Probabilistic Visibility Forecasting Using Bayesian Model Averaging

Bayesianmodelaveraging(BMA)isastatisticalpostprocessingtechniquethathasbeenusedinprobabilistic weather forecasting to calibrate forecast ensembles and generate predictive probability density functions (PDFs)for weather quantities. The authors apply BMA to probabilistic visibility forecasting using a predictive PDFthatisamixtureofdiscretepointmassandbetadistributioncomponents.Threeapproachestodeveloping predictivePDFsforvisibilityaredeveloped,eachusingBMAtopostprocessanensembleofvisibilityforecasts. In the first approach, the ensemble is 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. In the third approach, the ensemble members are generated from relative humidity and precipitation alone. These methods areappliedto12-hensembleforecastsfrom2007to2008andaretestedagainstverifyingobservationsrecorded at Automated Surface Observing Stations in the Pacific Northwest. Each of the three methods produces predictive PDFs that are calibrated and sharp with respect to both climatology and the raw ensemble.