Forecast of Low Visibility and Fog from NCEP: Current Status and Efforts

Abstract

Based on the visibility analysis data during November 2009 through April 2010 over North America from the Aviation Digital Database Service (ADDS), the performance of low visibility/fog predictions from the current operational 12 km-NAM, 13 km-RUC and 32 km-WRF-NMM models at the National Centers for Environmental Prediction (NCEP) was evaluated. The evaluation shows that the performance of the low visibility/fog forecasts from these models is still poor in comparison to those of precipitation forecasts from the same models. In order to improve the skill of the low visibility/fog prediction, three efforts have been made at NCEP, including application of a rule-based fog detection scheme, extension of the NCEP Short Range Ensemble Forecast System (SREF) to fog ensemble probabilistic forecasts, and a combination of these two applications. How to apply these techniques in fog prediction is described and evaluated with the same visibility analysis data over the same period of time. The evaluation results demonstrate that using the multi-rule-based fog detection scheme significantly improves the fog forecast skill for all three models relative to visibility-diagnosed fog prediction, and with a combination of both rule-based fog detection and the ensemble technique, the performance skill of fog forecasting can be further raised.

A persistent wintertime fog episode at Lisbon airport (Portugal): performance of ECMWF and AROME models

“…These biases are larger for AROME than for ECMWF model. The low skill of NWP models in reproducing the fog life cycle has also been documented by other studies (Tudor, ; Zhou et al ., ; Steeneveld et al ., ). Additionally, both models reveal limitations in distinguishing between the development of low clouds (as in 6 January) and fog.…”

Characterization and Modeling of Fog in the Mexico Basin

“…Like most hydrometeorological phenomena, fog occurrence strongly varies with geographical location (Croft et al, 1997), and its observation and recording methods are still very dependent on direct human presence and perception (Lee et al, 2010). Despite the numerous field studies and recent advances on instrumentation and remote sensing technologies for fog observations, its numerical modeling and forecasting skills are still limited (Zhou et al, 2012). This is mainly due to the complex interactions that occur over various time and space scales among microphysical, thermodynamical and dynamical processes, associated to the great variability of the surfaceatmosphere interface and topographic effects (Gultepe et al, 2007).…”

Fog Simulations Based on Multi-Model System: A Feasibility Study

“…However, due to large computing resource requirements as well as the complicated formulation mechanisms, including the microphysics of droplets, aerosol chemistry, radiation, turbulence, dynamic processes of different scales and surface conditions (GULTEPE et al, 2007), the progress in numerical forecasting of fog is still slow. At present, fog has no direct operational guidance by any current NWP center and its prediction lags far behind forecasting of precipitation (ZHOU et al, 2011). Therefore, challenges remain for quantitative fog forecasting.…”

“…Considering the fact that we are in dire need of an objective and quantitative method for fog forecasting due to the rapid development of highway transportation in China, and because it is impossible to set up a sounding or tower observation like that at an airport along the highway, we need to explore some new ways to provide fog forecasts in a relative large domain. In addition, we need a method which offers better results than using a NWP model directly, because recent studies have revealed very poor performance of NWP models in fog forecasting (ZHOU et al, 2011). We noted that some other techniques have been developed using the output of an NWP model to diagnose fog or low clouds, such as rule-based fog forecasting (ZHOU and DU, 2010;BURROWS and TOTH, 2011), but these methods are for fog occurrence prediction, and do not predict fog intensity or visibility.…”