Foggy Days Research Articles

A Multi-Tiered Collaborative Network for Optical Remote Sensing Fine-Grained Ship Detection in Foggy Conditions

Ship target detection faces the challenges of complex and changing environments combined with the varied characteristics of ship targets. In practical applications, the complexity of meteorological conditions, uncertainty of lighting, and the diversity of ship target characteristics can affect the accuracy and efficiency of ship target detection algorithms. Most existing target detection methods perform well in conditions of a general scenario but underperform in complex conditions. In this study, a collaborative network for target detection under foggy weather conditions is proposed, aiming to achieve improved accuracy while satisfying the need for real-time detection. First, a collaborative block was designed and SCConv and PCA modules were introduced to enhance the detection of low-quality images. Second, the PAN + FPN structure was adopted to take full advantage of its lightweight and efficient features. Finally, four detection heads were used to enhance the performance. In addition to this, a dataset for foggy ship detection was constructed based on ShipRSImageNet, and the mAP on the dataset reached 48.7%. The detection speed reached 33.3 frames per second (FPS), which is ultimately comparable to YOLOF. It shows that the model proposed has good detection effectiveness for remote sensing ship images during low-contrast foggy days.

Simultaneous lightwave information and power transfer for NLOS ultraviolet communications under different weather conditions

In this work, a power allocation scheme for ultraviolet communication (UVC) with simultaneous lightwave information and power transfer (SLIPT) is proposed. Based on the non-line-of-sight (NLOS) single scattering channel model, the path loss and bit error rate (BER) performance of the UVC system with different geometrical parameters are analyzed. The effects of three types of weather, light rain, fine weather and severe fog conditions are considered. It is proved that the path loss is minimized on foggy days with minimum BER, followed by sunny days. The proposed SLIPT approach that the receiving energy is in turn divided into two parts, one for energy harvesting (EH) and the other for information decoding (ID). The system is designed to optimize energy harvesting for autonomous power supply, while ensuring a specified BER threshold and maintaining spectral efficiency. The effects of three types of weather on the SLIPT system are further investigated. It is demonstrated that under shorter communication distance(r), smaller elevation angle of receiving terminal (βR), larger field of view (FoV) and lower spectral efficiency(η) conditions, a higher percentage of energy is allocated for EH, with foggy days being the most favorable. The extreme geometric parameters of rainy condition can lead to the inability of communication, which needs to be limited to r ≤ 10 m, elevation angle of Rx ≤ 6°, FOV ≥100°, spectral efficiency ≤3, under the preset parameters in this paper.

Multi-feature fusion dehazing based on CycleGAN

Under the foggy environment, lane line images are obscured by haze, which leads to lower detection accuracy, higher false detection of lane lines. To address the above problems, a multi-layer feature fusion dehazing network based on CycleGAN architecture is proposed. Firstly, the foggy image is enhanced to remove the fog in the image, and then the lane line detection network is used for detection. For the dehazing network, a multi-layer feature fusion module is used in the generator to fuse the features of different coding layers of U-Net to enhance the network’s recovery of information such as details and edges, and a frequency domain channel attention mechanism is added at the key nodes of the network to enhance the network’s attention to different fog concentrations. At the same time, to improve the discriminant effect of the discriminator, the discriminator is extended to a global and local discriminator. The experimental results show that the dehaze effect on Reside and other test data sets is better than the comparison method. The peak signal-to-noise ratio is improved by 2.26 dB compared to the highest GCA-Net algorithm. According to the lane detection of fog images, it is found that the proposed network improves the accuracy of lane detection on foggy days.

Analysis of the impact of Arctic environment conditions on the psychophysical status of navigators and safety of ice navigation

Abstract The Arctic is a specific geographical region, whose environment effects any person's staying and working there, so it demands researching and accounting in the practice of ice navigation. The most difficult period of navigation is the winter-spring time, when Arctic Seas are covered by an ice cover. Ice navigation is always associated with a large number of difficulties for the crewmen, not only because of the geographical location of the water area, but also the climatic and environment conditions that impact on the mental and physical state of crewmen. These conditions, namely, the length of the polar night and day, the extreme frosts, maximum number of foggy days, and the duration of the ice period on all the tracks of the cargo ships along the North Sea Route were selected and analysed as the dominant factors that impact on the ship's crewmen. The analysis was performed on the base of the ‘Matrix of Arctic Impacts’ arraying that summarizes data about the level of the selected factors displayed in the local water areas along the North Sea Route and provided the selection of the local water areas with the most impacts of Arctic environment factors on the psychophysical status of the crewmen. In these water areas, accidents caused by the ‘human factor’ are most likely to occur. Therefore, special researches of the Arctic environment's impact on navigators is required in order to improve the safety of ice navigation.

