Types Of Fog Research Articles

Implementation of WRF‐Urban Asymmetric Convective Model (UACM) for Simulating Urban Fog Over Delhi, India

AbstractAccurate fog prediction in densely urbanized cities poses a challenge due to the complex influence of urban morphology on meteorological conditions in the urban roughness sublayer. This study implemented a coupled WRF‐Urban Asymmetric Convective Model (WRF‐UACM) for Delhi, India, integrating explicit urban physics with Sentinel‐updated USGS land‐use and urban morphological parameters derived from the UT‐GLOBUS dataset. When evaluated against the baseline Asymmetric Convective Model (WRF‐BACM) using Winter Fog Experiment (WiFEX) data, WRF‐UACM significantly improved urban meteorological variables such as diurnal variations in 10‐m wind speed, 2‐m air temperature (T2), and 2‐m relative humidity (RH2) during a fog event. UACM also demonstrates improved accuracy in simulating temperature and significantly reducing biases for wind speed and daytime RH2 under clear sky conditions. UACM reproduced the nighttime urban heat island effect within the city, showing realistic diurnal heating and cooling patterns that are important for accurate fog onset and duration. UACM effectively predicts the onset, evolution, and dissipation of fog, aligning well with observed data and satellite imagery. Compared to WRF‐BACM, WRF‐UACM reduces the cold bias soon after sunset, thus improving the fog onset error by ∼3 hr. This study highlights the UACM's potential to improve fog prediction and its application in operational settings. With further investigation into different fog types, the UACM can provide crucial insights for preventive measures and reducing disruptions in urban areas.

Impact of Multi-Scattered LiDAR Returns in Fog.

In the context of autonomous driving, the augmentation of existing data through simulations provides an elegant solution to the challenge of capturing the full range of adverse weather conditions in training datasets. However, existing physics-based augmentation models typically rely on single scattering approximations to predict light propagation under unfavorable conditions, such as fog. This can prevent the reproduction of important signal characteristics encountered in a real-world environment. Consequently, in this work, Monte Carlo simulations are employed to assess the relevance of multiple-scattered light to the detected LiDAR signal in different types of fog, with scattering phase functions calculated from Mie theory considering real particle size distributions. Bidirectional path tracing is used within the self-developed GPU-accelerated Monte Carlo software to compensate for the unfavorable photon statistics associated with the limited detection aperture of the LiDAR geometry. To validate the Monte Carlo software, an analytical solution of the radiative transfer equation for the time-resolved radiance in terms of scattering orders is derived, thereby providing an explicit representation of the double-scattered contributions. The results of the simulations demonstrate that the shape of the detected signal can be significantly impacted by multiple-scattered light, depending on LiDAR geometry and visibility. In particular, double-scattered light can dominate the overall signal at low visibilities. This indicates that considering higher scattering orders is essential for improving AI-based perception models.

THE ROLE OF AIR TEMPERATURE AND DEW POINT DURING FOG RECURRENCES ON THE ABSHERON PENINSULA (AZERBAIJAN)

The article focuses on the characteristics of air temperature and dew point changes observed during repeated fog events in the Absheron Peninsula in 2000...2022. For this purpose, continuous observation data of Heydar Aliyev International Airport was used. Here is an analysis of all types of fog by month of occurrence. The limits of total fog, Meteorological Optical Range ≤ 500 m. and 501...1000 m. are considered in the analysis. Repeating such criteria, attention was paid to the recorded air temperature and dew point indicators for I...III, IV...VI and X...XII months of the year. The analyzes show that the fogs observed in the peninsula are mostly recorded at a temperature of 6...8 0C. The most commonly observed dew point temperature ranges of 0,0...0,3 0C and 1,0...1,2 0C at all ranges of MOR in fog repeats. The results of the research are of particular importance for the planning of the work of all transport areas and the forecast of fogs.

