A Bayesian state space modelling approach to probabilistic quantitative precipitation forecasting

Dan Cornford*

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

The generation of very short range forecasts of precipitation in the 0-6 h time window is traditionally referred to as nowcasting. Most existing nowcasting systems essentially extrapolate radar observations in some manner, however, very few systems account for the uncertainties involved. Thus deterministic forecast are produced, which have a limited use when decisions must be made, since they have no measure of confidence or spread of the forecast. This paper develops a Bayesian state space modelling framework for quantitative precipitation nowcasting which is probabilistic from conception. The model treats the observations (radar) as noisy realisations of the underlying true precipitation process, recognising that this process can never be completely known, and thus must be represented probabilistically. In the model presented here the dynamics of the precipitation are dominated by advection, so this is a probabilistic extrapolation forecast. The model is designed in such a way as to minimise the computational burden, while maintaining a full, joint representation of the probability density function of the precipitation process. The update and evolution equations avoid the need to sample, thus only one model needs be run as opposed to the more traditional ensemble route. It is shown that the model works well on both simulated and real data, but that further work is required before the model can be used operationally. © 2004 Elsevier B.V. All rights reserved.

Original languageEnglish
Pages (from-to)92-104
Number of pages13
JournalJournal of Hydrology
Volume288
Issue number1-2
DOIs
Publication statusPublished - 20 Mar 2004

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nowcasting
modeling
radar
probability density function
advection
uncertainty
forecast
sampling

Bibliographical note

NOTICE: this is the author’s version of a work that was accepted for publication in Journal of Hydrology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Cornford, Dan (2004). A Bayesian state space modelling approach to probabilistic quantitative precipitation forecasting. Journal of Hydrology, 288 (1-2), pp. 92-104. DOI 10.1016/j.jhydrol.2003.11.040

Keywords

  • bayesian
  • data assimilation
  • probabilistic
  • quantitative precipitation forecasting
  • state space models

Cite this

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title = "A Bayesian state space modelling approach to probabilistic quantitative precipitation forecasting",
abstract = "The generation of very short range forecasts of precipitation in the 0-6 h time window is traditionally referred to as nowcasting. Most existing nowcasting systems essentially extrapolate radar observations in some manner, however, very few systems account for the uncertainties involved. Thus deterministic forecast are produced, which have a limited use when decisions must be made, since they have no measure of confidence or spread of the forecast. This paper develops a Bayesian state space modelling framework for quantitative precipitation nowcasting which is probabilistic from conception. The model treats the observations (radar) as noisy realisations of the underlying true precipitation process, recognising that this process can never be completely known, and thus must be represented probabilistically. In the model presented here the dynamics of the precipitation are dominated by advection, so this is a probabilistic extrapolation forecast. The model is designed in such a way as to minimise the computational burden, while maintaining a full, joint representation of the probability density function of the precipitation process. The update and evolution equations avoid the need to sample, thus only one model needs be run as opposed to the more traditional ensemble route. It is shown that the model works well on both simulated and real data, but that further work is required before the model can be used operationally. {\circledC} 2004 Elsevier B.V. All rights reserved.",
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A Bayesian state space modelling approach to probabilistic quantitative precipitation forecasting. / Cornford, Dan.

In: Journal of Hydrology, Vol. 288, No. 1-2, 20.03.2004, p. 92-104.

Research output: Contribution to journalArticle

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