Quantitative Precipitation Estimation Integrated by Poissons Equation Using Radar Mosaic, Satellite, and Rain Gauge Network.
| Published in | Journal of Hydrologic Engineering, v. 22(5) |
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| Authors | Calvetti, L., Beneti, C., Neundorf, R.L., Inouye, R.T., dos Santos, T.N., Gomes, A.M., Herdies, D.L. and Gonçalves, L.G.G. |
| Publication year | 2016 |
| DOI | https://doi.org/10.1061/(asce)he.1943-5584.0001432 |
| Affiliations | Dept. of Meteorology, Federal Univ. of Pelotas, Campus Universitario Cx. Postal 354, CEP 96010-900, Pelotas, RS, Brazil, Parana Meteorological System(SIMEPAR), Francisco H. dos Santos 210, CEP 81531-980, Curitiba, PR, Brazil, Meteorological Research Institute, Sao Paulo State Univ. (IPMET/UNESP), Eng. Luiz Edmundo Coube, CEP 17033-360, Bauru, SP, Brazil, Center for Weather Forecasting and Climate Research (CPTEC/INPE), Presidente Dutra Highway, km 39, CEP 12630-000, Cachoeira Paulista, SP, Brazil
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| IAI Program | CRN3 |
| IAI Project | CRN3035 |
| Keywords | |
Abstract
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 10 mm h⁻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.