Accurate fog-dissipation forecasts at airports depend on observing the processes that control formation and clearing: near-surface moisture, boundary layer thermodynamics, wind shear and turbulence, and radiative exchange. Research on fog highlights the need for high-resolution vertical and temporal sampling to capture transitions between dense radiation fog and well-mixed conditions. Gokhan Gultepe Environment and Climate Change Canada emphasizes the role of combined surface, ceilometer and remote-sensing networks for characterizing fog microphysics and visibility evolution. David H. Lenschow National Center for Atmospheric Research stresses turbulence and shear measurements as critical for predicting mixing that erodes shallow fog layers.
Instrumentation and sampling strategies
Continuous high-frequency surface observations of visibility, temperature and dew point at runway level provide the operational baseline. Ceilometers deliver automated cloud-base and backscatter profiles that detect low-level stratus and fog thickness. Doppler lidar and wind profilers resolve vertical wind shear and small-scale turbulence that initiate mixing. Microwave radiometers estimate boundary-layer temperature and humidity profiles in the lowest few hundred meters where fog forms. Collecting these profiles at minute-scale cadence, rather than hourly, captures rapid dissipation events that affect landing minima.
Satellite imagery remains indispensable for synoptic context and for detecting advection fog approaching coastal airports. Geostationary satellites with high temporal resolution can show cooling patterns and low-cloud evolution, while spaceborne brightness temperature differences help identify cloud-phase changes. Ground-based GNSS integrated water vapor retrievals provide continuous moisture monitoring over an airport network and improve short-range forecasts when fused with local sensors.
Integration, modeling and operational relevance
Data assimilation of dense observations into high-resolution numerical models and nowcasting systems sharply improves forecast skill. Rapid-update mesoscale models and machine-learning nowcasts that ingest ceilometer, lidar and surface sensors produce more reliable dissipation timing and visibility trends than models run with sparse input. Accurate dissipation forecasts reduce unnecessary diversions and delays, lowering emissions from holding and reroutes and improving passenger safety. In coastal regions the cultural and economic dependency on air travel magnifies the local impact of misforecast fog; in polar or mountainous territories the same observations must be adapted for extreme radiative cooling or orographic trapping.
Investing in an integrated observing system that prioritizes vertical profiles, turbulence, and high-cadence visibility measurements yields the largest operational gains. Peer-reviewed syntheses of fog research led by Gokhan Gultepe Environment and Climate Change Canada and boundary-layer analyses by David H. Lenschow National Center for Atmospheric Research provide empirical support for this observational strategy.