Ramesh, N.I. ORCID: 0000-0001-6373-2557, Thayakaran, R. and Onof, C. (2012) Multi-site doubly stochastic Poisson process models for fine-scale rainfall. Stochastic Environmental Research and Risk Assessment, 27 (6). pp. 1383-1396. ISSN 1436-3240 (Print), 1436-3259 (Online) (doi:https://doi.org/10.1007/s00477-012-0674-x)

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Abstract

We consider a class of doubly stochastic Poisson process models in the modelling of fine-scale rainfall at multiple gauges in a dense network. Multi-site stochastic point process models are constructed and their likelihood functions are derived. The application of this class of multi-site models, a useful alternative to the widely-known Poisson cluster models, is explored to make inferences about the properties of fine time-scale rainfall. The proposed models, which incorporate covariate information about the catchment area, are used to analyse tipping- bucket raingauge data from multiple sites. The results show the potential of this class of models to reproduce temporal and spatial variability of fine time-scale rainfall characteristics.

Item Type:	Article
Additional Information:	[1] Published online: 15 December 2012.
Uncontrolled Keywords:	doubly stochastic Poisson process, rainfall modelling, maximum likelihood, multi-site models, bucket tip-time series, fine-scale rainfall,
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences Q Science > Q Science (General)
Pre-2014 Departments:	School of Computing & Mathematical Sciences
Related URLs:	Publisher Organisation
Last Modified:	27 Oct 2020 14:50
URI:	http://gala.gre.ac.uk/id/eprint/9476

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