. The net gain in accuracy, which is the difference between the true and false ML fractions, is 7.5 % and ∼ 2.0 % over snow-free and snow/ice-covered surfaces. Overall, the MCANN is more accurate than most currently available methods. When corrected for the viewing-zenith-angle dependence of each parameter, the ML fraction detected is relatively invariant across the swath. Compared to the CC ML variability, the MCANN is robust seasonally and interannually and produces similar distribution patterns over the globe, except in the polar regions. Additional research is needed to conclusively evaluate the viewing zenith angle (VZA) dependence and further improve the MCANN accuracy. This approach should greatly improve the monitoring of cloud vertical structure using operational passive sensors.