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Rainfall modelling using Poisson-cluster processes: a review of developments

  • C. Onof
  • R. E. Chandler
  • A. Kakou
  • P. Northrop
  • H. S. Wheater
  • V. Isham

Abstract.

 Over a decade ago, point rainfall models based upon Poisson cluster processes were developed by Rodriguez-Iturbe, Cox and Isham. Two types of point process models were envisaged: the Bartlett–Lewis and the Neyman–Scott rectangular pulse models. Recent developments are reviewed here, including a number of empirical studies. The parameter estimation problem is addressed for both types of Poisson-cluster based models. The multiplicity of parameters which can be obtained for a given data set using the method of moments is illustrated and two approaches to finding a best set of parameters are presented. The use of a proper fitting method will allow for the problems encountered in regionalisation to be adequately dealt with. Applications of the point process model to flood design are discussed and finally, results for a model with dependent cell depth and duration are given. Taking into account the spatial features of rainfall, three multi-site models are presented and compared. They are all governed by a master Poisson process of storm origins and have a number of cell origins associated with each storm origin. The three models differ as to the type of dependence structure between the cell characteristics at different sites. Analytical properties are presented for these models and their ability to represent the spatial structure of a set of raingauge data in the South-West of England is examined. Continuous spatial-temporal models are currently being developed and results are presented for a model in which storm centres arrive in a homogeneous Poisson process in space-time, and cells follow them in time according to a Bartlett–Lewis type cluster. Examples of simulations using this model are shown and compared with radar data from the South-West of England. The paper concludes with a summary of the main areas in which further research is required.

Keywords

Radar Poisson Process Radar Data Dependent Cell Parameter Estimation Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • C. Onof
    • 1
  • R. E. Chandler
    • 2
  • A. Kakou
    • 3
  • P. Northrop
    • 4
  • H. S. Wheater
    • 1
  • V. Isham
    • 2
  1. 1.Department of Civil and Environmental Engineering, Imperial College of Science, Technology and Medicine, London SW7 2BU, UKGB
  2. 2.Department of Statistical Science, University College, London, UKGB
  3. 3.Department of Statistics, London School of Economics, London, UKGB
  4. 4.Department of Statistics, Oxford University, Oxford, UKGB

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