Using Evolutionary Programs to Maximize Minimum Temperature Forecast Skill

AbstractEvolutionary program ensembles are developed and tested for minimum temperature forecasts at Chicago, Illinois, at forecast ranges of 36, 60, 84, 108, 132, and 156 h. For all forecast ranges examined, the evolutionary program ensemble outperforms the 21-member GFS model output statistics (MOS) ensemble when considering root-mean-square error and Brier skill score. The relative advantage in root-mean-square error widens with forecast range, from 0.18°F at 36 h to 1.53°F at 156 h while the probabilistic skill remains positive throughout. At all forecast ranges, probabilistic forecasts of abnormal conditions are particularly skillful compared to the raw GFS guidance.The evolutionary program reliance on particular forecast inputs is distinct from that obtained from considering multiple linear regression models, with less reliance on the GFS MOS temperature and more on alternative data such as upstream temperatures at the time of forecast issuance, time of year, and forecasts of wind speed, precipitati...

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