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Towards improving the framework for probabilistic forecast evaluation

Smith, Leonard A., Suckling, Emma B., Thompson, Erica L., Maynard, Trevor and Du, Hailiang (2015) Towards improving the framework for probabilistic forecast evaluation. Climatic Change, 132 (1). pp. 31-45. ISSN 0165-0009

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Identification Number: 10.1007/s10584-015-1430-2


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.

Item Type: Article
Official URL:
Additional Information: © 2015 The Authors
Divisions: Statistics
Centre for Analysis of Time Series
Subjects: G Geography. Anthropology. Recreation > GE Environmental Sciences
H Social Sciences > HA Statistics
Date Deposited: 05 Aug 2015 15:20
Last Modified: 20 Oct 2021 00:31
Projects: NE/H003479/1, 0951576, EP/K013661/1
Funders: Natural Environment Research Council, Economic and Social Research Council, Munich Re, National Science Foundation, Engineering and Physical Sciences Research Council

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