DOI: 10.1175/JCLI-D-15-0196.1
Scopus记录号: 2-s2.0-84957837536
论文题名: A Bayesian framework for verification and recalibration of ensemble forecasts: How uncertain is NAO predictability?
作者: Siegert S. ; Stephenson D.B. ; Sansom P.G. ; Scaife A.A. ; Eade R. ; Arribas A.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2016
卷: 29, 期: 3 起始页码: 995
结束页码: 1012
语种: 英语
Scopus关键词: Atmospheric pressure
; Bayesian networks
; Climatology
; Signal to noise ratio
; Bayesian methods
; Climate prediction
; Ensembles
; Probability forecasts/models/distribution
; Seasonal forecasting
; Statistical techniques
; Forecasting
; Bayesian analysis
; calibration
; climate prediction
; ensemble forecasting
; North Atlantic Oscillation
; numerical model
; probability
英文摘要: Predictability estimates of ensemble prediction systems are uncertain because of limited numbers of past forecasts and observations. To account for such uncertainty, this paper proposes a Bayesian inferential framework that provides a simple 6-parameter representation of ensemble forecasting systems and the corresponding observations. The framework is probabilistic and thus allows for quantifying uncertainty in predictability measures, such as correlation skill and signal-to-noise ratios. It also provides a natural way to produce recalibrated probabilistic predictions from uncalibrated ensembles forecasts. The framework is used to address important questions concerning the skill of winter hindcasts of the North Atlantic Oscillation for 1992-2011 issued by the Met Office Global Seasonal Forecast System, version 5 (GloSea5), climate prediction system. Although there is much uncertainty in the correlation between ensemble mean and observations, there is strong evidence of skill: The 95% credible interval of the correlation coefficient of [0.19, 0.68] does not overlap zero. There is also strong evidence that the forecasts are not exchangeable with the observations: With over 99% certainty, the signal-to-noise ratio of the forecasts is smaller than the signal-to-noise ratio of the observations, which suggests that raw forecasts should not be taken as representative scenarios of the observations. Forecast recalibration is thus required, which can be coherently addressed within the proposed framework. © 2016 American Meteorological Society. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/50150
Appears in Collections: 气候变化事实与影响
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作者单位: University of Exeter, Exeter, United Kingdom; Met Office Hadley Centre, Exeter, United Kingdom
Recommended Citation:
Siegert S.,Stephenson D.B.,Sansom P.G.,et al. A Bayesian framework for verification and recalibration of ensemble forecasts: How uncertain is NAO predictability?[J]. Journal of Climate,2016-01-01,29(3)