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Multi-site doubly stochastic Poisson process models for fine-scale rainfall

Multi-site doubly stochastic Poisson process models for fine-scale rainfall

Ramesh, N.I., 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:
Last Modified: 14 Oct 2016 09:23
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/9476

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