Preprints
https://doi.org/10.5194/egusphere-2023-2804
https://doi.org/10.5194/egusphere-2023-2804
05 Dec 2023
 | 05 Dec 2023

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

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

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 MLANN 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 MLANN 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 MLANN 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 VZA dependence and further improve the MLANN accuracy. This approach should greatly improve the monitoring of cloud vertical structure using operational passive sensors.

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

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-2804', Anonymous Referee #1, 11 Jan 2024
    • AC1: 'Reply on RC1', S.S. Sun-Mack, 11 Feb 2024
  • RC2: 'Comment on egusphere-2023-2804', Anonymous Referee #2, 16 Jan 2024
    • AC2: 'Reply on RC2', S.S. Sun-Mack, 11 Feb 2024
Sunny Sun-Mack, Patrick Minnis, Yan Chen, Gang Hong, and William L. Smith Jr.
Sunny Sun-Mack, Patrick Minnis, Yan Chen, Gang Hong, and William L. Smith Jr.

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Short summary
Multilayer (ML) clouds affect the radiation budget differently than single-layer (SL) clouds and need to be identified in satellite images. A neural network was trained to identify ML clouds by matching imagery with lidar/radar data. This method correctly identifies ~87 % SL and ML clouds with a net accuracy gain of 7.5 % over snow-free surfaces. It is more accurate than most available methods and constitutes a first step in providing a reasonable 3-D characterization of the cloudy atmosphere.