Stability Detection of Canopy RGB Images for Different Underlying Surfaces Based on SVM

This study aims to investigate the impact of different environmental conditions on the stability of RGB images in ecological sites and the anti-interference properties of vegetation images on different underlying surfaces. Three vegetation types including sugarcane, forest, and karst (mainly shrub and grass) are used to segment green vegetation using machine learning, and the RGB vegetation indices are calculated using color channel data. Then, The effect of weather, season, and time period on different types of vegetation indices are studied, which provide technical references for quantitative application of RGB image data. The results indicate the following: ① For the vegetation with high canopy density, the SVM machine learning segmentation algorithm used in this study is more applicable, as the RGB image segmentation accuracy of sugarcane and forest is significantly higher than karst. For different weather conditions, the segmentation accuracy of sugarcane and forest RGB images on sunny or cloudy days is higher than that on rainy or foggy days, but the effect on sparse vegetation in karst is not obvious. Additionally, the segmentation accuracy of different vegetation types has a small increase with NLM filter processing. ② Change in weather, season, and time can affect the stability of the image index. For different weather conditions, the vegetation indices of sugarcane and forest images on sunny or cloudy days are the more stable (ExGR, outlier proportion between 3.25% and 5.63%), while on rainy and foggy days they are less stable (ExR, outlier proportion between 17.60% and 21.59%). For different seasons, the stability of the sugarcane and karst image index obtained in spring is better (ExG, outlier proportion between 3.32% and 6.88%), while the stability of the forest image index obtained in summer is better (ExR, outlier proportion = 10.32%). For different times within a day, the sugarcane image index obtained in the morning is more stable (ExGR, outlier proportion = 4.62%), while the stability of the forest image index obtained in the afternoon is better (ExGR, outlier proportion = 9.24%). ③ The stability of the sugarcane image index is more affected by weather and season. For forest, the influence of the weather is more obvious than the season. But, for karst, the effect of season on the vegetation index is greater than that of weather.

Climate state of the Three Gorges Region in the Yangtze River basin in 2022–2023

Based on daily observation data of the Three Gorges Region (TGR) of the Yangtze River basin and global reanalysis data, the climate characteristics, climate events, and meteorological disasters of the TGR in 2022 and 2023 were analyzed. For the TGR, the average annual temperature for 2022 and 2023 was 0.8 °C and 0.4 °C higher than normal, respectively, making them the two warmest years in the past decade. In 2022, the TGR experienced its warmest summer on record. The average air temperature was 2.4 °C higher than the average, and there were 24.8 days of above-average high temperature days during summer. Rainfall in the TGR varied significantly between 2022 and 2023. Annual rainfall was 18.4 % below normal and drier than normal in most parts of the region. In contrast, the precipitation in 2023 was considerably higher than the long-term average, and above normal for almost the entire year. The average wind speed exhibited minimal variation between the two years. However, the number of foggy days and relative humidity increased in 2023 compared to 2022. In 2022–2023, the TGR mainly experienced meteorological disasters such as extreme high temperatures, regional heavy rain and flooding, overcast rain, and inverted spring chill. Analysis indicates that the abnormal western Pacific subtropical high and the abnormal persistence of the eastward-shifted South Asian high were the two important drivers of the durative enhancement of record-breaking high temperature in the summer of 2022.摘要基于长江三峡地区观测资料和全球再分析资料, 分析了该地区 2022–2023年气候特征, 酸雨状况以及主要天气气候事件. 2022 年和2023年三峡地区平均气温分别较常年偏高0.8 °C和0.4 °C, 是近十年来最暖的两年, 特别是2022年夏季出现破记录极端高温; 2022年三峡地区降水量较常年偏少18.4 %, 2023年降水量转为偏多15.3 %. 在这两年中该地区主要出现了极端高温, 区域性暴雨洪涝, 连阴雨和倒春寒等气候事件. 分析表明, 西太平洋副热带高压和南亚高压协同异常是2022年夏季极端高温维持的两个重要因素.

Projection helps to improve visual impact: On a dark or foggy day

Driving is a highly visually demanding activity. Different driving conditions have different effects on drivers, so to understand the effects of driving vision on drivers, this paper directly investigates the role of central and peripheral vision in different scenarios and tests whether projection training improves driving behavior. We use a VR device to selectively present information in the central and peripheral parts of the field of view to achieve these goals. In Experiment 1, we compare drivers’ performance with and without experience when driving through four different visual conditions under dark and foggy skies. Participants’ visual search behavior and driving behavior activities were recorded simultaneously. Experiment 2 determined whether training with a circular projection of three colors improved the driver’s behavior. The results showed that (1) central vision is critical to the driver, and the importance of peripheral vision can be directly measured using the VR device; (2) a clear middle and blurred peripheral vision not only improves driver behavior in foggy weather but also helps to improve attention and driving ability; and (3) the color projection training indicated that the green projection was more effective than the others and that it significantly improved (4) Novice drivers collected visual information mainly from their central vision and were less able to drive than veterans, but green projection improved their driving ability and reduced collisions. Most importantly, the study results provide a new visual training paradigm that can improve driver behavior on dark and foggy days, especially for female novices.

