Precipitation estimation over Mexico applying PERSIANN system and gauge data

Persistent Link:
http://hdl.handle.net/10150/206869
Title:
Precipitation estimation over Mexico applying PERSIANN system and gauge data
Author:
Miranda, Jose Leopoldo Guevara.
Issue Date:
2002
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Developing countries like Mexico mostly rely on rain-gauge networks to obtain the much-needed rainfall data to manage their water resources. Recently, satellite-based rainfall estimation can cover remote areas of the world, such as oceans, mountains and desert, where rain gauges are unable to be installed. The purpose of this study is to develop a technique of combining Mexican rain-gauge data with satellite-based rainfall estimation to provide better rainfall information for Mexico. The Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) system is an advanced satellite-based rainfall estimation tool recently developed at the University of Arizona. In order to allow the PERSIANN system to use the gauge data, the point, daily gauge rainfall must be rescaled into hourly, grid-area rainfall. A scheme based on cloud infrared images to distribute rain rates is developed to disaggregate daily, area-averaged gauge data, and the produced high-resolution rainfall is used to train the PERSIANN system. The effectiveness of the disaggregation scheme is evaluated in southwest U.S. where the high-resolution hourly rainfall from NCEP is available for validation. Then the same strategy is applied to Mexico using the Mexican gauge data. The results show that the disaggregation scheme provides reliable high-resolution data for training PERSIANN, improving rainfall estimates over places (such as Mexico) with a lack of high-resolution ground based rainfall data.
Type:
text; Thesis-Reproduction (electronic)
LCSH Subjects:
Hydrology.; Precipitation forecasting -- Mexico.; Precipitation (Meteorology) -- Measurement -- Mexico.
Degree Name:
M.S.
Degree Level:
masters
Degree Program:
Graduate College; Hydrology and Water Resources
Degree Grantor:
University of Arizona
Advisor:
Sorooshian, Soroosh

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titlePrecipitation estimation over Mexico applying PERSIANN system and gauge dataen_US
dc.creatorMiranda, Jose Leopoldo Guevara.en_US
dc.contributor.authorMiranda, Jose Leopoldo Guevara.en_US
dc.date.issued2002-
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractDeveloping countries like Mexico mostly rely on rain-gauge networks to obtain the much-needed rainfall data to manage their water resources. Recently, satellite-based rainfall estimation can cover remote areas of the world, such as oceans, mountains and desert, where rain gauges are unable to be installed. The purpose of this study is to develop a technique of combining Mexican rain-gauge data with satellite-based rainfall estimation to provide better rainfall information for Mexico. The Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) system is an advanced satellite-based rainfall estimation tool recently developed at the University of Arizona. In order to allow the PERSIANN system to use the gauge data, the point, daily gauge rainfall must be rescaled into hourly, grid-area rainfall. A scheme based on cloud infrared images to distribute rain rates is developed to disaggregate daily, area-averaged gauge data, and the produced high-resolution rainfall is used to train the PERSIANN system. The effectiveness of the disaggregation scheme is evaluated in southwest U.S. where the high-resolution hourly rainfall from NCEP is available for validation. Then the same strategy is applied to Mexico using the Mexican gauge data. The results show that the disaggregation scheme provides reliable high-resolution data for training PERSIANN, improving rainfall estimates over places (such as Mexico) with a lack of high-resolution ground based rainfall data.en_US
dc.description.notehydrology collectionen_US
dc.typetexten_US
dc.typeThesis-Reproduction (electronic)en_US
dc.subject.lcshHydrology.en_US
dc.subject.lcshPrecipitation forecasting -- Mexico.en_US
dc.subject.lcshPrecipitation (Meteorology) -- Measurement -- Mexico.en_US
thesis.degree.nameM.S.en_US
thesis.degree.levelmastersen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineHydrology and Water Resourcesen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorSorooshian, Sorooshen_US
dc.identifier.oclc220948790-
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