The scientific community that includes meteorologists, physical scientists, engineers, medical doctors, biologists, and environmentalists has shown interest in a better understanding of fog for years because of its effects on, directly or indirectly, the daily life of human beings. The total economic losses associated with the impact of the presence of fog on aviation, marine and land transportation can be comparable to those of tornadoes or, in some cases, winter storms and hurricanes. The number of articles including the word ''fog'' in Journals of American Meteorological Society alone was found to be about 4700, indicating that there is substantial interest in this subject. In spite of this extensive body of work, our ability to accurately forecast/nowcast fog remains limited due to our incomplete understanding of the fog processes over various time and space scales. Fog processes involve droplet microphysics, aerosol chemistry, radiation, turbulence, large/small-scale dynamics, and surface conditions (e.g., partaining to the presence of ice, snow, liquid, plants, and various types of soil). This review paper summarizes past achievements related to the understanding of fog formation, development and decay, and in this respect, the analysis of observations and the development of forecasting models and remote sensing methods are discussed in detail. Finally, future perspectives for fog-related research are highlighted.

The Impact of Arctic Aerosols on Clouds During one flight leg over the water on 4 April, large chunks of ice were seen floating in the Arctic Ocean after breaking up from the ice sheet along the coastline near Barrow, Alaska. Photo by Alexei Korolev.

The objective of this work is to suggest a new warm-fog visibility parameterization scheme for numerical weather prediction (NWP) models. In situ observations collected during the Radiation and Aerosol Cloud Experiment, representing boundary layer low-level clouds, were used to develop a parameterization scheme between visibility and a combined parameter as a function of both droplet number concentration N d and liquid water content (LWC). The current NWP models usually use relationships between extinction coefficient and LWC. A newly developed parameterization scheme for visibility, Vis ϭ f (LWC, N d ), is applied to the NOAA Nonhydrostatic Mesoscale Model. In this model, the microphysics of fog was adapted from the 1D Parameterized Fog (PAFOG) model and then was used in the lower 1.5 km of the atmosphere. Simulations for testing the new parameterization scheme are performed in a 50-km innermost-nested simulation domain using a horizontal grid spacing of 1 km centered on Zurich Unique Airport in Switzerland. The simulations over a 10-h time period showed that visibility differences between old and new parameterization schemes can be more than 50%. It is concluded that accurate visibility estimates require skillful LWC as well as N d estimates from forecasts. Therefore, the current models can significantly over-/ underestimate Vis (with more than 50% uncertainty) depending on environmental conditions. Inclusion of N d as a prognostic (or parameterized) variable in parameterizations would significantly improve the operational forecast models.

A field project that includes surface observations, remote sensing, and forecast models provides a better understanding of fog-induced low visibility and improves the parameterization of fog microphysics.

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Airborne observations from 14 flights in marine stratus over the Gulf of Maine and Bay of Fundy in August and September of 1993 are examined for the relationships among the cloud droplet number concentrations (Nd), the out‐of‐cloud aerosol particle number concentrations (Na), the major ion concentrations in the cloud water, and turbulence in cloud. There was a wide range of aerosol concentrations, but when low stratus and the main anthropogenic plume from eastern North America were in the same area the plume overrode the cloud. The Nd increased with increasing Na and cloud water sulfate concentration (cwSO4=), but the relationships were very weak. The separation of the data between smooth and lightly turbulent air substantially improved the ability to explain the variance in the Nd by either of these two quantities. Also, the relative increase in Nd for increases in Na and cwSO4= was greater for lightly turbulent air than for smooth air. The estimated minimum size of particles activated in these clouds ranged from 0.14 μm to 0.31 μm, corresponding to average supersaturations of <0.1%. The minimum size tended to be lower for lightly turbulent air and smaller Na. The results for lightly turbulent air agree well with previously reported parameterizations of the impact of aerosols on Nd, but the results for smooth air do not agree. In general, more knowledge of the physical factors controlling the Nd in stratiform clouds, such as turbulence, is needed to improve not only our ability to represent Nd but also to increase our understanding of the impact of the aerosol particles on the Nd and climate.

This review paper summarizes current knowledge available for aviation operations related to meteorology and provides suggestions for necessary improvements in the measurement and prediction of weather-related parameters, new physical methods for numerical weather predictions (NWP), and next-generation integrated systems. Severe weather can disrupt aviation operations on the ground or in-flight. The most important parameters related to aviation meteorology are wind and turbulence, fog visibility (Vis) and ceiling, rain and snow amount and rates, icing, ice microphysical parameters, convection and precipitation intensity, microbursts, hail, and lightning. Measurements of these parameters are also functions of sensor response times and measurement thresholds in extreme weather conditions. In addition to these, airport environments can play an important role leading to intensification of extreme weather conditions or high impact weather events, e.g., anthropogenic ice fog. To observe meteorological parameters, new remote sensing platforms, namely wind LIDAR, sodars, radars, and geostationary satellites, and in-situ observations at the surface and in the cloud, as well as aircraft and Unmanned Aerial Vehicles (UAV) mounted sensors, are becoming more common. Because of prediction issues at smaller time and space scales (e.g., <1 km), meteorological forecasts from NWP models need to be continuously improved. Aviation weather forecasts also need to be developed to provide information that represents both deterministic and statistical approaches. In this review, we present available resources and issues for aviation meteorology and evaluate them for required improvements related to measurements, nowcasting, forecasting, and climate change, and emphasize future challenges.

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