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

  • N. I. RameshEmail author
  • R. Thayakaran
  • C. Onof
Original Paper

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.

Keywords

Doubly stochastic Poisson process Rainfall modelling Maximum likelihood Multi-site models Bucket tip-time series Fine-scale rainfall 

Notes

Acknowledgement

We thank an anonymous reviewer for useful comments that have greatly improved the manuscript.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.School of Computing and Mathematical Sciences, University of Greenwich, Maritime Greenwich CampusLondonUK
  2. 2.Department of Civil and Environmental EngineeringImperial College LondonLondonUK

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