1. Introduction
Fogs consist of a large amount of small liquid water droplets or ice crystals suspended in a certain air volume near the ground. The physical nature of fogs and clouds is the same. One can state that fog is a cloud touching the ground’s surface. Fogs reduce the visibility in the surrounding area—according to its meteorological definition [
1], it is a state of the atmospheric air with visibility lower than 1 km, as measured near the surface.
Besides the hydrosphere, fogs present a form of liquid water in the atmosphere of the Earth. Generally, fog formation and existence are under the strong influence of local orographic factors, the actual synoptic situation, and the atmospheric circulations. It can be formed everywhere—over land and water surfaces. Although it is not a climate-forming factor, it is of great meteorological importance, particularly due to its local character of formation, its capability of reducing the temperature amplitude, and the fact that it is directly related to the humidity parameters.
Natural fog, as a form of condensed water existing in the atmosphere, has significant impacts on many components of the environment, such as the global and regional climate, the atmosphere’s thermal and radiative budget, air quality, waters, flora and fauna, air-surface interactions, etc. [
2,
3,
4]. At the same time, as resulting in reduced visibility, fogs can perturb and affect severely the societal life and functionality (e.g., air-, surface-, and water transport) causing an impressive number of injures and fatalities [
5]. Depending on the physical and chemical nature and composition of the droplets, fogs can also have direct or indirect adverse effects on human health (respiratory and radiation diseases, skin and eye damages, secondary health effects, etc.) [
6,
7].
Because of the various and some even severe influences of fogs on the environment and human activities and health, fog research and studies have a long and rich history. In a comprehensive review paper, Gultepe et al. [
5] made a profound analysis of the experimental and theoretical contributions and achievements of fog-related research, including a detailed historical overview of the subject and highlighting problems of fog modeling and forecasting. Nowadays, scientific reports devoted to measurement, characterization and applications of fogs continue to appear in large numbers (e.g., [
4,
8,
9]).
The present review considers important aspects of natural fogs. It is not aimed to survey comprehensively the fog-related scientific literature. Instead, the main purpose of the work is to summarize recent (mostly published during the last decade and a half) research contributions and achievements concerning selected aspects of the formation, collection, characterization, classification, and impacts of natural fogs.
The contents of this publication are structured as follows: in
Section 2, basic characteristics of water and fog are discussed, in particular, specifics of the nucleation and condensation processes. Fog’s formation, conditioning, and classification are explored in
Section 3. The important effect of water vapor’s partial pressure on the surface tension of the liquid water–air interface is handled in
Section 4.
Section 5 is devoted to fog modeling, parameterization, and forecasting. Fog’s impact on the environment, human health, and societal activities is analyzed in
Section 6. Basic conclusions of the work are summarized in
Section 7.
5. Fog Modeling and Forecasting
The processes of globalization, urbanization, and intensification of the economical and all other human activities, typical for the modern societal life, lead to considerable worldwide expansion of land-, water-, and air transportation. Due to the severe impacts of fogs on all types of transportation, the significance of fog/mist forecasting increases correspondingly. In order to provide reliable fog forecasting, adequate modeling of the processes of formation, presence, evolution, and dissipation/deposition of fogs becomes indispensable. As far as the distributions of water vapor and hydrometeors are highly variable in space and time, the precise analysis and forecasting are still challenging tasks [
43]. In addition, few fog monitoring sites exist [
44,
45]. Therefore, studying fogs on a spatial scale requires numerical modeling and simulations. A wide variety of theoretical models have been reported, concerning different aspects of fog physics, microphysics, chemistry, dynamics, etc., ranging from simple local 1D-models [
46,
47] to large-scale complex 3D-models [
48]. Mesoscale meteorological models have also been applied to regional forecasting of fog events [
49,
50,
51], including the fifth-generation mesoscale model (MM5) [
52].
The visibility
is one of the basic characteristics directly related to the effects and possible damage caused by fogs. This is why the parameterization of fog visibility is an important issue and a subject of extensive modeling. Typically, the relationship between the extinction coefficient and the liquid water content
is used in the visibility parameterization models [
53], resulting in the following expression:
where
are empirically determined numerical coefficients (in many operational forecast models:
,
[
53,
54]).
