Compared to ground precipitation measurements, satellite-based precipitation estimation products have the advantage of global coverage and high spatiotemporal resolutions. However, the accuracy of satellite-based precipitation products is still insufficient to serve many weather, climate, and hydrologic applications at high resolutions. In this paper, we develop a state-of-the-art deep learning framework for precipitation estimation using bispectral satellite information, Infrared (IR), and water vapor (WV) channels. Specifically, we design a two-stage framework for precipitation estimation from bispectral information, consisting of an initial rain/no-rain (R/NR) binary classification, followed by a second stage estimating the non-zero precipitation amount. In the first stage, the model aims to eliminate the large fraction of NR pixels and to delineate precipitation regions precisely. In the second stage, the model aims to estimate the point-wise precipitation amount accurately while preserving its heavy-skewed distribution. We apply stacked denoising auto-encoders (SDAEs), a commonly used deep learning method, in both stages. We evaluate performance along a number of common performance measures, including both R/NR and real-valued precipitation accuracy, and compare with an operational product, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS). For R/NR binary classification, our proposed two-stage model outperforms PERSIANN-CCS by 32.56% in the Critical Success Index (CSI). For real-valued precipitation estimation, the two-stage model is 23.40% lower in average bias, 44.52% lower in average mean squared error, and has a 27.21% higher correlation coefficient. Hence, the two-stage deep learning framework has the potential to serve as a more accurate and more reliable satellite-based precipitation estimation product. We also provide some future directions for development of satellite-based precipitation estimation products in both incorporating auxiliary information and improving retrieval algorithms.
Journal of Hydrometeorology – American Meteorological Society
Published: Jan 24, 2018
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