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

  • Ramesh N
  • Thayakaran R
  • Onof C
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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. © 2012 Springer-Verlag Berlin Heidelberg.

Author-supplied keywords

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

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  • N. I. Ramesh

  • R. Thayakaran

  • C. Onof

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