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A Markov modulated Poisson process model for rainfall increments.

C Onof1, B Yameundjeu, J P Paoli

  • 1Department of Civil and Environmental Engineering, Imperial College, London, UK.

Water Science and Technology : a Journal of the International Association on Water Pollution Research
|March 13, 2002
PubMed
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This study models rainfall using continuous-time Markov chains to capture fine temporal patterns from tipping-bucket gauges, improving rainfall intensity and wet/dry sequence representation at 5-minute scales.

Area of Science:

  • Hydrology and Environmental Science
  • Statistical Modeling

Background:

  • Traditional hierarchical point process models struggle with fine temporal resolution in rainfall data.
  • Tipping-bucket rain gauge data is increasingly abundant, necessitating direct modeling approaches.

Purpose of the Study:

  • To develop a direct modeling approach for rainfall increments from tipping-bucket gauges.
  • To represent fine temporal structures in rainfall intensity and wet/dry sequences.

Main Methods:

  • Utilizing Poisson processes with an arrival rate modulated by a continuous-time Markov chain.
  • Employing two or three states within the Markov chain for model flexibility.

Main Results:

  • The proposed model effectively captures rainfall intensity distribution structure at 5-minute intervals.

Related Experiment Videos

  • Wet/dry sequences in rainfall data are accurately represented using the Markov chain approach.
  • The model overcomes limitations of traditional methods for high-resolution rainfall analysis.
  • Conclusions:

    • Direct modeling of rainfall increments with Markov-modulated Poisson processes is a viable alternative.
    • This approach offers improved representation of rainfall characteristics at fine temporal scales.
    • The findings are crucial for hydrological studies requiring accurate, high-resolution rainfall data.