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Generating high resolution precipitation conditional on rainfall observations and satellite data

dc.contributor.advisorFriederichs, Petra
dc.contributor.authorPscheidt, Ieda
dc.date.accessioned2020-04-24T08:04:46Z
dc.date.available2020-04-24T08:04:46Z
dc.date.issued15.09.2017
dc.identifier.urihttp://hdl.handle.net/20.500.11811/7240
dc.description.abstractThis study is part of the high resolution reanalysis project proposed for Germany and Europe (Bollmeyer et. al., 2014) within the framework of the Hans Ertel Centre for Weather Research (HErZ). The reanalysis for Germany assimilates among other variables high resolution rainfall rates. For the most recent years, radar data is assimilated, however, for periods before 2007 this data is not available and another radar-like dataset is required. This study proposes the method HIRAIN to generate an ensemble of probable space-time precipitation fields given a set of observational data. HIRAIN works in two steps. First, a Bayesian statistical model conditional on observations from synoptic stations and on satellite information simulates the latent spatial Gaussian process that drives the occurrence of precipitation exceeding a selected threshold. In a second step, realisations of occurrence/non-occurrence of precipitation exceeding the same thresholds are obtained given the simulated latent process. The occurrence/non-occurrence of precipitation is generated through two different methodologies. HIRAIN is extended to several thresholds of precipitation amount and the final precipitation product is generated from the fields occurrence/non-occurrence of the individual thresholds. A Bayesian approach is used in HIRAIN to provide more realistic fields than those produced by interpolation methods. In the Bayesian approach the data at the observation locations are honored and the spatial covariance structure of the spatial process is reproduced in each realisation. Moreover, the ability to generate ensemble of possible precipitation patterns provides valuable information of precipitation uncertainties that plays also an important role in ensemble reanalysis. HIRAIN produces precipitation dataset with hourly and 4 km resolution. This product presents a more appropriate resolution for the purposes of the reanalysis than the rainfall datasets available by the time the Germany reanalysis project started.
dc.language.isoeng
dc.relation.ispartofseriesBonner Meteorologische Abhandlungen ; 79
dc.rightsIn Copyright
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectNiederschlag
dc.subjectHochauflösung
dc.subjectBayesisches Modell
dc.subjectSimulation
dc.subjectDatengenerierung
dc.subjectprecipitation
dc.subjecthigh resolution
dc.subjectBayesian model
dc.subjectconditional simulation
dc.subjectdata generation
dc.subject.ddc310 Allgemeine Statistiken
dc.subject.ddc550 Geowissenschaften
dc.titleGenerating high resolution precipitation conditional on rainfall observations and satellite data
dc.typeDissertation oder Habilitation
dc.publisher.nameUniversitäts- und Landesbibliothek Bonn
dc.publisher.locationBonn
dc.rights.accessRightsopenAccess
dc.identifier.urnhttps://nbn-resolving.org/urn:nbn:de:hbz:5n-48088
ulbbn.pubtypeErstveröffentlichung
ulbbnediss.affiliation.nameRheinische Friedrich-Wilhelms-Universität Bonn
ulbbnediss.affiliation.locationBonn
ulbbnediss.thesis.levelDissertation
ulbbnediss.dissID4808
ulbbnediss.date.accepted2017-07-06
ulbbnediss.instituteMathematisch-Naturwissenschaftliche Fakultät : Fachgruppe Erdwissenschaften / Meteorologisches Institut
ulbbnediss.fakultaetMathematisch-Naturwissenschaftliche Fakultät
dc.contributor.coRefereeHense, Andreas


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