It has been shown [
55,
56] that for more adequate visibility parameterization, particularly under warm-fog conditions, it is necessary that the droplet number concentration
be also taken into account as an independent variable along with the
. According to the experimental relation of Jiusto [
57], the visibility is directly related to the average cloud droplet radius (and hence to the number concentration) and is indirectly related to the LWC. A new visibility parameterization scheme has been offered in the case of warm fog [
54,
56], regarding visibility as a function not only of
, but also of the droplet number concentration
:
where
is the so-called fog index.
The fog microphysics of the 1D Parameterized Fog (PAFOG) model [
58] is incorporated into and fully coupled with the 3D NOAA Nonhydrostatic Mesoscale Model [
59] and used in experiments for parameterization of fogs and low-level clouds in the planetary boundary layer.
In the work of Zhang et al. [
60], parameterization of fog visibility and its relationship with other fog properties, particularly microphysical ones, have been investigated. Two parameterization schemes (one considering
only, the other considering both
and
) are applied to four fog cases, including dense and light fogs. The results obtained support the conclusions that the optimized parameterization scheme considering the two parameters (
and
) provides a better approximation to the observed visibility, yielding (in the case of dense fogs) a relative error of 5%, as compared to the one considering
only, with a relative error of 20%.
It has been demonstrated that the use of the Global Positioning System (GPS) can be expanded into many fields, including the GPS meteorology, by using the delay of GPS signals passing through the atmosphere between GPS satellites and receivers [
61,
62]. An integrated Water Vapor (IWV) model based on GPS observations has been used in climatology along with the numerical weather prediction (NWP) model [
63]. The GPS IWV model is also applied as a new approach to fog detection and assessments by studying (in time domain) the relationship between the GPS IWV and meteorological observations during formation, evolution, and dissipation of dense fog [
64,
65].
The rate of water-vapor phase transformations and their dynamics are described and studied by using the water-continuity equation (Euler equation):
where
is the total water content,
is the water vapor content, and
is the cloud water content.
Four fog types, including radiation-, advection-, and mixed fog, as well as lack of fog, are analyzed on different time-scale series. The best results, in terms of predictability, are obtained for the radiation fog, while the worst ones concern the mixed fogs. The GPS IWV is assessed to be rather an auxiliary approach to analyzing fog formation and evolution dynamics.
The two-dimensional positive matrix factorization (PMF) model has been used to identify aerosol sources affecting fog formation [
66]. Decomposing a time-series aerosol chemical data set, four fog formation factors are identified: secondary species, biomass burning aerosols, dust, and sea salt. The particle mass is predicted with a satisfactory fitting accuracy and relative standard deviations.
Along with the fog formation and evolution, the processes of fog–surface interactions and deposition are also subjected to extensive modeling, as an important factor for the water balance of ecosystems. Too little is known about the magnitude and the temporal and spatial variability of fog deposition and its driving forces for mountain ecosystems.
In order to analyze quantitatively and to understand the mechanisms and features of fog water deposition on the underlying surface, various approaches have previously been developed and used [
46,
67,
68].
To study fog occurrence, acidification, and deposition in mountain forests, the scheme of fog deposition onto vegetation has been incorporated into the Weather Research and Forecasting (WRF) meteorological model in view of calculating the removal of cloud liquid water due to fog deposition [
69,
70,
71]. By using a modified version of the model, better predictions concerning the liquid water content of fog than the original version of WRF are achieved [
72].
The one-dimensional model of Lovett [
46], on the rates and mechanisms of cloud water deposition to a subalpine balsam fir forest, has been tested experimentally on a monthly basis [
47]. A relatively poor agreement between the model and measurements has been found, suggesting the limited applicability of the model for correct predictions, mainly to the fog deposition order of magnitude.
An important aspect of fog–surface interaction is the deposition of different chemical substances by fogs, when fog droplets intercept with vegetation. It is known that the concentration of ions in fog is much higher than in rain water [
73], favoring the higher rates of chemical deposition on forests and canopy by fog, and affecting the ecosystems, particularly in mountainous areas characterized by more frequent fog occurrences [
74].