Challenges in Simulating Prevailing Fog Types Over Urban Region of Delhi

AbstractAccurately predicting fog is challenging due to interplay of myriad processes in its formation and high spatiotemporal variability. This study compares the performance of the Weather Research and Forecasting model with control (CNTL‐WRF) and assimilated fine‐grid (HRLDAS‐WRF) soil fields in the Ingo‐Gangetic Plain (IGP) over a 2‐years winter period (2019–2020 and 2020–2021). Results show HRLDAS‐WRF enhances accuracy in representing surface fog's heterogeneity and lifecycle across the IGP, demonstrating a spatial skill improvement of approximately 18% with a Fraction Skill Score of 0.44, compared to CNTL‐WRF's (0.36). Employing fog classification algorithm identifies 25 dense fog episodes (Vis < 500 m) over Delhi's urban boundary layer, including 14 radiation (RAD), 5 cloud‐base lowering (CBL), 3 advection + radiation (ADV + RAD), and 3 evaporation (EVA) episodes. CNTL‐WRF predicts 20 episodes but misses five due to a dry bias in the initial moisture conditions. However, HRLDAS‐WRF demonstrates limited vertical fog growth in various occurrences, highlighting the crucial role of fine‐gridded soil states for enhanced land‐surface feedback. Detailed analysis shows a 40% reduction in mean onset error for RAD fog occurrences in HRLDAS‐WRF when compared to CNTL‐WRF. In CBL fog episodes, both models exhibit significant radiative cooling and inversion before fog onset, leading to inaccurate predictions as RAD fog. Similarly, forecasting the abrupt development of ADV + RAD fog episodes is challenging as models struggle to replicate moisture intrusion over radiatively cooled surfaces in windy conditions. Predicting EVA fog, forms within an hour after sunrise, remains difficult due to the current model parameterization that rapidly dissipates fog soon after sunrise.

Fog climatology at Shanghai Pudong International Airport

AbstractUsing 22‐year ground observations on meteorological variables, we conducted a statistical analysis to reveal fog characteristics at Shanghai Pudong International Airport (SPIA). Fog events were classified by fog types using an objective method. Two types of advection fogs are dominant in fog events at SPIA, followed by radiation fogs. Different fogs have evident annual and monthly variations in frequency. Advection fog mostly appear from February to April, while radiation fog is mainly between November and February. Six synoptic patterns associated with fogs were determine by a self‐organizing maps cluster method, two of which, namely ‘west of marine high pressure’ and ‘inverted trough’, are most favourable for the formation of advection fogs, whereas ‘bottom of weak cold high‐pressure’ is favourable for radiation fogs. The frequency of advection fog occurrence exhibits temporal fluctuations with distinct peaks occurring 2–3 h after sunset, around midnight, and 1–2 h before sunrise, while almost all radiation fog occur in the second half of the night, and favourable conditions for fog onset become more effective with the length of the night. Radiation fog exhibits a longer duration than advection fog and tends to result in lower visibility. The prevalent wind direction for the formation of advection fog is generally from the east to the southeast, whereas radiation fog is typically associated with westerly winds. The temperature and air pressure during the formation of both advection and radiation fogs follow a normal distribution in most seasons. More fog characteristics were categorized by fog types and seasons.

Fog analysis of airports in the northeast of Turkey in the last decade

AbstractFog is one of the important meteorological phenomena that play a critical role in the aircraft flying conditions and at airports. Fog events can lead to serious damage to the airport operations and the economy due to low visibility conditions and interrupting transportation. This study aims to explain the characteristics of the fogs occurring at the airports of the Kars, Agrı, and Igdır cities where located in the northeast of Turkey and neighbouring Georgia, Armenia, Nakhchivan, and Iran, and evaluate fog types. Within the scope of the study, a total of 3792 foggy aviation routine weather reports (METAR) and selected aviation special weather reports (SPECI) observations of the 10 years; from 2011 to 2020 were used for Igdır Şehit Bülent Aydın Airport (International Civil Aviation Organization [ICAO] code: LTCT), Kars Harakani Airport (LTCF) and Agrı Ahmed‐i Hani Airport (LTCO). During the time period studied, the number of foggy days in LTCT, LTCF, and LTCO was 41, 296, and 464, respectively. Fog events were most frequent from November to March, with December, January, and February being the most active months. August and September have no fog events in all three airports. Fog formation occurs usually at night hours and reaches a maximum around sunrise. Based on the physical formation mechanism, in LTCT, precipitation fog (PCP) was dominant at 39.02%; in LTCF and LTCO, radiation fog (RAD) was dominant at 33.11% and 58.84%, respectively.

