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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. [ABSTRACT FROM AUTHOR]