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Proceedings Paper

Using deep recurrent neural network for direct beam solar irradiance cloud screening
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Paper Abstract

Cloud screening is an essential procedure for in-situ calibration and atmospheric properties retrieval on (UV-)MultiFilter Rotating Shadowband Radiometer [(UV-)MFRSR]. Previous study has explored a cloud screening algorithm for direct-beam (UV-)MFRSR voltage measurements based on the stability assumption on a long time period (typically a half day or a whole day). To design such an algorithm requires in-depth understanding of radiative transfer and delicate data manipulation. Recent rapid developments on deep neural network and computation hardware have opened a window for modeling complicated End-to-End systems with a standardized strategy. In this study, a multi-layer dynamic bidirectional recurrent neural network is built for determining the cloudiness on each time point with a 17-year training dataset and tested with another 1-year dataset. The dataset is the daily 3-minute cosine corrected voltages, airmasses, and the corresponding cloud/clear-sky labels at two stations of the USDA UV-B Monitoring and Research Program. The results show that the optimized neural network model (3-layer, 250 hidden units, and 80 epochs of training) has an overall test accuracy of 97.87% (97.56% for the Oklahoma site and 98.16% for the Hawaii site). Generally, the neural network model grasps the key concept of the original model to use data in the entire day rather than short nearby measurements to perform cloud screening. A scrutiny of the logits layer suggests that the neural network model automatically learns a way to calculate a quantity similar to total optical depth and finds an appropriate threshold for cloud screening.

Paper Details

Date Published: 1 September 2017
PDF: 14 pages
Proc. SPIE 10405, Remote Sensing and Modeling of Ecosystems for Sustainability XIV, 1040503 (1 September 2017); doi: 10.1117/12.2273364
Show Author Affiliations
Maosi Chen, USDA UV-B Monitoring and Research Program (United States)
John M. Davis, USDA UV-B Monitoring and Research Program (United States)
Chaoshun Liu, East China Normal Univ. (China)
Zhibin Sun, USDA UV-B Monitoring and Research Program (United States)
Melina Maria Zempila, USDA UV-B Monitoring and Research Program (United States)
Wei Gao, USDA UV-B Monitoring and Research Program (United States)
Colorado State Univ. (United States)


Published in SPIE Proceedings Vol. 10405:
Remote Sensing and Modeling of Ecosystems for Sustainability XIV
Wei Gao; Ni-Bin Chang; Jinnian Wang, Editor(s)

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