Monday, 8 January 2018: 10:30 AM
Room 19AB (ACC) (Austin, Texas)
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 windspeeds, postprocessed using member-by-member algorithms.
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