In a series of works, Shimadera et al. [
75,
76,
77] have studied the ionic concentrations in fog water of several ecologically important polluting ions. Fog deposition contribution rates of trans-boundary transported SO
, NO
, NH
have been analyzed in [
75]. A two-dimensional fog deposition model has been developed and used to predict the turbulent fog water flux, together with the Community Multiscale Air Quality (CMAQ) modeling system and meteorological fields produced by the MM5. By using the model, the amounts of sulfur and reactive nitrogen compounds NO
x (NO, NO
2, NO
3, N
2O
5, HNO
3, HONO, aerosol nitrate) deposition by fog have been estimated [
76]. The annual deposition of sulfur (SO
2 and SO
) by fog in a mountain region has been studied, as compared to dry and wet deposition mechanisms, by using a combination of three numerical models, namely: the WRF model, the CMAQ model, and a fog deposition model [
77]. It is ascertained that the sulfur deposition amount by fog is larger than that by dry deposition and comparable to the one by wet deposition. The accuracy of model predictions concerning deposition of polluting ions by fog water is critically analyzed.
The advection-diffusion equation of fog can be written as follows [
77]:
where
is the horizontal wind component (ms
−1),
is the eddy diffusivity of heat (m
2 s
−1),
and
are fog water deposition terms by inertial impaction and gravitational settling of fog droplets on leaves, respectively. Normally, the main deposition mechanism is inertial impaction. However, under low-speed wind conditions, the relative weight of gravitational settling could increase [
46]. The deposition terms
and
are defined by
where
is the one-sided leaf area density (m
2 m
−3),
is the efficiency of inertial impaction,
is the gravitational settling velocity of fog droplets,
and
are the portions of the effective leaf area for deposition of fog droplets by inertial impaction and gravitational settling, respectively. All coefficients present in Equations (
3)–(
8) can be determined empirically or from the literature (e.g., [
69]). By using Equations (
6)–(
8), the processes of fog diffusion/advection and deposition could be analyzed.
Comprehensive studies on and analyses of fog modeling and forecasting/nowcasting, including method and model classification and characterization, can be found in the review of Gultepe et al. [
5], as well as in the reports of various Actions of the European Cooperation in Science and Technology (COST Actions) (e.g., [
78]).
Forecasting fogs in synoptic meteorology is a very difficult task. Even the currently most advanced numerical weather models have troubles in predicting precisely fog formation. This may be related to the strong influence of local conditions, in addition to the lack of consideration of the proper surface tension physics. However, as mentioned above, there are ways to predict fog bearing in mind the development of the atmospheric circulation. For example, during the movement of a cold front over a well-known warm surface with enough humidity, fog would often appear in the morning [
79]. Another synoptic situation assisting in fog forecasting is the absence of wind in the eye of an anticyclone. This state of the atmosphere favors temperature inversions, particularly in the case of taking place over valleys with water basins or rivers [
80,
81].
Detection and measurement of ground fogs by satellites is a modern instrument for operational nowcasting applications and studies on the climate processes and changes [
82,
83].
7. Conclusions
Clouds and fogs are essential for the Earth’s climate. In spite of the abundant research on this topic, it is still an open field for research and development. Accordingly, important issues to be addressed still remain, such as how the atmospheric water cycle is affected by water droplet nucleation and condensation, or how to achieve better understanding and characterization of the mechanisms and conditions for cloud/fog formation, existence, and decay. This imposes new demands and challenges to the cloud/fog research, needing more and permanent monitoring and studies of fogs, due to their importance for the environment and societal life.
Fog forecasting remains a challenging task, necessitating updating of our knowledge on droplet- and water–air-interface physics. Existing 1D and 3D fog models should be updated regularly to keep their relevance or new ones should be developed, requiring new parameterization schemes and data. Since the meteorological models evolve faster because of the large number of stations and abundance of parameterization data, combining fog models with mesoscale and regional meteorological models appears to be effective and fruitful for more adequate fog parameterization and forecasting.
The intensification of the use of combustive and polluting technologies and power sources in industry and transportation affects the natural fog composition, provoking more frequent and longer-lasting smog occurrences in industrial and urban areas, with severe impacts on air quality and human health. In addition, increasing production and usage of artificial fogs, along with their usefulness and advantages, can also be a potential source of allergies and other side effects, as well as a subject of non-correct dangerous or damaging applications.
In summary, fogs are important factors influencing the environmental balance, air quality, ecosystems, transportation, and human health. Fog research has achieved remarkable progress in understanding the nature and mechanisms of fog formation, existence, and decay. Still, the constantly changing natural and social environment impose new demands and challenges, needing new efforts, data, and approaches, aimed to better and more adequate parameterization, modeling, and forecasting of fog events, in order to reduce their negative environmental, social, and medical effects.