Space-time calibration of radar rainfall data

Patrick E. Brown, Peter J. Diggle, Martin E. Lord, Peter C. Young

Research output: Contribution to journalArticlepeer-review

Abstract

Motivated by a specific problem concerning the relationship between radar reflectance and rainfall intensity, the paper develops a space-time model for use in environmental monitoring applications. The model is cast as a high dimensional multivariate state space time series model, in which the cross-covariance structure is derived from the spatial context of the component series, in such a way that its interpretation is essentially independent of the particular set of spatial locations at which the data are recorded. We develop algorithms for estimating the parameters of the model by maximum likelihood, and for making spatial predictions of the radar calibration parameters by using realtime computations. We apply the model to data from a weather radar station in Lancashire, England, and demonstrate through empirical validation the predictive performance of the model.

Original languageEnglish (US)
Pages (from-to)221-241
Number of pages21
JournalJournal of the Royal Statistical Society. Series C: Applied Statistics
Volume50
Issue number2
StatePublished - 2001
Externally publishedYes

Keywords

  • Dynamic linear model
  • Environmental monitoring
  • Kalman filter

ASJC Scopus subject areas

  • Mathematics(all)
  • Statistics and Probability

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