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Open AccessArticle

Daytime Rainy Cloud Detection and Convective Precipitation Delineation Based on a Deep Neural Network Method Using GOES-16 ABI Images

1
NSF Spatiotemporal Innovation Center, George Mason Univ., Fairfax, VA 22030, USA
2
Department of Geography and GeoInformation Science, George Mason Univ., Fairfax, VA 22030, USA
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Department of Geography, Pennsylvania State University, State College, PA 16801, USA
4
Department of Atmospheric, Oceanic and Earth Sciences, George Mason Univ., Fairfax, VA 22030, USA
5
NASA Center for Climate Simulation, Greenbelt, MD 20771, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(21), 2555; https://doi.org/10.3390/rs11212555
Received: 27 September 2019 / Revised: 19 October 2019 / Accepted: 26 October 2019 / Published: 30 October 2019
(This article belongs to the Section Atmosphere Remote Sensing)
Precipitation, especially convective precipitation, is highly associated with hydrological disasters (e.g., floods and drought) that have negative impacts on agricultural productivity, society, and the environment. To mitigate these negative impacts, it is crucial to monitor the precipitation status in real time. The new Advanced Baseline Imager (ABI) onboard the GOES-16 satellite provides such a precipitation product in higher spatiotemporal and spectral resolutions, especially during the daytime. This research proposes a deep neural network (DNN) method to classify rainy and non-rainy clouds based on the brightness temperature differences (BTDs) and reflectances (Ref) derived from ABI. Convective and stratiform rain clouds are also separated using similar spectral parameters expressing the characteristics of cloud properties. The precipitation events used for training and validation are obtained from the IMERG V05B data, covering the southeastern coast of the U.S. during the 2018 rainy season. The performance of the proposed method is compared with traditional machine learning methods, including support vector machines (SVMs) and random forest (RF). For rainy area detection, the DNN method outperformed the other methods, with a critical success index (CSI) of 0.71 and a probability of detection (POD) of 0.86. For convective precipitation delineation, the DNN models also show a better performance, with a CSI of 0.58 and POD of 0.72. This automatic cloud classification system could be deployed for extreme rainfall event detection, real-time forecasting, and decision-making support in rainfall-related disasters. View Full-Text
Keywords: geoAI; precipitation detection; convective; stratiform; ABI; deep learning geoAI; precipitation detection; convective; stratiform; ABI; deep learning
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Liu, Q.; Li, Y.; Yu, M.; Chiu, L.S.; Hao, X.; Duffy, D.Q.; Yang, C. Daytime Rainy Cloud Detection and Convective Precipitation Delineation Based on a Deep Neural Network Method Using GOES-16 ABI Images. Remote Sens. 2019, 11, 2555.

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