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dc.contributor.authorKriebel, Patrick James
dc.date.accessioned2017-03-30T04:30:30Z
dc.date.available2017-03-30T04:30:30Z
dc.date.issued2016-08
dc.identifier.otherkriebel_patrick_j_201608_ms
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/kriebel_patrick_j_201608_ms
dc.identifier.urihttp://hdl.handle.net/10724/36795
dc.description.abstractA novel approach to modeling the distribution of precipitation volume is developed using a combination of traditional and new techniques in spatial statistics. Data are taken from the Community Collaborative Rain, Hail and Snow (CoCoRaHS) network; this network of trained volunteers provides daily precipitation depth measurements across the country. Data for three regions in Colorado were selected due to its spatial density. Combined variogram clouds were calculated for each region, and variograms were fitted to this data using weighted least squares. Precipitation depths were estimated using ordinary Kriging, and bilinear interpolation was used to approximate daily precipitation volumes. Distributions were fitted to the seasonal volume estimates using maximum likelihood, and fit comparisons were done using negative log-likelihood and the Anderson-Darling test.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectBilinear Interpolation
dc.subjectCoCoRaHS
dc.subjectDistribution fitting
dc.subjectKriging
dc.subjectPrecipitation
dc.subjectVariogram
dc.subjectVolume
dc.titleEstimating precipitation volume distributions using data from the spatially dense cocorahs network
dc.typeThesis
dc.description.degreeMS
dc.description.departmentStatistics
dc.description.majorStatistics
dc.description.advisorLynne Seymour
dc.description.committeeLynne Seymour
dc.description.committeeJaxk Reeves
dc.description.committeeThomas Mote


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