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Using the cluster point-process in modelling the hourly rainfall series

Yusof, Fadhilah and Mohd. Daud, Zalina and Yusof, Zulkifli (2006) Using the cluster point-process in modelling the hourly rainfall series. In: Recent Advances in Probability and Statistics. Penerbit UTM, Johor , pp. 141-160. ISBN 978-983-52-0612-2

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Abstract

A stochastic rainfall model has an important role in any hydrological analysis. This model is frequently used to generate a large number of rainfall events based upon the statistical characteristics of the historical rainfall records. It also provides some theoretical knowledge to the hydrologists in understanding the temporal rainfall structure. The models may be classified according to time intervals such as a daily rainfall model or an hourly rainfall model. Daily rainfall event has always used the Markov Chain model to represent it, while the hourly rainfall phenomena has always used the cluster processes [1]. A point process is a stochastic process whose realizations consist of point events in time or space. Point rainfall models based upon Poisson cluster processes were developed by [2,3]. These models are more appealing for rainfall time series simulation as they are able to preserve rainfall statistics over a range of time scales. As rain cells are known to exist in the actual rainfall events [4], the Poisson Cluster models are able to represent the existent of clusters of rain cells in their structures. The global scheme of the models may reproduce the hierarchical structure of the rainfall fields.

Item Type:Book Section
Subjects:Q Science
Divisions:Science
ID Code:25752
Deposited By: Liza Porijo
Deposited On:05 Jun 2012 04:22
Last Modified:05 Jun 2014 01:09

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