Show simple item record

dc.contributor.advisorCarroll, Raymond J.en_US
dc.contributor.advisorSherman, Michaelen_US
dc.creatorLi, Boen_US
dc.date.accessioned2010-01-15T00:15:10Zen_US
dc.date.accessioned2010-01-16T02:17:41Z
dc.date.available2010-01-15T00:15:10Zen_US
dc.date.available2010-01-16T02:17:41Z
dc.date.created2006-08en_US
dc.date.issued2009-06-02en_US
dc.identifier.urihttp://hdl.handle.net/1969.1/ETD-TAMU-1868
dc.description.abstractThis dissertation includes two parts. Part 1 develops a geostatistical method to calibrate Texas NexRad rainfall estimates using rain gauge measurements. Part 2 explores the asymptotic joint distribution of sample space-time covariance estimators. The following two paragraphs briefly summarize these two parts, respectively. Rainfall is one of the most important hydrologic model inputs and is considered a random process in time and space. Rain gauges generally provide good quality data; however, they are usually too sparse to capture the spatial variability. Radar estimates provide a better spatial representation of rainfall patterns, but they are subject to substantial biases. Our calibration of radar estimates, using gauge data, takes season, rainfall type and rainfall amount into account, and is accomplished via a combination of threshold estimation, bias reduction, regression techniques and geostatistical procedures. We explore a varying-coefficient model to adapt to the temporal variability of rainfall. The methods are illustrated using Texas rainfall data in 2003, which includes WAR-88D radar-reflectivity data and the corresponding rain gauge measurements. Simulation experiments are carried out to evaluate the accuracy of our methodology. The superiority of the proposed method lies in estimating total rainfall as well as point rainfall amount. We study the asymptotic joint distribution of sample space-time covariance esti-mators of stationary random fields. We do this without any marginal or joint distri-butional assumptions other than mild moment and mixing conditions. We consider several situations depending on whether the observations are regularly or irregularly spaced, and whether one part or the whole domain of interest is fixed or increasing. A simulation experiment illustrates the asymptotic joint normality and the asymp- totic covariance matrix of sample space-time covariance estimators as derived. An extension of this part develops a nonparametric test for full symmetry, separability, Taylor's hypothesis and isotropy of space-time covariances.en_US
dc.format.mediumelectronicen_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoen_USen_US
dc.subjectNexRad dataen_US
dc.subjectthresholden_US
dc.subjectlinear regressionen_US
dc.subjectvariogram estimationen_US
dc.subjectasymptotic normalityen_US
dc.subjectcovarianceen_US
dc.subjectincreasing domain asymptoticsen_US
dc.subjectrandom fielden_US
dc.titleAn analysis of Texas rainfall data and asymptotic properties of space-time covariance estimatorsen_US
dc.typeBooken
dc.typeThesisen
thesis.degree.departmentStatisticsen_US
thesis.degree.disciplineStatisticsen_US
thesis.degree.grantorTexas A&M Universityen_US
thesis.degree.nameDoctor of Philosophyen_US
thesis.degree.levelDoctoralen_US
dc.contributor.committeeMemberEriksson, Marianen_US
dc.contributor.committeeMemberMallick, Banien_US
dc.type.genreElectronic Dissertationen_US
dc.type.materialtexten_US
dc.format.digitalOriginborn digitalen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record