Hybrid FSO/RF system over proposed random dust attenuation model based on real-time data combined with G–G atmospheric turbulence

Free-space optical (FSO) communication links are thought to be most negatively impacted by fog and sandstorms. Several probabilistic models for signal attenuation in various types of fog have been proposed in the literature. To the best of our knowledge, there is no prior probabilistic signal attenuation model for dusty channels. As a result, in this study, we analyse the probabilistic behaviour of the FSO channel during sandstorms using real-time data from the Egyptian Meteorological Authority and propose the negative-exponential (NE) distribution as a novel statistical model for signal attenuation in dust. A probability distribution function (PDF) is then derived for the random dust channel state. Also, a closed-form channel state distributional formulation is derived for a stochastic FSO channel model that considers both dust effects and Gamma–Gamma (G–G) atmospheric turbulence-induced fading. Next, closed formulas are derived for both the average bit-error rate (BER) and outage probability for the FSO system operating in this channel environment. Using the forms derived, it can be shown that the dust storm has a significant impact on the system’s performance. Finally, to overcome this degradation, we suggest utilizing a hybrid FSO and radio frequency (RF) wireless communication system. The received signals are combined using the well-known diversity selection combining (SC) technique. Further, the hybrid system’s performance was compared to that of an FSO-only system.

Characteristics of Advection Fog at Qingdao Liuting International Airport

The advection fog characteristics at Qingdao Liuting International Airport during 2000–2022 are studied based on surface observation, sounding and reanalysis data. Surface observation data show that there were two types of fog: evaporation fog (EF) dominated by northwesterly wind in winter and cooling fog (CF) dominated by southeasterly wind in spring and summer. CF is thicker than EF due to different planetary boundary layer (PBL) structures. For EF, the middle and low troposphere are affected by dry and cold air, while CF is affected by warm and moist air below 850 hPa. When EF formed, downdrafts and a positive vertical gradient of the pseudo-equivalent potential temperature indicate stable PBL, surface heat flux is upward from sea to atmosphere and surface wind diverges near the air–sea interface. When CF formed, these characteristics are reversed. Fog is significantly affected by sea–land–atmosphere interactions. The moisture source is mainly from surface fluxes released by the Yellow Sea in the case of EF, while it is from moist air at low latitudes and local land transpiration in the case of CF. The difference in temperature between the sea surface and surface air changes from the range of 0–8 K for EF but from −4–0 K for CF.

Artificial atmospheric cloud formations: state and prospects of research

The article presents a classification of atmospheric cloud formations, which includes, along with clouds and fogs of natural origin, also anthropogenic atmospheric cloud formations of an unintentional nature, accompanying human economic activity, and intentional nature (artificial clouds and fogs purposefully created by man).
 Based on a systematic analysis of patent materials and information sources in this subject area, possible methods for creating artificial clouds and fogs are summarized, indicating the level of their implementation.
 It is shown that at present it is possible to create the following types of artificial clouds and fogs: artificial cirrus crystalline clouds, artificial convective clouds, artificial wavy clouds, artificial fogs, artificial low-altitude water-aerosol formations. Some results of field experiments on the creation of artificial fogs and clouds of various forms in our country and abroad are presented. 
 The list of solved applied tasks in the creation of artificial clouds and fogs is defined. The possibility of solving such problems is shown by the example of destroying thunderstorms, hail in large areas with the help of artificial cirrus clouds and by the example of destroying frost with the help of artificial fogs created by thermo kinetic installations.

