A stochastic model for daily climate

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dc.contributor.advisorZucchini, Walteren_ZA
dc.contributor.authorBrandão, Anabela de Gusmãoen_ZA
dc.date.accessioned2016-01-12T11:19:30Z
dc.date.available2016-01-12T11:19:30Z
dc.date.issued1986en_ZA
dc.descriptionIncludes bibliography.en_ZA
dc.description.abstractThis thesis describes the results of a study to establish whether climate variables could be usefully modelled on a daily basis. Three stochastic models are considered for the description of daily climate sequences, which can then be used to generate artificial sequences. The climate variables under consideration are rainfall, maximum and minimum temperature, evaporation, sunshine duration, windrun and maximum and minimum humidity. A simple Markov chain-Weibull model is proposed to model rainfall. Three multivariate models (one proposed by Richardson (1981), two new) are suggested for modelling the remaining climate variables. The model parameters are allowed to vary seasonally, while the error term is assumed to follow an autoregressive process. The models were validated and their general performance·was found to be satisfactory. Some weaknesses were identified and are discussed. The. main conclusion of this study is that daily climate sequences can indeed be usefully described by means of stochastic models.en_ZA
dc.identifier.apacitationBrandão, A. d. G. (1986). <i>A stochastic model for daily climate</i>. (Thesis). University of Cape Town ,Faculty of Science ,Department of Mathematics and Applied Mathematics. Retrieved from http://hdl.handle.net/11427/16348en_ZA
dc.identifier.chicagocitationBrandão, Anabela de Gusmão. <i>"A stochastic model for daily climate."</i> Thesis., University of Cape Town ,Faculty of Science ,Department of Mathematics and Applied Mathematics, 1986. http://hdl.handle.net/11427/16348en_ZA
dc.identifier.citationBrandão, A. 1986. A stochastic model for daily climate. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Brandão, Anabela de Gusmão AB - This thesis describes the results of a study to establish whether climate variables could be usefully modelled on a daily basis. Three stochastic models are considered for the description of daily climate sequences, which can then be used to generate artificial sequences. The climate variables under consideration are rainfall, maximum and minimum temperature, evaporation, sunshine duration, windrun and maximum and minimum humidity. A simple Markov chain-Weibull model is proposed to model rainfall. Three multivariate models (one proposed by Richardson (1981), two new) are suggested for modelling the remaining climate variables. The model parameters are allowed to vary seasonally, while the error term is assumed to follow an autoregressive process. The models were validated and their general performance·was found to be satisfactory. Some weaknesses were identified and are discussed. The. main conclusion of this study is that daily climate sequences can indeed be usefully described by means of stochastic models. DA - 1986 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 1986 T1 - A stochastic model for daily climate TI - A stochastic model for daily climate UR - http://hdl.handle.net/11427/16348 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/16348
dc.identifier.vancouvercitationBrandão AdG. A stochastic model for daily climate. [Thesis]. University of Cape Town ,Faculty of Science ,Department of Mathematics and Applied Mathematics, 1986 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/16348en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Mathematics and Applied Mathematicsen_ZA
dc.publisher.facultyFaculty of Scienceen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherMathematical Statisticsen_ZA
dc.titleA stochastic model for daily climateen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMScen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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