Multi‐objective calibration of forecast ensembles using Bayesian model averaging

Bayesian Model Averaging (BMA) has recently been proposed as a method for statistical postprocessing of forecast ensembles from numerical weather prediction models. The BMA predictive probability density function (PDF) of any weather quantity of interest is a weighted average of PDFs centered on the bias‐corrected forecasts from a set of different models. However, current applications of BMA calibrate the forecast specific PDFs by optimizing a single measure of predictive skill. Here we propose a multi‐criteria formulation for postprocessing of forecast ensembles. Our multi‐criteria framework implements different diagnostic measures to reflect different but complementary metrics of forecast skill, and uses a numerical algorithm to solve for the Pareto set of parameters that have consistently good performance across multiple performance metrics. Two illustrative case studies using 48‐hour ensemble data of surface temperature and sea level pressure, and multi‐model seasonal forecasts of temperature, show that a multi‐criteria formulation provides a more appealing basis for selecting the appropriate BMA model.