Dealing with the heterogeneous presentations of freezing of gait: how reliable are the freezing index and heart rate for freezing detection?

BackgroundFreezing of gait (FOG) is an unpredictable gait arrest that hampers the lives of 40% of people with Parkinson’s disease. Because the symptom is heterogeneous in phenotypical presentation (it can present as trembling/shuffling, or akinesia) and manifests during various circumstances (it can be triggered by e.g. turning, passing doors, and dual-tasking), it is particularly difficult to detect with motion sensors. The freezing index (FI) is one of the most frequently used accelerometer-based methods for FOG detection. However, it might not adequately distinguish FOG from voluntary stops, certainly for the akinetic type of FOG. Interestingly, a previous study showed that heart rate signals could distinguish FOG from stopping and turning movements. This study aimed to investigate for which phenotypes and evoking circumstances the FI and heart rate might provide reliable signals for FOG detection.MethodsSixteen people with Parkinson’s disease and daily freezing completed a gait trajectory designed to provoke FOG including turns, narrow passages, starting, and stopping, with and without a cognitive or motor dual-task. We compared the FI and heart rate of 378 FOG events to baseline levels, and to stopping and normal gait events (i.e. turns and narrow passages without FOG) using mixed-effects models. We specifically evaluated the influence of different types of FOG (trembling vs akinesia) and triggering situations (turning vs narrow passages; no dual-task vs cognitive dual-task vs motor dual-task) on both outcome measures.ResultsThe FI increased significantly during trembling and akinetic FOG, but increased similarly during stopping and was therefore not significantly different from FOG. In contrast, heart rate change during FOG was for all types and during all triggering situations statistically different from stopping, but not from normal gait events.ConclusionWhen the power in the locomotion band (0.5–3 Hz) decreases, the FI increases and is unable to specify whether a stop is voluntary or involuntary (i.e. trembling or akinetic FOG). In contrast, the heart rate can reveal whether there is the intention to move, thus distinguishing FOG from stopping. We suggest that the combination of a motion sensor and a heart rate monitor may be promising for future FOG detection.

The Multiple Scattering of Laser Beam Propagation in Advection Fog and Radiation Fog

The laser beams were scattered and attenuated when they propagate in fogs for laser communication, laser remote sensing detection. For different density and droplets distribution of fogs, the laser scatter and attenuation are different, the correspond mechanism need thorough investigation. The characteristics of laser beam scattering in different types of fogs are studied based on the droplet size characteristics of advection fog and radiation fog, the scattering coefficients of droplets with different laser wavelengths(0.86 μm, 0.91 μm, 1.06 μm, 1.3015, and 10.6 μm) are calculated, the multi scattering of laser beam is studied by the Monte Carlo method, the propagation path and scattering direction of photons is analyzed, relations between asymmetry factor, albedo of fog droplets, and the visibility are presented, and the forward scattering intensity and the backward scattering intensity versus scattering angle are gotten and discussed.

Categorisation of random images into fog and blur based on the statistical analysis

Noisy images are a bottleneck to solve the image processing problems. The present paper aims to classify images as different types of foggy and blurry images. A feature based classifier called FB classifier has been proposed. Given an image the classifier is able to tell whether the image is clear or unclear, which type of distortion is there, either foggy or blurry and also the categories of different types of blur and fog. The quality of the images taken through any equipment depends on few factors: 1) medium in which the photograph is taken; 2) the movements of either the camera or the object or movement of both; 3) the quality of the equipment that is used for capturing. All the algorithms of classification or the removal of distortions are made to handle the above three scenarios. The three factors encompass all types of foggy or the blurry images. The images viewed are given different threshold values according to their properties and finally the cumulative threshold value decides which type of the image is it. The algorithm is simple to implement yet it is comparable to the state of art methods.

