Evaluation of Summer Rainfall Estimation by Satellite Data using the ANN Model for the GCM Subgrid Distribution.

Persistent Link:
http://hdl.handle.net/10150/191296
Title:
Evaluation of Summer Rainfall Estimation by Satellite Data using the ANN Model for the GCM Subgrid Distribution.
Author:
Faridhosseini, Alireza.
Issue Date:
1998
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:
A system for Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) is under development at The University of Arizona. The current core of this system is an adaptive Artificial Neural Network (ANN) model that estimates rainfall rates using infrared satellite imagery and ground-surface information. In this thesis, the model is validated for both the Florida peninsula and the Southwestern United States (using GOES-8 and ground-based data). The PERSIANN System dramatically improves estimation performance in response to the diverse rainfall characteristics of different geographical regions and time of year. Another important feature investigated in this thesis is that the coverage of rainfall of an area is strongly dependent on its size (window size), and this dependence exhibits a scaling law over a range of sizes. Secondly, this coverage is dependent on the resolution at which it is measured (pixel size). Therefore, an improved disaggregation scheme for GCMs is proposed which incorporates the previous findings so as to allow the coverage to be obtained for any area and any mean rainfall depth.
Type:
Thesis-Reproduction (electronic); text
LCSH Subjects:
Hydrology.; Rain and rainfall -- Measurement.; Rain and rainfall -- Environmental aspects.
Degree Name:
M.S.
Degree Level:
masters
Degree Program:
Hydrology and Water Resources; Graduate College
Degree Grantor:
University of Arizona
Committee Chair:
Sorooshian, Soroosh

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleEvaluation of Summer Rainfall Estimation by Satellite Data using the ANN Model for the GCM Subgrid Distribution.en_US
dc.creatorFaridhosseini, Alireza.en_US
dc.contributor.authorFaridhosseini, Alireza.en_US
dc.date.issued1998en_US
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.abstractA system for Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) is under development at The University of Arizona. The current core of this system is an adaptive Artificial Neural Network (ANN) model that estimates rainfall rates using infrared satellite imagery and ground-surface information. In this thesis, the model is validated for both the Florida peninsula and the Southwestern United States (using GOES-8 and ground-based data). The PERSIANN System dramatically improves estimation performance in response to the diverse rainfall characteristics of different geographical regions and time of year. Another important feature investigated in this thesis is that the coverage of rainfall of an area is strongly dependent on its size (window size), and this dependence exhibits a scaling law over a range of sizes. Secondly, this coverage is dependent on the resolution at which it is measured (pixel size). Therefore, an improved disaggregation scheme for GCMs is proposed which incorporates the previous findings so as to allow the coverage to be obtained for any area and any mean rainfall depth.en_US
dc.description.notehydrology collectionen_US
dc.typeThesis-Reproduction (electronic)en_US
dc.typetexten_US
dc.subject.lcshHydrology.en_US
dc.subject.lcshRain and rainfall -- Measurement.en_US
dc.subject.lcshRain and rainfall -- Environmental aspects.en_US
thesis.degree.nameM.S.en_US
thesis.degree.levelmastersen_US
thesis.degree.disciplineHydrology and Water Resourcesen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.chairSorooshian, Sorooshen_US
dc.contributor.committeememberHsu, Kuo-Linen_US
dc.contributor.committeememberXiaogang, Gaoen_US
dc.contributor.committeememberLiang, Dicksonen_US
dc.identifier.oclc214126879en_US
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