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Development of functional relationships between radar and rain gage data using inductive modeling techniques

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Date Issued:
2008
Summary:
Traditional methods such as distance weighing, correlation and data driven methods have been used in the estimation of missing precipitation data. Also common is the use of radar (NEXRAD) data to provide better spatial distribution of precipitation as well as infilling missing rain gage data. Conventional regression models are often used to capture highly variant nonlinear spatial and temporal relationships between NEXRAD and rain gage data. This study aims to understand and model the relationships between radar (NEXRAD) estimated rainfall data and the data measured by conventional rain gages. The study is also an investigation into the use of emerging computational data modeling (inductive) techniques and mathematical programming formulations to develop new optimal functional approximations. Radar based rainfall data and rain gage data are analyzed to understand the spatio-temporal associations, as well as the effect of changes in the length or availability of data on the models. The upper and lower Kissimmee basins of south Florida form the test-bed to evaluate the proposed and developed approaches and also to check the validity and operational applicability of these functional relationships among NEXRAD and rain gage data for infilling of missing data.
Title: Development of functional relationships between radar and rain gage data using inductive modeling techniques.
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Name(s): Peters, Delroy.
College of Engineering and Computer Science
Department of Civil, Environmental and Geomatics Engineering
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Issuance: monographic
Date Issued: 2008
Publisher: Florida Atlantic University
Physical Form: electronic
Extent: x, 194 p. : ill. (some col.).
Language(s): English
Summary: Traditional methods such as distance weighing, correlation and data driven methods have been used in the estimation of missing precipitation data. Also common is the use of radar (NEXRAD) data to provide better spatial distribution of precipitation as well as infilling missing rain gage data. Conventional regression models are often used to capture highly variant nonlinear spatial and temporal relationships between NEXRAD and rain gage data. This study aims to understand and model the relationships between radar (NEXRAD) estimated rainfall data and the data measured by conventional rain gages. The study is also an investigation into the use of emerging computational data modeling (inductive) techniques and mathematical programming formulations to develop new optimal functional approximations. Radar based rainfall data and rain gage data are analyzed to understand the spatio-temporal associations, as well as the effect of changes in the length or availability of data on the models. The upper and lower Kissimmee basins of south Florida form the test-bed to evaluate the proposed and developed approaches and also to check the validity and operational applicability of these functional relationships among NEXRAD and rain gage data for infilling of missing data.
Identifier: 316795968 (oclc), 166454 (digitool), FADT166454 (IID), fau:2837 (fedora)
Note(s): by Delroy Peters.
Thesis (M.S.)--Florida Atlantic University, 2008.
Includes bibliography.
Electronic reproduction. Boca Raton, Fla., 2008. Mode of access: World Wide Web.
Subject(s): Weather control -- Mathematical models
Radar meteorology -- Technological innovations
Precipitation (Meteorology) -- Measurement
Weather forecasting -- Technological innovations
Held by: FBoU FAUER
Persistent Link to This Record: http://purl.flvc.org/FAU/166454
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU