Correction of CMPAS Precipitation Products over Complex Terrain Areas with Machine Learning Models |
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| Authors: | LI Shi-ying HUANG Xiao-long WU Wei DU Bing and JIANG Yu-he |
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| Institution: | 1. Sichuan Meteorological Observation and Data Center, Chengdu 610072 China;2. Heavy Rain and Drought-flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province, Chengdu 610072 China,1. Sichuan Meteorological Observation and Data Center, Chengdu 610072 China;2. Heavy Rain and Drought-flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province, Chengdu 610072 China,1. Sichuan Meteorological Observation and Data Center, Chengdu 610072 China;2. Heavy Rain and Drought-flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province, Chengdu 610072 China,1. Sichuan Meteorological Observation and Data Center, Chengdu 610072 China;2. Heavy Rain and Drought-flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province, Chengdu 610072 China and 1. Sichuan Meteorological Observation and Data Center, Chengdu 610072 China;2. Heavy Rain and Drought-flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province, Chengdu 610072 China |
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| Abstract: | Machine learning models were used to improve the accuracy of China Meteorological Administration Multisource Precipitation Analysis System(CMPAS)in complex terrain areas by combining rain gauge precipitation with topographic factors like altitude, slope, slope direction, slope variability, surface roughness, and meteorological factors like temperature and wind speed.The results of the correction demonstrated that the ensemble learning method has a considerably corrective effect and the three methods(Random Forest, AdaBoost, and Bagging)adopted in the study had similar results.The mean bias between CMPAS and 85% of automatic weather stations has dropped by more than 30%.The plateau region displays the largest accuracy increase, the winter season shows the greatest error reduction, and decreasing precipitation improves the correction outcome.Additionally, the heavy precipitation process'precision has improved to some degree.For individual stations, the revised CMPAS error fluctuation range is significantly reduced. |
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| Keywords: | machine learning models ensemble learning precipitation correction error correction high-resolution precipitation complex terrain |
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