ANALYSIS OF THE FOGGY DAYS AND ITS KEY MECHANISMS OVER THE YANGON FLIGHT REGION


 
 
 A preliminary study of the foggy day analysis at Yangon International Airport was carried out from the previous author’s first study by using a statistical approach. This opens up a fresh line of inquiry and the opportunity to consider climatology from a different angle. Fog has occurred most frequently in the winter months of December and January. During the 19 years period from 2003 to 2021, we count 187038 occurrences of observation from Yangon International Airport using half-hourly METAR data. This information included 32966 instances of meteorological phenomena, including 379 incidences of mist and 7911 instances of foggy weather. The total number of days with fog varies from 23 to 91 per year, with the year 2014 having the most days with fog (91), and the year 2010 having the fewest days with fog (23). Winter has nearly six times the amount of fog days as summer and 1.5 times the amount of fog days as autumn. The most frequent time of day for fog was between 2300 and 01:00 UTC. Fog most usually occurs between 1800 and 2000 UTC following rains in the summer and early autumn. Data during the four to six hours prior to the commencement of the fog are subjected to classification criteria, 7610 of the 8289 fog events are only radiation and advection fog types. The first three months of the year have the highest incidence of radiation fog onset. The greatest frequencies of radiation fog onset are in February (17 %) and March (18 %). Fog episodes in 19 years were observed to be accompanied by stratus clouds 53.3 percent of the time, with low clouds being absent in 46.7 percent of them. The departure of the beginning and dissipation from the sunrise and sunset, respectively, can be used to analyze the behavior of each form of fog. At Yangon International Airport, the conditions where the fog was most likely to form were those when the minimum temperature fell between 16 and 27 °C (liquid fog). The latest discovery raises a new topic and a new option for interpreting climatology from a new perspective and making considerable progress in the predictability study, according to a well- known physicist in another setting.
 
 

Seasonal and Microphysical Characteristics of Fog at a Northern Airport in Alberta, Canada

Reduction in visibility (Vis) due to fog is one of the deadliest severe weather hazards affecting aviation and public transportation. Nowcasting/forecasting of Vis reduction due to fog using current models is still problematic, with most using some type of empirical parameterization. To improve the models, further observational studies to better understand fog microphysics and seasonal variability are required. To help achieve these goals, the seasonal and microphysical characteristics of different fog types at Cold Lake airport (CYOD), Alberta, Canada were analyzed using hourly and sub-hourly METAR data. Microphysical and meteorological measurements obtained using the DMT Fog Monitor FM-120 and the Vaisala PWD22 were examined. The results showed that radiation fog (RF) dominates at CYOD in summer while precipitation, advection and cloud-base-lowering fogs mostly occur in fall and winter. All fog types usually form at night or early morning and dissipate after sunrise. The observed dense fog events (Vis < 400 m) were mainly caused by RF. The observed mean fog particle spectra (n(D)) for different fog types and temperatures showed bimodal n(D) (with two modes near 4 μm and 17–25 μm; the maximum total number concentration (Nd) was 100 cm−3 and 20 cm−3, respectively, corresponding to each mode). Parameterizations of Vis as a function of liquid water content (LWC) and Nd were developed using both the observed Vis and calculated Vis based on n(D). It was found that the observed Vis was higher than the calculated Vis for warm fog with LWC > 0.1 gm−3 and most of the mass was contributed by the large drops. Based on the observed Vis, the relative error of the visibility parameterization as a function of both LWC and Nd (32%) was slightly lower than that (34%) using LWC alone for warm fogs.

