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Stochastic point process model for fine-scale rainfall time series

Stochastic point process model for fine-scale rainfall time series

Ramesh, N. I. and Thayakaran, R. (2012) Stochastic point process model for fine-scale rainfall time series. In: Proceedings of the international conference on stochastic modelling techniques and data analysis. Electronic proceedings, pp. 635-642.

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Abstract

A stochastic point process model, which is constructed from a class of doubly stochastic Poisson processes, is proposed to analyse point rainfall time series observed in fine sub-hourly time scales. Under the framework of this stochastic model rain cells arrive according to a Poisson process whose arrival rate is governed by a finite-state Markov chain. Each cell of the point process has a random lifetime during which instantaneous random depths (pulses) of rainfall bursts occur as another Poisson process. The structure of this model enables us to study the variability of rainfall characteristics at small time intervals. The covariance structure of the pulse occurrence process is studied. Second-order properties of the time series of cumulative rainfall in discrete intervals are derived to model 5-minute rainfall data, over a period of 48 years, from Germany. The results show that the proposed model is capable of reproducing rainfall properties well at various sub-hourly resolutions.

Item Type: Conference Proceedings
Title of Proceedings: Proceedings of the international conference on stochastic modelling techniques and data analysis
Uncontrolled Keywords: Doubly Stochastic Poisson process, Fine-scale rainfall, Point process, Stochastic models, Rainfall pulse
Faculty / Department / Research Group: Faculty of Architecture, Computing & Humanities
Faculty of Architecture, Computing & Humanities > Department of Mathematical Sciences
Last Modified: 10 Nov 2017 13:05
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/17980

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