Statistical calibration and bridging of ECMWF System4 outputs for forecasting seasonal precipitation over China

Zhaoliang Peng, Q. J. Wang*, James C. Bennett, Andrew Schepen, Florian Pappenberger, Prafulla Pokhrel, Ziru Wang

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

33 Citations (Scopus)

Abstract

This study evaluates seasonal precipitation forecasts over China produced by statistically postprocessing multiple-output fields from the European Centre for Medium-Range Weather Forecasts' System4 (SYS4) coupled ocean-atmosphere general circulation model (CGCM). To ameliorate systematic deficiencies in the SYS4 precipitation forecasts, we apply a Bayesian joint probability (BJP) modeling approach to calibrate the raw forecasts. To improve the skill of the calibration forecasts, we use six large-scale climate indices, calculated from SYS4 sea surface temperature forecasts, to establish a set of BJP statistical bridging models to forecast precipitation. The calibration forecasts and bridging forecasts are merged through Bayesian model averaging to combine strengths of the different models. The BJP calibration effectively removes bias and improves statistical reliability of the raw forecasts. The calibration forecasts are skillful at a 0 month lead in most seasons, but skill decreases sharply at a 1 month lead. The skill of the bridging forecasts is more stable at different lead times. Consequently, the merged calibration and bridging forecasts at a 1 month lead are clearly more skillful than the calibration forecasts, and the skill is maintained out to a 4 month lead. The forecast framework used in this study can help to better realize the potential of CGCM ensemble forecasts. The increased reliability as well as improved skill of seasonal precipitation forecasts suggests that the system proposed here could be a useful operational forecasting tool. Key Points Evaluating postprocessed ECMWF forecasts of seasonal precipitation over China Calibration leads to reliable and unbiased forecasts Bridging significantly improves forecast skill at lead times of 1 to 4 months

Original languageEnglish
Pages (from-to)7116-7135
Number of pages20
JournalJournal of Geophysical Research: Atmospheres
Volume119
Issue number12
DOIs
Publication statusPublished - 27 Jun 2014

Keywords

  • Bayesian joint probability
  • Bayesian model averaging
  • ECMWF system 4
  • seasonal precipitation forecasts
  • statistical bridging
  • statistical calibration

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