Feasibility analysis of early warning of sea fog within six hours for two harbors in the South China Sea

Sea fog significantly impacts harbor operations, at times even causing navigation to cease. This study examines two harbors in the north of the South China Sea, analyzing the feasibility of increasing each harbor’s sea fog early warning capability to 6 h in advance. Although the harbors are separated by only about 100 km, analysis of their backward trajectories reveals differences in the incoming flow and sea fog types. Concerning the types, at Xuwen harbor, warm advection fog represents 49.56% of the cases, cold advection representing 48.03%. At Zhanjiang harbor, 37.06% are warm advection fog, with 58.33% cold advection fog cases. We propose different monitoring and early warning schemes for the harbors. For Xuwen, we suggest eight visibility lidars located on the north and south sides of Qiongzhou Strait (two on the north, six on the south). Here, such a setup would give warning probabilities of sea fog of 87.50, 66.23, and 49.78% for advance times of 2, 3, and 4 h. For Zhanjiang, we suggest two visibility lidars and four buoys at the east side of the harbor. The corresponding warning probabilities are 83.77, 64.47, and 47.15% for the same advance times. For 5–6 h in advance, the early warning probabilities of both harbors drop quickly. We also suggest a flow chart for the early warning and monitoring scheme at each harbor.

Fog trends in India: Relationships to fog type and western disturbances

AbstractFog is a major hazard in wintertime over India, particularly in the Indo‐Gangetic Plains, leading to significant impacts for transport and human health. Using 3‐hourly surface observations, from 69 sites across India, all fog and dense fog events between 2000 and 2020 are identified. For each event, the main fog formation mechanism is objectively categorized using a classification algorithm, distinguishing between radiation, advection, evaporation, precipitation or cloud‐base lowering fog types. In contrast to the findings of other international studies, radiation fog dominates as the most common fog type at the vast majority of locations in India, accounting for 68.1% of all fog events and 70.0% of dense fog events. Statistically significant positive trends are seen in the frequency of all fog events at Delhi, Lucknow and Patna, in the Indo‐Gangetic plains, between 1997/1998 and 2018/2019, dominated by comparable statistically significant positive trends in radiation fogs. Western disturbances (WD) are often linked to the formation of fog in India. Using a climatology of WDs, we show that 46.9% of radiation fog onsets in Delhi in December and January, the primary fog months, happened in conjunction with an active WD event. Conversely, only 32.3% of WDs during these same months coincided with the onset of a radiation fog event. WD‐related radiation fog events are shown to cluster into three distinct groups, with WD centres located to the northwest (51.4% of cases), southwest (13.3%) and east (35.2%) of Delhi. Each cluster is shown to have coherent and distinct near‐surface characteristics which are conducive to fog formation. Trends in WD frequency cannot fully account for the observed trends in fog events. We argue that the fog trends are more likely the result of a complex interaction between urban expansion and the associated rapid change in aerosol loading, resulting in impacts on radiation balance, microphysics and heat‐island processes.

A climatological study of fog in Turkey

AbstractIn this study, climatological properties of fog types, their association with surrounding environment and background synoptic mechanisms triggering fog formation are investigated for Turkey. For this purpose, SYNOP (surface synoptic observations) and METAR (meteorological aerodrome report) codes of 105 stations are used for the period 2014–2019. While fog types are separated to the fog, quasi‐fog, and dense fog events according to horizontal visibility conditions, stations are classified as mountain, seaside, urban, and rural by considering the surrounding environment. Synoptic mechanisms causing the occurrence of springtime marine fog events over Black Sea are investigated using NCEP/NCAR Reanalysis. According to the main results, highest numbers of fog events are observed during winter, spring, fall, and summer months, respectively. Radiation fog frequently occurs in the mountainous regions (i.e., central and eastern part) of Turkey during winter. As a result of the rainfall during daytime, significant amount of moisture begins to saturation at night owing to rapid cooling processes and fog layer forms just above the surface. During spring, Black Sea‐effect marine fog events are frequently shown in the seaside stations of the Black Sea and northeastern Marmara regions. On the western Black Sea, winds from northeast enable rising of cold sea water to the surface (upwelling) and transfer it to the relatively warm land areas. Denser humid and cold air settling on the surface forces warm land air to rise. Additionally, warm air advection at 850 hPa (low level) creates a thick inversion layer over the region. For the eastern Black Sea, relatively cold and humid sea surface transferred to the coasts by light local northwesterly winds encounters with the mountain barrier and is trapped in the region. Furthermore, southerly winds (850 hPa) cross over the Kaçkar Mountains cause föehn effect and generate an inversion layer over the fog layer.

