DOI: 10.1007/s10584-015-1430-2
Scopus记录号: 2-s2.0-84940725250
论文题名: Towards improving the framework for probabilistic forecast evaluation
作者: Smith L.A. ; Suckling E.B. ; Thompson E.L. ; Maynard T. ; Du H.
刊名: Climatic Change
ISSN: 0165-0009
EISSN: 1573-1480
出版年: 2015
卷: 132, 期: 1 起始页码: 31
结束页码: 45
语种: 英语
Scopus关键词: Climatology
; Dynamics
; Continuous ranked probability scores
; Evaluation measures
; Forecast performance
; Forecast systems
; Global-mean temperature
; Probabilistic forecasts
; Probabilistic Skill
; Similar physical simulations
; Forecasting
; climate change
; climatology
; decadal variation
; forecasting method
; performance assessment
; probability
; temperature profile
英文摘要: The evaluation of forecast performance plays a central role both in the interpretation and use of forecast systems and in their development. Different evaluation measures (scores) are available, often quantifying different characteristics of forecast performance. The properties of several proper scores for probabilistic forecast evaluation are contrasted and then used to interpret decadal probability hindcasts of global mean temperature. The Continuous Ranked Probability Score (CRPS), Proper Linear (PL) score, and IJ Good’s logarithmic score (also referred to as Ignorance) are compared; although information from all three may be useful, the logarithmic score has an immediate interpretation and is not insensitive to forecast busts. Neither CRPS nor PL is local; this is shown to produce counter intuitive evaluations by CRPS. Benchmark forecasts from empirical models like Dynamic Climatology place the scores in context. Comparing scores for forecast systems based on physical models (in this case HadCM3, from the CMIP5 decadal archive) against such benchmarks is more informative than internal comparison systems based on similar physical simulation models with each other. It is shown that a forecast system based on HadCM3 out performs Dynamic Climatology in decadal global mean temperature hindcasts; Dynamic Climatology previously outperformed a forecast system based upon HadGEM2 and reasons for these results are suggested. Forecasts of aggregate data (5-year means of global mean temperature) are, of course, narrower than forecasts of annual averages due to the suppression of variance; while the average “distance” between the forecasts and a target may be expected to decrease, little if any discernible improvement in probabilistic skill is achieved. © 2015, The Author(s). Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/84523
Appears in Collections: 气候减缓与适应 气候变化事实与影响
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作者单位: Centre for the Analysis of Time Series, London School of Economics, Houghton Street, London, United Kingdom; Department of Statistics, University of Chicago, Chicago, IL, United States; NCAS-Climate, Department of Meteorology, University of Reading, Reading, United Kingdom; Center for Robust Decision Making on Climate and Energy Policy, University of Chicago, 5735 South Ellis Avenue, Chicago, IL, United States
Recommended Citation:
Smith L.A.,Suckling E.B.,Thompson E.L.,et al. Towards improving the framework for probabilistic forecast evaluation[J]. Climatic Change,2015-01-01,132(1)