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Enforcing calibration in ensemble postprocessing
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DOI：10.1002/qj.3185.png)
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摘要
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Desirable attributes of probability forecasts are maximal sharpness, consistent with calibration (reliability'). The usual procedure of optimizing ensemble-postprocessing parameters by minimizing a proper scoring rule such as the continuous ranked probability score or the ignorance' (i.e. negative log-likelihood) does not guarantee the necessary calibration condition, potentially compromising the value of the resulting forecasts to users. The calibration condition can be enforced by including a miscalibration penalty in the loss function to be minimized in parameter estimation. The procedure is illustrated using ensemble forecasts for minimum temperatures and wind speeds, postprocessed using member-by-member algorithms.
Keyword:
sharpness
reliability
member-by-member postprocessing
CRPS
proper scores
Value Score
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