Data assimilation for precipitation nowcasting using Bayesian inference

Remi Barillec, Dan Cornford

Research output: Contribution to journalArticle

Abstract

This work introduces a new variational Bayes data assimilation method for the stochastic estimation of precipitation dynamics using radar observations for short term probabilistic forecasting (nowcasting). A previously developed spatial rainfall model based on the decomposition of the observed precipitation field using a basis function expansion captures the precipitation intensity from radar images as a set of ‘rain cells’. The prior distributions for the basis function parameters are carefully chosen to have a conjugate structure for the precipitation field model to allow a novel variational Bayes method to be applied to estimate the posterior distributions in closed form, based on solving an optimisation problem, in a spirit similar to 3D VAR analysis, but seeking approximations to the posterior distribution rather than simply the most probable state. A hierarchical Kalman filter is used to estimate the advection field based on the assimilated precipitation fields at two times. The model is applied to tracking precipitation dynamics in a realistic setting, using UK Met Office radar data from both a summer convective event and a winter frontal event. The performance of the model is assessed both traditionally and using probabilistic measures of fit based on ROC curves. The model is shown to provide very good assimilation characteristics, and promising forecast skill. Improvements to the forecasting scheme are discussed
Original languageEnglish
Pages (from-to)1050-1065
Number of pages16
JournalAdvances in Water Resources
Volume32
Issue number7
DOIs
Publication statusPublished - Jul 2009

Fingerprint

nowcasting
data assimilation
radar
Kalman filter
precipitation intensity
advection
decomposition
rainfall
winter
summer
distribution

Bibliographical note

NOTICE: this is the author’s version of a work that was accepted for publication in Advances in Water Resources. 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 Barillec, Remi and Cornford, Dan (2009). Data assimilation for precipitation nowcasting using Bayesian inference. Advances in Water Resources, 32 (7), pp. 1050-1065. DOI 10.1016/j.advwatres.2008.09.004

Keywords

  • precipitation
  • nowcasting
  • data assimilation
  • variational bayes
  • radar

Cite this

Barillec, Remi ; Cornford, Dan. / Data assimilation for precipitation nowcasting using Bayesian inference. In: Advances in Water Resources. 2009 ; Vol. 32, No. 7. pp. 1050-1065.
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Data assimilation for precipitation nowcasting using Bayesian inference. / Barillec, Remi; Cornford, Dan.

In: Advances in Water Resources, Vol. 32, No. 7, 07.2009, p. 1050-1065.

Research output: Contribution to journalArticle

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