The Potential for Mesoscale Visibility Predictions with a Multimodel Ensemble

Abstract

his work examines the viability of producing short-range (<20 h) probabilistic fog predictions in remote locations, absent an observational history, using an uncalibrated 4-km, 10-member Weather Research and Forecasting Model (WRF) ensemble configured to closely match the Air Force Weather Agency Mesoscale Ensemble Forecast Suite. Three distinct sources of error in the final predictions are considered separately to facilitate a better understanding of the total error and appropriate mitigation strategies. These include initial condition error, parameterization of subgrid-scale processes, and error in the visibility parameterization used to convert NWP model output variables to visibility. The raw WRF predictions are generally not skillful in valley and coastal regions, where they produce a shortage of light fog predictions with visibilities of 1?7 mi (1.6?11.3 km) in favor of excessive forecasts of zero cloud water, corresponding to no fog. Initial condition error and visibility parameterization error are shown to play a relatively minor role compared to error in the parameterization of subgrid-scale processes. This deficiency is caused by a negative relative humidity bias, which results from a warm overnight bias. A second-order source of error arises from an inconsistent delineation of fog and haze in the NWP model compared to the verifying observations. Results show that under most conditions it is necessary to deviate from the perfect-prog assumption, and to introduce some method of statistical postprocessing to obtain skillful visibility predictions from the ensemble.