Fog though a rare event has adverse economic implications to both the airline and aviation service providers if it’s occurrence, duration and dissipation periods are not properly predicted. This work assesses the accuracy and skill in forecasting fog events and suggesting possible adjustments to improve forecast accuracy and skill. The forecast used in this study are produced by MeteoGroup using Model Output Statistics (MOS). Forecasts for Heathrow, Northolt and Kenley are considered for analysis. These forecasts are used by British Airports Authority (BAA) in planning airport operations. The forecasts are produced daily at 08:00 UTC with a validity of 24 hours. Hourly Meteorological Aerodrome Reports (METARs) are used to verify the forecast. The forecast accuracy and skill is determined using Hit Rate (HR), False Alarm Ratio (FAR), Frequency Bias (FBI), and Critical Success Index (CSI) evaluated from a 2-category contingency table. Significance of the forecast error is evaluated using a student’s t-test for difference in means at 0.05 significance level. The HR and CSI for the original forecast for all the three stations, Heathrow, Northolt and Kenley was below 20%. Upon adjusting the forecast using regression analysis, the HR and CSI for Heathrow improved to 53.7% and 40.8% respectively. The HR and CSI for Northolt improved to 27.4% and 24.2% respectively. The improvement for Kenley was insignificant since the HR and CSI slightly improved to values below 10%. Although this method is purely statistical hence do not involve physical dynamics that dictate fog formation, maintenance and dissipation, in absence of a dynamical methods that can be applied to improve the accuracy and skill of visibility forecast within fog range then regression methods used in this study is a better option especially for Heathrow airport.
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