Adaptive behavior of intercity travelers within urban agglomeration in response to adverse weather: Accounting for multilayer unobserved heterogeneity

Travelers often change their behavior in reaction to adverse weather conditions. This paper seeks to conduct an empirical evaluation of both ordered and unordered discrete outcome frameworks for examining the adaptive behavior decisions of intercity travelers and investigate the varying effects of explanatory factors on different alternatives of adaptive behavior during adverse weather. The data from 754 respondents in the Beijing-Tianjin-Hebei urban agglomeration, China are collected by a two-phase survey instrument. To capture the unobserved heterogeneity more effectively, this paper develops three advanced models to investigate the variations in intercity travel behavior during adverse weather. Four alternative adaptive behaviors are determined as outcome variables: maintaining original travel plans, changing only the intercity mode, changing the departure date, and canceling the trip, while potential influencing factors including adverse weather conditions, trip-related characteristics, individual attributes and urban agglomeration attributes are statistically assessed. The results indicate that the unordered models consistently outperform their ordered counterparts, and the incorporation of multilayer heterogeneity enhances the model fit. Furthermore, significant factors and their coefficient values vary across the different adaptive behavior alternatives. Intercity travelers demonstrate a higher probability of changing departure dates or canceling trips during snowy and windy days compared to rainy and foggy days. Trains exhibit higher flexibility and reliability during adverse weather, and access attributes significantly affect intercity travel adaptive behavior. Additionally, the analysis of individual and urban agglomeration attributes uncovers variations in adaptive behavior among individuals and cities. These findings provide profound insights into the complexities and variations of intercity travel behavior during adverse weather, and propose practical strategies to mitigate the detrimental impacts of adverse weather on intercity travelers.

Influence of meteorological variables and air pollutants on fog/smog formation in seven major cities of Indo-Gangetic Plain.

The Indo-Gangetic Plains (IGP) of the Indian subcontinent during winters experience widespread fog episodes. The low visibility is not only attributed to meteorological conditions but also to the increased pollution levels in the region. The study was carried out for Tier 1 and Tier II cities of the IGP of India, including Kolkata, Amritsar, Patiala, Hisar, Delhi, Patna, and Lucknow. This work analyzes data from 1990 to 2023 (33years) employing the Mann-Kendall-Theil-Sen slope to determine the trends in fog occurrences and the relation between fog and meteorological parameters using multiple linear regressions. Furthermore, identifying the most relevant fog (visibility)-impacting factors from a set of both meteorological factors and air pollutants using step-wise regression. All cities indicated trend in the number of foggy days except for Kolkata. The multiple regression analysis reveals relatively low associations between fog occurrences and meteorological factors (30 to 59%), although the association was stronger when air pollution levels were considered (60 to 91%). Relative humidity, PM2.5, and PM10 have the most influence on fog formation. The study provides comprehensive insights into fog trends by incorporating meteorological data and air pollution analysis. The findings highlight the significance of acknowledging meteorological and pollution factors to understand and mitigate the impacts of reduced visibility. Hence, this information can guide policymakers, urban planners, and environmental management agencies in developing effective strategies to manage fog-related risks and improve air quality.

Main drivers of productivity of Acacia mangium plantations in Vietnam

Acacia mangium is one of the most important hardwood plantation species in the tropics. The question of how to grow Acacia productively and sustainably remains a major issue for the forestry sector in many tropical countries. We analyzed the productivity of A. mangium plantations across five contrasting ecological zones in Vietnam. These covered gradients in rainfall from 1750 mm to 3060 mm and a mean annual temperature range of 22 – 27 0C. Plantation productivity across these zones varied from an MAI of 6 m3 ha y−1 to 31 m3 ha y−1. A comprehensive suite of statistical methods was utilized for variable and model selection to minimize confounding and multicollinearity issues among predictors and identify determinants of plantation productivity using 27 biophysical variables. These variables encompass plantation age, climate parameters, as well as site and soil properties. Results showed that, apart from plantation age, the factors influencing plantation productivity and explaining a majority of variation in mean annual timber increment are soil organic carbon content and the number of foggy days. Soil carbon is probably an index of overall soil fertility and its significance reflects the fact that the plantations have often been established on relatively degraded sites. The lower productivity at sites having a higher frequency of foggy days (up to 57 days per year in some regions) may arise from lower solar radiation and temperature during fog. The results of this study can inform site selection and plantation management tools aimed at maximizing Acacia mangium productivity in Vietnam and other countries with similar ecological conditions. Planting A. mangium in high-intensity fog areas is not recommended. Further studies on the ecophysiological mechanisms of how fog influences the growth of tropical A. mangium plantations are also needed.

