Detection of single and multilayer clouds in an artificial neural network approach

Sunny Sun-Mack; Patrick Minnis; William L. Smith Jr.; Gang Hong; Yan Chen

doi:10.1117/12.2277397

13 October 2017 Detection of single and multilayer clouds in an artificial neural network approach

Sunny Sun-Mack, Patrick Minnis, William L. Smith Jr., Gang Hong, Yan Chen

Proceedings Volume 10424, Remote Sensing of Clouds and the Atmosphere XXII; 1042408 (2017) https://doi.org/10.1117/12.2277397
Event: SPIE Remote Sensing, 2017, Warsaw, Poland

Abstract

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 truth 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.

Citation Download Citation

Sunny Sun-Mack, Patrick Minnis, William L. Smith Jr., Gang Hong, and Yan Chen "Detection of single and multilayer clouds in an artificial neural network approach", Proc. SPIE 10424, Remote Sensing of Clouds and the Atmosphere XXII, 1042408 (13 October 2017); https://doi.org/10.1117/12.2277397

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