Quantitative Precipitation Estimation Integrated by Poisson’s Equation Using Radar Mosaic, Satellite, and Rain Gauge Network
AbstractHigh-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...
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creator | Calvetti, Leonardo Beneti, Cesar Neundorf, Réverton Luís Antunes Inouye, Rafael Toshio Santos, Tiago Noronha dos Gomes, Ana Maria Herdies, Dirceu Luis de Gonçalves, Luis Gustavo Gonçalves |
description | AbstractHigh-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. |
doi_str_mv | 10.1061/(ASCE)HE.1943-5584.0001432 |
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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. 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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.</description><subject>Case Studies</subject><subject>Case Study</subject><subject>Estimates</subject><subject>Gages</subject><subject>Gauges</subject><subject>Hydrology</subject><subject>Poisson equation</subject><subject>Radar</subject><subject>Rain</subject><subject>Satellites</subject><issn>1084-0699</issn><issn>1943-5584</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNqNkc9OAjEQxjdGE_HPOzSeMAFst7vL1hshq5Cgosi56Xa7pAgttF0NF-Nr-Ho-iV2XeDPxNDOd7zeZzhcEFwj2EEzQVXswG2aXo6yHSIS7cZxGPQghinB4ELR-3w59DtOoCxNCjoMTa5e1xhet4P2xYspJx5x8FWBqBJebn0orkFkn1006Vk4sDHOiAPkOTLW0Vquvj08Lsm3VSOZWqgV4YgUz4E5bJnkHzDyxWkknOoCpwjelAresWghwL9ybNi9nwVHJVlac7-NpML_Jnoej7uThdjwcTLosQpGrP5ELDAsMo7IUJMEEpxznfRKKfoxzyAsShwKmGHGYiwgVYckQ4knMcRKXqMCnQbuZuzF6Wwnr6Fpa7ndjSujKUpQSTFCc9uF_pCiNUT-spdeNlBttrREl3Rh_MbOjCNLaH0prf-goo7UXtPaC7v3xcNLAzE-nS10Z5S_wS_4NfgOGMZZN</recordid><startdate>20170501</startdate><enddate>20170501</enddate><creator>Calvetti, Leonardo</creator><creator>Beneti, Cesar</creator><creator>Neundorf, Réverton Luís Antunes</creator><creator>Inouye, Rafael Toshio</creator><creator>Santos, Tiago Noronha dos</creator><creator>Gomes, Ana Maria</creator><creator>Herdies, Dirceu Luis</creator><creator>de Gonçalves, Luis Gustavo Gonçalves</creator><general>American Society of Civil Engineers</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7UA</scope><scope>C1K</scope><scope>F1W</scope><scope>H96</scope><scope>L.G</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>KR7</scope></search><sort><creationdate>20170501</creationdate><title>Quantitative Precipitation Estimation Integrated by Poisson’s Equation Using Radar Mosaic, Satellite, and Rain Gauge Network</title><author>Calvetti, Leonardo ; Beneti, Cesar ; Neundorf, Réverton Luís Antunes ; Inouye, Rafael Toshio ; Santos, Tiago Noronha dos ; Gomes, Ana Maria ; Herdies, Dirceu Luis ; de Gonçalves, Luis Gustavo Gonçalves</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a414t-558be30d304ffe963938c3b792e753b0cd952e0831c0be41d2fa11c65c365f1d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Case Studies</topic><topic>Case Study</topic><topic>Estimates</topic><topic>Gages</topic><topic>Gauges</topic><topic>Hydrology</topic><topic>Poisson equation</topic><topic>Radar</topic><topic>Rain</topic><topic>Satellites</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Calvetti, Leonardo</creatorcontrib><creatorcontrib>Beneti, Cesar</creatorcontrib><creatorcontrib>Neundorf, Réverton Luís Antunes</creatorcontrib><creatorcontrib>Inouye, Rafael Toshio</creatorcontrib><creatorcontrib>Santos, Tiago Noronha dos</creatorcontrib><creatorcontrib>Gomes, Ana Maria</creatorcontrib><creatorcontrib>Herdies, Dirceu Luis</creatorcontrib><creatorcontrib>de Gonçalves, Luis Gustavo Gonçalves</creatorcontrib><collection>CrossRef</collection><collection>Aqualine</collection><collection>Water Resources Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><jtitle>Journal of hydrologic engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Calvetti, Leonardo</au><au>Beneti, Cesar</au><au>Neundorf, Réverton Luís Antunes</au><au>Inouye, Rafael Toshio</au><au>Santos, Tiago Noronha dos</au><au>Gomes, Ana Maria</au><au>Herdies, Dirceu Luis</au><au>de Gonçalves, Luis Gustavo Gonçalves</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Quantitative Precipitation Estimation Integrated by Poisson’s Equation Using Radar Mosaic, Satellite, and Rain Gauge Network</atitle><jtitle>Journal of hydrologic engineering</jtitle><date>2017-05-01</date><risdate>2017</risdate><volume>22</volume><issue>5</issue><issn>1084-0699</issn><eissn>1943-5584</eissn><abstract>AbstractHigh-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.</abstract><pub>American Society of Civil Engineers</pub><doi>10.1061/(ASCE)HE.1943-5584.0001432</doi></addata></record> |
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subjects | Case Studies Case Study Estimates Gages Gauges Hydrology Poisson equation Radar Rain Satellites |
title | Quantitative Precipitation Estimation Integrated by Poisson’s Equation Using Radar Mosaic, Satellite, and Rain Gauge Network |
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