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Proceedings Paper • Open Access

Advances in neural network detection and retrieval of multilayer clouds for CERES using multispectral satellite data

Paper Abstract

An artificial neural network (ANN) algorithm, employing several Aqua MODIS infrared channels, the retrieved total cloud visible optical depth, and vertical humidity profiles is trained to detect multilayer (ML) ice-over-water cloud systems as identified by matched CloudSat and CALIPSO (CC) data. The multilayer ANN, or MLANN, algorithm is also trained to retrieve the optical depth and the top and base heights of the upper-layer ice clouds in ML systems. The trained MLANN was applied to independent MODIS data resulting in a combined ML and single layer hit rate of 80% (77%) for nonpolar regions during the day (night). The results are more accurate than currently available methods and the previous version of the MLANN. Upper-layer cloud top and base heights are accurate to ±1.2 km and ±1.6 km, respectively, while the uncertainty in optical depth is ±0.457 and ±0.556 during day and night, respectively. Areas of further improvement and development are identified and will be addressed in future versions of the MLANN.

Paper Details

Date Published: 9 October 2019
PDF: 12 pages
Proc. SPIE 11152, Remote Sensing of Clouds and the Atmosphere XXIV, 1115202 (9 October 2019);
Show Author Affiliations
Patrick Minnis, SSAI (United States)
Sunny Sun-Mack, SSAI (United States)
William L. Smith Jr., NASA Langley Research Ctr. (United States)
Gang Hong, SSAI (United States)
Yan Chen, SSAI (United States)


Published in SPIE Proceedings Vol. 11152:
Remote Sensing of Clouds and the Atmosphere XXIV
Adolfo Comerón; Evgueni I. Kassianov; Klaus Schäfer; Richard H. Picard; Konradin Weber; Upendra N. Singh, Editor(s)

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