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This special issue of the Journal of Pure and Applied Geophysics contains 15 papers related to fog, visibility, and low clouds that focus on microphysical and conventional surface observations, satellite detection techniques, modeling aspects, and climatological and statistical methods for fog forecasting. The results presented in this special issue come from research efforts in North America and Europe, mainly from the Canadian Fog Remote Sensing And Modeling (FRAM) and European COST-722 fog/visibility related projects. COST () is an intergovernmental European framework for international cooperation between nationally funded research activities. COST creates scientific networks and enables scientists to collaborate in a wide spectrum of activities in research and technology. COST activities are administered by the COST Office.

This special issue on fog, low clouds and visibility is dedicated to the memory of Professor Peter Zwack, who passed away prematurely on November 8, 2005, after a courageous bout with cancer.

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 objective of this work is to apply a new microphysical parameterization for fog visibility for potential use in numerical weather forecast simulations, and to compare the results with ground-based observations. The observations from the Fog Remote Sensing And Modeling (FRAM) field which took place during the winter of 2005–2006 over southern Ontario, Canada (Phase I) were used in the analysis. The liquid water content (), droplet number concentration (), and temperature () were obtained from the fog measuring device (FMD) spectra and Rosemount probe, correspondingly. The visibility () from a visibility meter, liquid water path from microwave radiometers (MWR), and inferred fog properties such as mean volume diameter, , and N were also used in the analysis. The results showed that is nonlinearly related to both and N. Comparisons between newly derived parameterizations and the ones already in use as a function of suggested that if models can predict the total N and at each time step using a detailed microphysics parameterization, can then be calculated for warm fog conditions. Using outputs from the Canadian Mesoscale Compressible Community (MC2) model, being tested with a new multi-moment bulk microphysical scheme, the new parameterization resulted in more accurate values where the correction reached up to 20–50%.

Automated detection of fog and low stratus in nighttime satellite data has been implemented on the basis of numerous satellite systems in past decades. Commonly, differences in small-droplet emissivities at 11μm and 3.9μm are utilized. With Meteosat SEVIRI, however, this method cannot be applied with a fixed threshold due to instrument design: The 3.9μm band is exceptionally wide and overlaps with the 4μm CO absorption band. Therefore, the emissivity difference varies with the length of the slant atmospheric column between sensor and object. To account for this effect, the new technique presented in this paper is based on the dynamical extraction of emissivity difference thresholds for different satellite viewing zenith angles. In this way, varying concentrations of CO and column depths are accounted for. The new scheme is exemplified in a plausibility study and shown to provide reliable results.

A nighttime image product that depicts areas of the lowest cloud base heights has been developed by combining brightness temperature data from the Geostationary Operational Environmental Satellite (GOES) Imager InfraRed (IR) bands centered at 3.9 μm and 10.7 μm, with hourly shelter temperatures from surface observing sites and offshore marine buoys. A dependent data sample showed a good correlation between the surface temperature minus IR cloud top temperature differences versus measured cloud base heights. Histogram analysis indicated that a temperature difference of less than 4-C related to a >50% frequency of ceilings below 1000 ft above ground level, the threshold for Instrument Flight Rules (IFR). Using this result as a model, an experimental Low Cloud Base image product was developed that highlights regions of likely IFR ceilings. Validation of the Low Cloud Base product for two separate periods resulted in Probabilities of Detection of 67% and 72% and False Alarm Rates of 6% and 11%, respectively. Some regional variation observed could be related to the relative frequency of multilavered overcast conditions. The biggest factor leading to underdetection of IFR ceilings by the GOES Low Cloud Base product is the presence of overlying clouds, including thin cirrus contamination. The GOES Low Cloud Base product shows potential for use as guidance for aviation meteorologists over both continental and marine areas.

The Cloud Type product, developed by the Satellite Application Facility to support to nowcasting and very short-range forecasting (SAFNWC) of EUMETSAT and based on Météosat-8/SEVIRI, identifies cloud categories, and especially low and very low clouds which are first estimates of areas where fog is likely to occur. This cloud type is combined with precipitation information from radar data and with hourly diagnostic analyses of 2-metre relative humidity and 10-metre wind to elaborate an hourly analysis of fog probability. This analysis provides four levels of fog probability with a 3-kilometre horizontal resolution: No risk, low-level risk, medium-level risk and high-level risk. An evaluation of such fog probability analyses versus a one-year set of French hourly SYNOP reports shows encouraging results (potential of detection = 0.73 for low and medium and high-level risks), even if false alarm ratios remain high. Most of the non-detections occur at twilight and are due to satellite non-detections. Eventually, we show case studies that clearly illustrate the high potential of the method.

Numerical experiments are performed with a comprehensive one-dimensional boundary layer/fog model to assess the impact of vertical resolution on explicit model forecasts of an observed fog layer. Two simulations were performed, one using a very high resolution and another with a vertical grid typical of current high-resolution mesoscale models. Both simulations were initialized with the same profiles, derived from observations from a fog field experiment. Significant differences in the onset and evolution of fog were found. The results obtained with the high-resolution simulation are in overall better agreement with available observations. The cooling rate before the appearance of fog is better represented, while the evolution of the liquid water content within the fog layer is more realistic. Fog formation is delayed in the low resolution simulation, and the water content in the fog layer shows large-amplitude oscillations. These results show that the numerical representation of key thermo-dynamical processes occurring in fog layers is significantly altered by the use of a grid with reduced vertical resolution.

A probabilistic fog forecast system was designed based on two high resolution numerical 1-D models called COBEL and PAFOG. The 1-D models are coupled to several 3-D numerical weather prediction models and thus are able to consider the effects of advection. To deal with the large uncertainty inherent to fog forecasts, a whole ensemble of 1-D runs is computed using the two different numerical models and a set of different initial conditions in combination with distinct boundary conditions. Initial conditions are obtained from variational data assimilation, which optimally combines observations with a first guess taken from operational 3-D models. The design of, the ensemble scheme computes members that should fairly well represent the uncertainty of the current meteorological regime. Verification for an entire fog season reveals the importance of advection in complex terrain. The skill of 1-D fog forecasts is significantly improved if advection is considered. Thus the probabilistic forecast system has the potential to support the forecaster and therefore to provide more accurate fog forecasts.

Short-term forecasting of fog is a difficult issue which can have a large societal impact. Fog appears in the surface boundary layer and is driven by the interactions between land surface and the lower layers of the atmosphere. These interactions are still not well parameterized in current operational NWP models, and a new methodology based on local observations, an adaptive assimilation scheme and a local numerical model is tested. The proposed numerical forecast method of foggy conditions has been run during three years at Paris-CdG international airport. This test over a long-time period allows an in-depth evaluation of the forecast quality. This study demonstrates that detailed 1-D models, including detailed physical parameterizations and high vertical resolution, can reasonably represent the major features of the life cycle of fog (onset, development and dissipation) up to +6h. The error on the forecast onset and burn-off time is typically 1 h. The major weakness of the methodology is related to the evolution of low clouds (stratus lowering). Even if the occurrence of fog is well forecasted, the value of the horizontal visibility is only crudely forecasted. Improvements in the microphysical parameterization and in the translation algorithm converting NWP prognostic variables into a corresponding horizontal visibility seems necessary to accurately forecast the value of the visibility.