Advances in Earth Science ›› 2009, Vol. 24 ›› Issue (9): 981-989. doi: 10.11867/j.issn.1001-8166.2009.09.0981

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Review of Stochastic Simulation of Sub-daily Scale Precipitation

Yin Shuiqing 1,Xie Yun 2,Chen Deliang 3,Lin Xiaojuan 2,Li Weijing 1   

  1. 1.Laboratory for Climate Studies,Beijing Climate Center,China Meteorological Administration,Beijing 100081,China;
    2.School of Geography, Beijing Normal University,Beijing  100875,China;
    3.Department of Earth Sciences,University of Gothenburg, Gothenburg  40530,Sweden
  • Received:2009-04-02 Revised:2009-05-18 Online:2009-09-10 Published:2009-09-10
  • Contact: Shui qing Yin E-mail:yinsq@cma.gov.cn
  • Supported by:

    National Key Basic Research Special Foundation Project (2007CB407203);National Key Technology R&D Program

Yin Shuiqing,Xie Yun,Chen Deliang,Lin Xiaojuan,Li Weijing. Review of Stochastic Simulation of Sub-daily Scale Precipitation[J]. Advances in Earth Science, 2009, 24(9): 981-989.

       The sub-daily scale precipitation simulators are necessary for earth surface process models, for they can provide these models with more detailed rainfall data which are not widely available currently. In this review, focus was placed on two kinds of stochastic simulation techniques: Storm-based (Event-based) model and Poison cluster model. Firstly, criteria on how to define a storm and the development of storm-based simulation model were introduced. Secondly, several aspects of Bartlett-Lewis and Neyman-Scott poison cluster models were reviewed, including model structure, model fitting, parameter sensitivity analysis and model improvement. Then, comparison researches on stochastic models′ behavior were also summarized to provide some information on how to choose proper simulation model based on application purposes. Finally, it was pointed out that the prospects of stochastic simulation were as follows: integrating advantages of storm-based model and poison process model; developing weather-type conditioned simulation; and combining statistical and dynamical downscaling techniques.

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