Spatiotemporal distribution of the rainstorm and the relationship between urban heat island and urban rain island in Beijing on July 21, 2012
MENG Dan1,2,3,4,5, GONG Huili1,2,3,4,5, LI Xiaojuan1,2,3,4,5, YANG Siyao1,2,3,4,5
1. Beijing Laboratory of Water Resource Security, Beijing 100048, China;
2. College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China;
3. Urban Environmental Processes and Digital Modeling Laboratory, Beijing 100048, China;
4. Laboratory of 3D Information Acquisition and Application, MOST, Beijing 100048, China;
5. Beijing Municipal Key Laboratory of Resources Environment and GIS, Beijing 100048, China
In this paper, the authors selected July 21, 2012, the biggest rainfall day since the founding of People's Republic China in Beijing, as the study target. The rainfall data from both Tropical Rainfall Measuring Mission (TRMM) and meteorological observations and MODIS LST products were mainly used to study the spatiotemporal distribution of rainstorm and the relationship between urban heat island (UHI) and urban rain island (URI). The spatial interpolation, spatial downscaling, accuracy assessment and correlation analysis were used in the study. Some conclusions have been reached. Firstly, the heavy rainfall area was located mainly in southern Beijing. The rainfall process moved from west to east, as shown by tracking the rainfall maxima of 3 h TRMM data. Secondly, the accuracy of TRMM data was improved by downscaling, as evidenced by the fact that the correlation between TRMM data and observational data was improved and RMSE decreased simultaneously. Finally, the spatial distribution of URI is consistent with UHI and the correlation between the two can produce optimal result in the maximum rainfall periods.
孟丹, 宫辉力, 李小娟, 杨思遥. 北京7·21暴雨时空分布特征及热岛-雨岛响应关系[J]. 国土资源遥感, 2017, 29(1): 178-185.
MENG Dan, GONG Huili, LI Xiaojuan, YANG Siyao. Spatiotemporal distribution of the rainstorm and the relationship between urban heat island and urban rain island in Beijing on July 21, 2012. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(1): 178-185.
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2017, Vol. 29 
