Evaluation of high-resolution precipitation estimates from satellites during July 2012 Beijing flood event using dense rain gauge observations

Sheng Chen; Huijuan Liu; Yalei You; Esther Mullens; Junjun Hu; Ye Yuan; Mengyu Huang; Li He; Yongming Luo; Xingji Zeng; Guoqiang Tang; Yang Hong

doi:10.1371/journal.pone.0089681

Evaluation of high-resolution precipitation estimates from satellites during July 2012 Beijing flood event using dense rain gauge observations

PLoS One. 2014 Apr 1;9(4):e89681. doi: 10.1371/journal.pone.0089681. eCollection 2014.

Authors

Sheng Chen¹, Huijuan Liu², Yalei You³, Esther Mullens⁴, Junjun Hu⁵, Ye Yuan², Mengyu Huang⁶, Li He⁷, Yongming Luo⁷, Xingji Zeng⁸, Guoqiang Tang⁹, Yang Hong¹

Affiliations

¹ School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, Oklahoma, United States of America; Advanced Radar Research Center, National Weather Center, Norman, Oklahoma, United States of America.
² Anhui Weather Modification Office, Hefei, China.
³ Departments of Earth, Ocean, and Atmospheric Science, Florida State University, Tallahassee, Florida, United States of America.
⁴ Cooperative Institutes for Mesoscale Meteorological Studies, University of Oklahoma, Norman, Oklahoma, United States of America; School of Meteorology, University of Oklahoma, Norman, Oklahoma, United States of America.
⁵ School of Computer Science, University of Oklahoma, Norman, Oklahoma, United States of America.
⁶ Beijing Weather Modification Office, Beijing, China.
⁷ Remote Sensing Application Test Base of National Satellite Center, Gaugnxi, China; Disaster Mitigation Institute of the Guangxi Meteorological Bureau, Nanning, Gaugnxi, China.
⁸ Meteorological Information Center of the Guangxi Meteorological Bureau, Nanning, Gaugnxi, China.
⁹ Department of Hydraulic Engineering, Tsinghua University, Beijing, China.

Abstract

Satellite-based precipitation estimates products, CMORPH and PERSIANN-CCS, were evaluated with a dense rain gauge network over Beijing and adjacent regions for an extremely heavy precipitation event on July 21 2012. CMORPH and PEERSIANN-CSS misplaced the region of greatest rainfall accumulation, and failed to capture the spatial pattern of precipitation, evidenced by a low spatial correlation coefficient (CC). CMORPH overestimated the daily accumulated rainfall by 22.84% while PERSIANN-CCS underestimated by 72.75%. In the rainfall center, both CMORPH and PERSIANN-CCS failed to capture the temporal variation of the rainfall, and underestimated rainfall amounts by 43.43% and 87.26%, respectively. Based on our results, caution should be exercised when using CMORPH and PERSIANN-CCS as input for monitoring and forecasting floods in Beijing urban areas, and the potential for landslides in the mountainous zones west and north of Beijing.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Beijing
Floods*
Geography
Probability
Rain*
Satellite Communications*
Spatial Analysis
Time Factors

Grants and funding

This work was supported by the R & D Special Foundation for Public Welfare Industry (meteorology) of China under Grant No. GYHY201306040; the National Natural Science Foundation of China with Grant No. 40905019, 41105098, 41275030; the State Key Laboratory of Severe Weather Open Fund 2013LASW–A09. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.