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Improving Monsoon Precipitation Prediction Using Combined Convolutional and Long Short Term Memory Neural Network

1
State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
2
Center for Hydrometeorology and Remote Sensing, University of California, Irvine, CA 92617, USA
3
Department of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
*
Author to whom correspondence should be addressed.
Water 2019, 11(5), 977; https://doi.org/10.3390/w11050977
Received: 1 April 2019 / Revised: 30 April 2019 / Accepted: 5 May 2019 / Published: 9 May 2019
(This article belongs to the Section Hydrology)
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Abstract

Precipitation downscaling is widely employed for enhancing the resolution and accuracy of precipitation products from general circulation models (GCMs). In this study, we propose a novel statistical downscaling method to foster GCMs’ precipitation prediction resolution and accuracy for the monsoon region. We develop a deep neural network composed of a convolution and Long Short Term Memory (LSTM) recurrent module to estimate precipitation based on well-resolved atmospheric dynamical fields. The proposed model is compared against the GCM precipitation product and classical downscaling methods in the Xiangjiang River Basin in South China. Results show considerable improvement compared to the European Centre for Medium-Range Weather Forecasts (ECMWF)-Interim reanalysis precipitation. Also, the model outperforms benchmark downscaling approaches, including (1) quantile mapping, (2) the support vector machine, and (3) the convolutional neural network. To test the robustness of the model and its applicability in practical forecasting, we apply the trained network for precipitation prediction forced by retrospective forecasts from the ECMWF model. Compared to the ECMWF precipitation forecast, our model makes better use of the resolved dynamical field for more accurate precipitation prediction at lead times from 1 day up to 2 weeks. This superiority decreases with the forecast lead time, as the GCM’s skill in predicting atmospheric dynamics is diminished by the chaotic effect. Finally, we build a distributed hydrological model and force it with different sources of precipitation inputs. Hydrological simulation forced with the neural network precipitation estimation shows significant advantage over simulation forced with the original ERA-Interim precipitation (with NSE value increases from 0.06 to 0.64), and the performance is only slightly worse than the observed precipitation forced simulation (NSE = 0.82). This further proves the value of the proposed downscaling method, and suggests its potential for hydrological forecasts. View Full-Text
Keywords: precipitation downscaling; convolutional neural networks; long short term memory networks; hydrological simulation precipitation downscaling; convolutional neural networks; long short term memory networks; hydrological simulation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Miao, Q.; Pan, B.; Wang, H.; Hsu, K.; Sorooshian, S. Improving Monsoon Precipitation Prediction Using Combined Convolutional and Long Short Term Memory Neural Network. Water 2019, 11, 977.

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