Journal
ENVIRONMENTAL MODELLING & SOFTWARE
Volume 134, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.envsoft.2020.104856
Keywords
Infrared information; Precipitation estimation; Deep learning; Convolutional neural networks
Categories
Funding
- U.S. Department of Energy (DOE) [DE-IA0000018]
- California Energy Commission (CEC) [300-15-005]
- University of California [4600010378 TO15 Am 22]
- MASEEH fellowship
- NOAA/NESDIS/NCDC (Prime Award) [NA09NES4400006]
- NOAA/NESDIS/NCDC (NCSU CICS)
- NOAA/NESDIS/NCDC [2009-1380-01]
- National Oceanic and Atmospheric Administration [ST133017CQ0058]
- Riverside Technology, Inc.
- office vice-chancellor for research of graduate students
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Reliable near real-time precipitation estimates are essential for monitoring and managing of natural disasters such as floods. Quality of inputs and capability of the retrieval algorithm are two important aspects for developing satellite-based precipitation datasets. Most retrieval algorithms utilize infrared (IR) information as their input due to its fine spatiotemporal resolution and near-instantaneous availability. However, their sole reliance on IR information limits their capability to learn different mechanisms of precipitation during training, resulting in less accurate estimates. Moreover, recent advances in the field of machine learning offer attractive opportunities to improve the precipitation retrieval algorithms. This study investigates the effectiveness of adding geographical information (i.e. latitude and longitude) to IR information and the application of a U-Net-based convolutional neural network for improving the accuracy of retrieval algorithms. This research suggests that applying an appropriate CNN architecture on geographical and IR information provides an opportunity to improve the satellite-based precipitation products.
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