Detection of Single and Multilayer Clouds in an Artificial Neural Network...

Sun-Mack, S., P. Minnis, W. Smith, G. Hong, and Y. Chen (2017), Detection of Single and Multilayer Clouds in an Artificial Neural Network Approach. Proc. SPIE Conf. Remote Sens. Clouds and the Atmos. XXII, Warsaw, Poland, 10424-7, 11-14, doi:10.1117/12.2277397.

Determining whether a scene observed with a satellite imager is composed of a thin cirrus over a water cloud or thick cirrus contiguous with underlying layers of ice and water clouds is often difficult because of similarities in the observed radiance values. In this paper an artificial neural network (ANN) algorithm, employing several Aqua MODIS infrared channels and the retrieved total cloud visible optical depth, is trained to detect multilayer ice-over-water cloud systems as identified by matched April 2009 CloudSat and CALIPSO (CC) data. The CC lidar and radar profiles provide the vertical structure that serves as output for a multilayer ANN, or MLANN, algorithm. Applying the trained MLANN to independent July 2008 MODIS data resulted in a combined ML and single layer hit rate of 75% (72%) for nonpolar regions during the day (night). The results are comparable to or more accurate than currently available methods. Areas of improvement are identified and will be addressed in future versions of the MLANN.

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Radiation Science Program (RSP)