Ein stochastisches Modell zur Beschreibung stündlichen Niederschlages

Ohlmer, Ulrike

Ein Beitrag zur stadtökologischen Forschung

dc.contributor.advisor	Zucchini, Walter Prof. Dr.	de
dc.contributor.author	Ohlmer, Ulrike	de
dc.date.accessioned	2013-12-10T16:11:04Z
dc.date.available	2013-12-10T16:11:04Z
dc.date.issued	1999	de
dc.identifier.uri	http://hdl.handle.net/11858/00-1735-0000-0022-5D66-4
dc.identifier.uri	http://dx.doi.org/10.53846/goediss-4253
dc.language.iso	ger	de
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/
dc.title	Ein stochastisches Modell zur Beschreibung stündlichen Niederschlages	de
dc.title.alternative	Ein Beitrag zur stadtökologischen Forschung	de
dc.type	doctoralThesis	de
dc.contributor.referee	Böker, Fred Prof. Dr.	de
dc.date.examination	1998-12-11	de
dc.subject.gok	LC 037 Wahrscheinlichkeits-Metrik {Wirtschafts- und Sozialstatistik, Ökonometrie} (PPN9621580171)	de
dc.description.abstracteng	A Neyman-Scott cluster process model for hourly precipitation is developed that accounts for seasonality by the model parameters being continuous sinusoidal functions of time. The process is fitted to a set of 27 years of hourly rainfall from Stuttgart, Germany. We give a detailed description of the fitting procedure which is based on a combination of the method of moments with a least squares approach. As part of the validation check we compare the second-order moments under the model with their empirical counterparts. These moments are mean, variance and lag(1)-autocorrelation of the series of hourly rainfall depths at several time scales, the probability of an interval being dry and the transition probabilities of wet intervals. Further checks are carried out on simulated time series which include the distribution of run length and of annual maximum rainfalls. The model turns out to give a good description of various features of hourly precipitation.	de
dc.identifier.urn	urn:nbn:de:gbv:7-webdoc-828-4	de
dc.identifier.purl	webdoc-828	de
dc.affiliation.institute	Fakultät für Wirtschaftswissenschaften	de
dc.identifier.ppn	269188851	de

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