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Correction of CMPAS precipitation Products over Complex Terrain Areas with Machine Learning Models

Journal of Tropical Meteorology 2023年第2期29卷 264-276页

作者：李施颖黄晓龙吴薇杜冰蒋雨荷 Sichuan Meteorological Observation and Data Center Chengdu 610072 China Heavy Rain and Drought-flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province Chengdu 610072 China

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 *** 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 *** 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 ***,the heavy precipitation process’precision has improved to some *** individual stations,the revised CMPAS error fluctuation range is significantly reduced.

关键词： machine learning models ensemble learning precipitation correction error correction high-resolution precipitation complex terrain

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Evaluation of CMPAS precipitation products over Sichuan,China

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Atmospheric and Oceanic Science Letters 2022年第2期15卷 47-53页

作者： Shiying Li Xiaolong Huang Wei Wu Bing Du Yuhe Jiang Sichuan Meteorological Observation and Data Center Heavy Rain and Drought-Flood Disasters in Plateau and Basin Key Laboratory of Sichuan ProvinceChengduChina

high-quality and high-resolution precipitation data are the basis for mesoscale numerical weather forecasting,model verification,and hydrological monitoring,which play an important role in meteorological and hydrological disaster prevention and *** this study,high-density rain gauge data are used to evaluate the fusion accuracy of the China Meteorological Administration Multisource precipitation Analysis System(CMPAS),and four CMPAS products with different spatial and temporal resolution and different data sources are compared,to derive the applicability of *** show that all the CMPAS products show high accuracy in the Sichuan Basin,followed by Panxi Area and the western Sichuan *** errors of the four products all rise with the increase in *** overestimates precipitation in summer and autumn and underestimates it in spring and ***,the applicability of these fused data in the Sichuan Basin is quite *** to the lack of observations and the influence of the terrain and meteorological conditions,the evaluation of CMPAS in the plateau area needs further analysis.

关键词： Multisource precipitation products high-resolution precipitation CMPAS Applicability assessment

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China land soil moisture EnKF data assimilation based on satellite remote sensing data

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Science China Earth Sciences 2011年第9期54卷 1430-1440页

作者： SHI ChunXiang XIE ZhengHui QIAN Hui LIANG MiaoLing YANG XiaoChun National Satellite Meteorological Center China Meteorological Administration Beijing 100081 China Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 China Institute of Geology Chinese Academy of Geological Sciences Key Geodynamics Laboratory the Ministry of Land and Resources Beijing 100037 China National Meteorological Center China Meteorological Administration Beijing 100081 China

Soil moisture plays an important role in land-atmosphere interactions. It is an important geophysical parameter in research on climate, hydrology, agriculture, and forestry. Soil moisture has important climatic effects by influencing ground evapotranspi ration, runoff, surface reflectivity, surface emissivity, surface sensible heat and latent heat flux. At the global scale, the extent of its influence on the atmosphere is second only to that of sea surface temperature. At the terrestrial scale, its influence is even greater than that of sea surface temperatures. This paper presents a China Land Soil Moisture Data Assimilation System (CLSMDAS) based on EnKF and land process models, and results of the application of this system in the China Land Soil Moisture Data Assimilation tests. CLSMDAS is comprised of the following components: 1) A land process mo del—Community Land Model Version 3.0 (CLM3.0)—developed by the US National Center for Atmospheric Research (NCAR); 2) precipitation of atmospheric forcing data and surface-incident solar radiation data come from hourly outputs of the FY2 geostationary meteorological satellite; 3) EnKF (Ensemble Kalman Filter) land data assimilation method; and 4) Observa tion data including satellite-inverted soil moisture outputs of the AMSR-E satellite and soil moisture observation data. Results of soil moisture assimilation tests from June to September 2006 were analyzed with CLSMDAS. Both simulation and assimila tion results of the land model reflected reasonably the temporal-spatial distribution of soil moisture. The assimilated soil mois ture distribution matches very well with severe summer droughts in Chongqing and Sichuan Province in August 2006, the worst since the foundation of the People’s Republic of China in 1949. It also matches drought regions that occurred in eastern Hubei and southern Guangxi in September.

关键词： EnKF land data assimilation AMSR-E soil moisture FY2C geostationary satellite high-resolution precipitation surface incident solar radiation

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