Probabilistic visibility forecasting using Bayesian model averaging

Bayesian Model Averaging (BMA) is a statistical postprocessing technique that has been used in probabilistic weather forecasting to calibrate forecast ensembles and generate predictive probability density functions (PDFs) for weather quantities. We apply BMA to probabilistic visibility forecasting using a predictive PDF that is a mixture of discrete point mass and beta distribution components. Three approaches to developing predictive PDFs for visibility are considered. The first approach is an application of BMA to ensemble visibility forecasts generated by a translation algorithm that converts predicted concentrations of hydrometeorological variables into visibility. The second approach augments the raw ensemble visibility forecasts with model forecasts of relative humidity and quantitative precipitation. A third approach generates deterministic forecasts from relative humidity and precipitation alone and subsequently produces predictive PDFs using BMA. These methods are applied to 12-h ensemble forecasts from 2007-2008 and are tested against verifying observations recorded at Automated Surface Observing Stations in the Pacic Northwest. Each of the three methods produces predictive PDFs that are calibrated and sharp with respect to both climatology and the raw ensemble.