Reconciling the isotope-based fog classification with meteorological conditions of different fog types

Although fog is an important component of the hydrological cycle of drylands, its formation mechanisms are not fully understood leading to discrepancy of fog type classifications using different methodologies. Such discrepancies may result in under or over estimation of the potential impacts of global climate change on the ecohydrology of fog dependent ecosystems. To fill this knowledge gap, this study applies hydrogen and oxygen stable isotopes to objectively classify fog and compares this classification to meteorological conditions defined for the formation of these fog types. Results suggest that this isotope based fog classification method is consistent with expected meteorological conditions for the different fog types such as radiation fog, advection fog and mixed fog. Wind speeds decreased from advection, to radiation fog while estimated fog (cloud) height was lowest during radiation fog and highest during advection fog. In addition, precipitation-offset (lc-excess) suggests that radiation and advection fog had different moisture sources, with radiation fog being locally sourced and advection fog originating from the ocean. This study suggests isotope-based fog classification is an objective method that could be applied to other coastal fog dependent ecosystems to help assess the potential impact of climate change on these ecosystems.

PARAFOG v2.0: a near-real-time decision tool to support nowcasting fog formation events at local scales

Abstract. An improved version of the near-real-time decision tool PARAFOG (PFG2) is presented to retrieve pre-fog alert levels and to discriminate between radiation (RAD) and stratus lowering (STL) fog situations. PFG2 has two distinct modules to monitor the physical processes involved in RAD and STL fog formation and is evaluated at European sites. The modules are based on innovative fuzzy logic algorithms to retrieve fog alert levels (low, moderate, high) specific to RAD/STL conditions, minutes to hours prior to fog onset. The PFG2-RAD module assesses also the thickness of the fog. Both the PFG2-RAD and PFG2-STL modules rely on the combination of visibility observations and automatic lidar and ceilometer (ALC) measurements. The overall performance of the PFG2-RAD and PFG2-STL modules is evaluated based on 9 years of measurements at the SIRTA (Instrumented Site for Atmospheric Remote Sensing Research) observatory near Paris and up to two fog seasons at the Paris-Roissy, Vienna, Munich, and Zurich airports. At all sites, pre-fog alert levels retrieved by PFG2 are found to be consistent with the local weather analysis. The advanced PFG2 algorithm performs with a hit rate of about 100 % for both considered fog types and presents a false alarm ratio on the order of 10 % (30 %) for RAD (STL) fog situations. Finally, the first high alerts that result in a subsequent fog event are found to occur for periods of time ranging from −120 min to fog onset, with the first high alerts occurring earlier for RAD than STL cases.

Single Image Atmospheric Veil Removal Using New Priors for Better Genericity

From an analysis of the priors used in state-of-the-art algorithms for single image defogging, a new prior is proposed to obtain a better atmospheric veil removal. Our hypothesis is based on a physical model, considering that the fog appears denser near the horizon rather than close to the camera. It leads to more restoration when the fog depth is more important, for a more natural rendering. For this purpose, the Naka–Rushton function is used to modulate the atmospheric veil according to empirical observations on synthetic foggy images. The parameters of this function are set from features of the input image. This method also prevents over-restoration and thus preserves the sky from artifacts and noises. The algorithm generalizes to different kinds of fog, airborne particles, and illumination conditions. The proposed method is extended to the nighttime and underwater images by computing the atmospheric veil on each color channel. Qualitative and quantitative evaluations show the benefit of the proposed algorithm. The quantitative evaluation shows the efficiency of the algorithm on four databases with different types of fog, which demonstrates the broad generalization allowed by the proposed algorithm, in contrast with most of the currently available deep learning techniques.