Quantitative Precipitation Estimation Integrated by Poisson's Equation Using Radar Mosaic, Satellite, and Rain Gauge Network

被引:3
|
作者
Calvetti, Leonardo [1 ]
Beneti, Cesar [2 ]
Antunes Neundorf, Reverton Luis [2 ]
Inouye, Rafael Toshio [2 ]
dos Santos, Tiago Noronha [2 ]
Gomes, Ana Maria [3 ]
Herdies, Dirceu Luis [4 ]
Goncalves de Goncalves, Luis Gustavo [4 ]
机构
[1] Univ Fed Pelotas, Dept Meteorol, Campus Univ Cx Postal 354, BR-96010900 Pelotas, RS, Brazil
[2] Parana Meteorol Syst SIMEPAR, Francisco H dos Santos 210, BR-81531980 Curitiba, Parana, Brazil
[3] Sao Paulo State Univ IPMET UNESP, Meteorol Res Inst, BR-17033360 Bauru, SP, Brazil
[4] Ctr Weather Forecasting & Climate Res CPTEC INPE, Presidente Dutra Highway,Km 39, BR-12630000 Cachoeira Paulista, SP, Brazil
关键词
Quantitative precipitation estimation (QPE); Weather radar; Satellite; Hydrometeorology; Rainfall; GLOBAL PRECIPITATION; SOUTH-AMERICA; PRODUCT; TRMM; PREDICTABILITY; VALIDATION; RESOLUTION; SEVIRI; CMORPH; IMAGES;
D O I
10.1061/(ASCE)HE.1943-5584.0001432
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
High-resolution quantitative precipitation estimation (QPE) from radar and satellite combined with rain gauges is one of the most important guides for hydrological forecasts. Whereas rain gauges provide accurate measurement at a point, remote sensing helps to retrieve the spatial pattern. An algorithm, named Siprec, has been used to blend rain gauges, radar mosaic data, and satellite Eumetsat/MPE estimates by using Poisson's equation over two basins in Brazil. The results indicated that Siprec decreased the root mean square error (RMSE) when compared to radar and satellite estimates as well as improved the correlation. Most of the errors were related to precipitation above 10mmh-1, due to large spatial variability, typical of deep convection. The solution of Poisson's equation acts directly on the data received at a certain time, converging the amplitude to the rain gauge values and keeping the spatial distribution of the radar or satellite measurement without a priori adjustments. This is an important advantage in an operational environment because it does not require frequent processing to update the weights like other schemes. (C) 2016 American Society of Civil Engineers.
引用
收藏
页数:11
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