Rainfall estimation from satellite infrared imagery using artificial neural networks

Persistent Link:
http://hdl.handle.net/10150/615703
Title:
Rainfall estimation from satellite infrared imagery using artificial neural networks
Author:
Hsu, Kuo-Lin; Sorooshian, Soroosh; Gao, Xiaogang; Gupta, Hoshin Vijai
Affiliation:
Department of Hydrology & Water Resources, The University of Arizona
Publisher:
Department of Hydrology and Water Resources, University of Arizona (Tucson, AZ)
Issue Date:
1997
Rights:
Copyright © Arizona Board of Regents
Collection Information:
This title from the Hydrology & Water Resources Technical Reports collection is made available by the Department of Hydrology & Atmospheric Sciences and the University Libraries, University of Arizona. If you have questions about titles in this collection, please contact repository@u.library.arizona.edu.
Abstract:
Infrared (IR) imagery collected by geostationary satellites provides useful information about the dirunal evolution of cloud systems. These IR images can be analyzed to indicate the location of clouds as well as the pattern of cloud top temperatures (Tbs). During the past several decades, a number of different approaches for estimation of rainfall rate (RR) from Tb have been explored and concluded that the Tb-RR relationship is (1) highly nonlinear, and (2) seasonally and regionally dependent. Therefore, to properly model the relationship, the model must be able to: (1) detect and identify a non-linear mapping of the Tb-RR relationship; (2) Incorporate information about various cloud properties extracted from IR image; (3) Use feedback obtained from RR observations to adaptively adjust to seasonal and regional variations; and (4) Effectively and efficiently process large amounts of satellite image data in real -time. In this study, a kind of artificial neural network (ANN), called Modified Counter Propagation Network (MCPN), that incorporates these features, has been developed. The model was calibrated using the data around the Japanese Islands provided by the Global Precipitation Climatology Project (GPCP) First Algorithm Intercomparison Project (AIP-I). Validation results over the Japanese Islands and Florida peninsula show that by providing limited ground-truth observation, the MCPN model is effective in monthly and hourly rainfall estimation. Comparison of results from MCPN model and GOES Precipitation Index (GPI) approach is also provided in the study.
Keywords:
Rain and rainfall -- Measurement.; Precipitation forecasting.; Infrared imaging.; Neural networks (Computer science)
Series/Report no.:
Technical Reports on Hydrology and Water Resources, No. 97-010
Sponsors:
Financial assistance leading to this research was provided from several sources, including NASA Global Change Fellowship (grant NGT-30045), NASA-EOS Interdisciplinary Program (NASA IDP-88-068), NOAA Pan American Climate Study Research Program (NA46GPO247-01), and the Hydrological Research Laboratory of the National Weather Service (NA37WH0385, NA47WH0408, and NA57WH0575). Dr. P.A. Arkin and Dr. P. Xie of the National Meteorological Center, NOAA, made many constructive suggestions and shared with us the benefits of their extensive experience. Dan Braithwaite performed the programming required to access the data and to develop the internet web site. The careful reading and editing of this manuscript was done by Ms. Corne Thies. The satellite and ground-based data for Japanese Islands were kindly made available to us by Dr. Arkin; the original source of the data is the Global Precipitation Climatology Project (GPCP) First Algorithm Intercomparison Project (AIP-1), which was supported by the World Climate Research Programme (WCRP). NASA Ames Research Center provided the GOES satellite data, and NASA Marshall Space Flight Center DAAC provided the NEXRAD WSR-88D radar composite data for the Florida peninsula. To all of these, we are profoundly grateful.

Full metadata record

DC FieldValue Language
dc.contributor.authorHsu, Kuo-Linen
dc.contributor.authorSorooshian, Sorooshen
dc.contributor.authorGao, Xiaogangen
dc.contributor.authorGupta, Hoshin Vijaien
dc.date.accessioned2016-07-07T18:36:56Z-
dc.date.available2016-07-07T18:36:56Z-
dc.date.issued1997-
dc.identifier.urihttp://hdl.handle.net/10150/615703-
dc.description.abstractInfrared (IR) imagery collected by geostationary satellites provides useful information about the dirunal evolution of cloud systems. These IR images can be analyzed to indicate the location of clouds as well as the pattern of cloud top temperatures (Tbs). During the past several decades, a number of different approaches for estimation of rainfall rate (RR) from Tb have been explored and concluded that the Tb-RR relationship is (1) highly nonlinear, and (2) seasonally and regionally dependent. Therefore, to properly model the relationship, the model must be able to: (1) detect and identify a non-linear mapping of the Tb-RR relationship; (2) Incorporate information about various cloud properties extracted from IR image; (3) Use feedback obtained from RR observations to adaptively adjust to seasonal and regional variations; and (4) Effectively and efficiently process large amounts of satellite image data in real -time. In this study, a kind of artificial neural network (ANN), called Modified Counter Propagation Network (MCPN), that incorporates these features, has been developed. The model was calibrated using the data around the Japanese Islands provided by the Global Precipitation Climatology Project (GPCP) First Algorithm Intercomparison Project (AIP-I). Validation results over the Japanese Islands and Florida peninsula show that by providing limited ground-truth observation, the MCPN model is effective in monthly and hourly rainfall estimation. Comparison of results from MCPN model and GOES Precipitation Index (GPI) approach is also provided in the study.en
dc.description.sponsorshipFinancial assistance leading to this research was provided from several sources, including NASA Global Change Fellowship (grant NGT-30045), NASA-EOS Interdisciplinary Program (NASA IDP-88-068), NOAA Pan American Climate Study Research Program (NA46GPO247-01), and the Hydrological Research Laboratory of the National Weather Service (NA37WH0385, NA47WH0408, and NA57WH0575). Dr. P.A. Arkin and Dr. P. Xie of the National Meteorological Center, NOAA, made many constructive suggestions and shared with us the benefits of their extensive experience. Dan Braithwaite performed the programming required to access the data and to develop the internet web site. The careful reading and editing of this manuscript was done by Ms. Corne Thies. The satellite and ground-based data for Japanese Islands were kindly made available to us by Dr. Arkin; the original source of the data is the Global Precipitation Climatology Project (GPCP) First Algorithm Intercomparison Project (AIP-1), which was supported by the World Climate Research Programme (WCRP). NASA Ames Research Center provided the GOES satellite data, and NASA Marshall Space Flight Center DAAC provided the NEXRAD WSR-88D radar composite data for the Florida peninsula. To all of these, we are profoundly grateful.en
dc.language.isoen_USen
dc.publisherDepartment of Hydrology and Water Resources, University of Arizona (Tucson, AZ)en
dc.relation.ispartofseriesTechnical Reports on Hydrology and Water Resources, No. 97-010en
dc.rightsCopyright © Arizona Board of Regentsen
dc.sourceProvided by the Department of Hydrology and Water Resources.en
dc.subjectRain and rainfall -- Measurement.en
dc.subjectPrecipitation forecasting.en
dc.subjectInfrared imaging.en
dc.subjectNeural networks (Computer science)en
dc.titleRainfall estimation from satellite infrared imagery using artificial neural networksen_US
dc.typetexten
dc.typeTechnical Reporten
dc.contributor.departmentDepartment of Hydrology & Water Resources, The University of Arizonaen
dc.description.collectioninformationThis title from the Hydrology & Water Resources Technical Reports collection is made available by the Department of Hydrology & Atmospheric Sciences and the University Libraries, University of Arizona. If you have questions about titles in this collection, please contact repository@u.library.arizona.edu.en
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