A Practical Model Blending Technique Based on Bayesian Model Averaging
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We have conducted several experiments to evaluate DABMA. We applied both DABMA and the original BMA technique to output from a stochastic ensemble and found the methods were comparable in terms of mean absolute error and statistical reliability. The DABMA weights and kernel widths, however, were more stable from day-to-day suggesting DABMA may be less prone to overfitting. To experiment with real data, we used DABMA to blend temperature forecasts from several deterministic MOS products. The DABMA-weighted mean was more accurate than the equally-weighted mean and the DABMA probabilistic forecasts were well-calibrated. We also applied DABMA to output from the Short Range Ensemble Forecast (SREF) System. Specifically, we created calibrated probabilistic forecasts of partial thicknesses and upper air temperatures to facilitate precipitation type forecasting. Based on these experiments, we believe the DABMA technique can be used to generate probabilistic forecast guidance by combining forecasts from a diverse set of numerical weather prediction systems (so called "model blending”).