TRMM (Tropical Rainfall Measuring Mission) data have been made available to the public users since June 1998. In this paper, some preliminary research is reported for the case study of heavy rainfall over the Yangtze ...TRMM (Tropical Rainfall Measuring Mission) data have been made available to the public users since June 1998. In this paper, some preliminary research is reported for the case study of heavy rainfall over the Yangtze River Basin using TRMM data at 2140 UTC 20 July 1998. TRMM derived precipitation products are also compared with rain gauge observation, ground radar data and numerical model simulation results. It is shown that TRMM data can be easily used to monitor the heavy rainfall and have many applications. Key words Heavy rainfall - TRMM data - Precipitation structure The research was supported by the National Natural Science Foundation of China (Grant No. 49705064), the Ministry of Science and Technology of China (Grant No. G19998040909), the Chinese Meteorological Administration, and the Ministry of Personnel (Outstanding Returned Oversea Scholar Program).展开更多
High-resolution precipitation data is conducive to objectively describe the spatial-temporal variability of regional precipitation,and the study of downscaling techniques and spatial scale effects can provide technica...High-resolution precipitation data is conducive to objectively describe the spatial-temporal variability of regional precipitation,and the study of downscaling techniques and spatial scale effects can provide technical and theoretical support to improve the spatial resolution and accuracy of satellite precipitation data.In this study,we used a machine learning algorithm combined with a regression algorithm RF-PLS(Random Forest-Partial Least Squares)to construct a downscaling model to obtain three types of high-resolution TRMM(Tropical Rainfall Measuring Mission)downscaled precipitation data for the years 2000-2017 at 250 m,500 m,and 1km.The scale effects with topographic and geomorphological features in the study area were analysed.Finally,we described the spatial and temporal variation of precipitation based on the optimal TRMM downscaled precipitation data.The results showed that:1)The linear relationships between the TRMM downscaled precipitation data obtained by each of the three downscaled models(PLS,RF,and RF-PLS)and the precipitation at the observation stations were improved compared to the linear relationships between the original TRMM data and the precipitation at the observation stations.The accuracy of the RF-PLS model was better than the other two models.2)Based on the RF-PLS model,the resolution of the TRMM data was increased to three different scales(250 m,500 m,and 1 km),considering the scale effects with topographic and geomorphological features.The precipitation simulation effect with a spatial resolution of 500 m was better than the other two scales.3)The annual precipitation was the highest in the areas with extremely high mountains,followed by the mediumhigh mountain,high mountain,medium mountain,medium-low mountain,plain,low mountain,and basin.展开更多
基金the National Natural Science Foundation of China (Grant No.49705064), the Ministry of Science and Technology of China (Grant N
文摘TRMM (Tropical Rainfall Measuring Mission) data have been made available to the public users since June 1998. In this paper, some preliminary research is reported for the case study of heavy rainfall over the Yangtze River Basin using TRMM data at 2140 UTC 20 July 1998. TRMM derived precipitation products are also compared with rain gauge observation, ground radar data and numerical model simulation results. It is shown that TRMM data can be easily used to monitor the heavy rainfall and have many applications. Key words Heavy rainfall - TRMM data - Precipitation structure The research was supported by the National Natural Science Foundation of China (Grant No. 49705064), the Ministry of Science and Technology of China (Grant No. G19998040909), the Chinese Meteorological Administration, and the Ministry of Personnel (Outstanding Returned Oversea Scholar Program).
基金supported by the National Natural Science Foundation of China(Grant No.41941017 and 41877522)the National Key Research and Development Program of China(Grant No.2021YFE0116800)Jiangsu Province Key R&D Program(Social Development)Project of China(Grant No.BE2019776)。
文摘High-resolution precipitation data is conducive to objectively describe the spatial-temporal variability of regional precipitation,and the study of downscaling techniques and spatial scale effects can provide technical and theoretical support to improve the spatial resolution and accuracy of satellite precipitation data.In this study,we used a machine learning algorithm combined with a regression algorithm RF-PLS(Random Forest-Partial Least Squares)to construct a downscaling model to obtain three types of high-resolution TRMM(Tropical Rainfall Measuring Mission)downscaled precipitation data for the years 2000-2017 at 250 m,500 m,and 1km.The scale effects with topographic and geomorphological features in the study area were analysed.Finally,we described the spatial and temporal variation of precipitation based on the optimal TRMM downscaled precipitation data.The results showed that:1)The linear relationships between the TRMM downscaled precipitation data obtained by each of the three downscaled models(PLS,RF,and RF-PLS)and the precipitation at the observation stations were improved compared to the linear relationships between the original TRMM data and the precipitation at the observation stations.The accuracy of the RF-PLS model was better than the other two models.2)Based on the RF-PLS model,the resolution of the TRMM data was increased to three different scales(250 m,500 m,and 1 km),considering the scale effects with topographic and geomorphological features.The precipitation simulation effect with a spatial resolution of 500 m was better than the other two scales.3)The annual precipitation was the highest in the areas with extremely high mountains,followed by the mediumhigh mountain,high mountain,medium mountain,medium-low mountain,plain,low mountain,and basin.