Video frame feeding approach for validating the performance of an object detection model in real-world conditions

The challenge of evaluating deep learning-based object detection models in complex traffic scenarios, characterized by changing weather and lighting conditions, is addressed in this study. Real-world testing proves time and cost-intensive, leading to the proposal of a Video Frame Feeding (VFF) approach as a solution. The proposed Video Frame Feeding approach acts as a bridge between object detection models and simulated environments, enabling the generation of realistic scenarios. Leveraging the CarMaker (CM) tool to simulate realistic scenarios, the framework utilizes a virtual camera to capture the simulated environment and feed video frames to an object identification model. The VFF algorithm, with automated validation using simulated ground truth data, enhances detection accuracy to over 95% at 30 frames per second within 130 meters. Employing the You Only Look Once (YOLO) version 4 and the German Traffic Sign Recognition Benchmark dataset, the study assesses a traffic signboard identification model across various climatic conditions. Notably, the VFF algorithm improves accuracy by 2% to 5% in challenging scenarios like foggy days and nights. This innovative approach not only identifies object detection issues efficiently but also offers a versatile solution applicable to any object detection model, promising improved dataset quality and robustness for enhanced model performance.

Variability in evapotranspiration in the Andean páramo as influenced by fog and rainfall

AbstractTropical mountains such as the páramos of the Andes, which serve as ‘water towers’ for local communities and downstream cities, are important areas for early detection of climate change. Here, fog and low‐intensity rainfall are very common and play a key role in ecohydrological processes. Although evapotranspiration (ET) represents an important part of the water cycle, how ET and fog processes interact and how they affect páramo vegetation and water resources availability are poorly understood. This study investigated the effects of foggy (fog only) and mixed (fog and rainfall) conditions on ET. To determine whether fog significantly reduces ET, we compared ET and meteorological data under these two conditions with those during dry days. We found that on foggy days, when fog was most prevalent in the early morning, ET declined on average by 4% and net radiation (Rn) by 9.2%. Under mixed conditions, daily ET declined by 42% and Rn by 33%. In the páramo, where mean annual precipitation and ET are 1210 and 635 mm, respectively, the estimated annual reduction in ET due to fog and rainfall combined is between 77 and 174 mm. We found that during fog and rainfall mixed conditions, solar radiation was reduced, consequently constraining the energy available for ET while sustaining high relative humidity, ultimately reducing water loss. Our findings, which suggest that the presence of fog and low‐intensity rainfall restricts water losses by evaporative demand, contribute to a better understanding of the ecohydrological importance of these water inputs in the Andes.

Achievement of high photoluminescence quantum yield orange-red phosphors by designing Eu2+ reduction environment for multi applications

The high photoluminescence quantum yield (PLQY) orange-red phosphor, which is of great significance for the application of phosphor-converted light-emitting diode (LED). Herein, the novel BaSrLu4O8:Eu2+ orange-red phosphor was synthesized, and the PLQY is enhanced from 30 % to 91 % by Al2O3 assisted sintering. Combined with the trap energy level analysis, it is considered that the addition of Al2O3 introduces new vacancies, which release electrons during high temperature sintering, creating a more favorable environment for the reduction of Eu3+, increasing the Eu2+ luminescence center in the matrix and improving the PLQY. The white LED device prepared with BaSrLu4O8:Eu2+ and Y3(Al,Ga)5O12:Ce3+ has a color rendering index of 84.4 at a low correlated color temperature of 4295 K. Meanwhile, the luminous brightness of the orange LED prepared by BaSrLu4O8:Eu2+ can maintain 89 % of the original light intensity in the simulated foggy day illumination experiment, which proves its excellent lighting prospect as an automobile steering lamp in foggy and smog extreme conditions. The results indicate that this work provides a new strategy for the reduction of Eu2+ while realizing the multi-application of phosphors.

A seven-year-based characterization of aerosol light scattering properties at a rural central European site

Our study investigates the temporal variability of aerosol light scattering properties measured at three wavelengths (total scattering coefficient, backscattering coefficient, Ångström scattering exponent, hemispheric backscattering ratio and asymmetry parameter) at the rural background site of the National Atmospheric Observatory Košetice over a period of over seven years (2012–2019). The influence of fog and cloudiness on aerosol light scattering properties (ASP), together with their comparison to particle number size distribution was also investigated. The overall medians of total scattering (σsp) and backscattering coefficient (σbsp), hemispheric backscattering ratio (b) and asymmetry parameter (g) measured at 550 nm and Ångström scattering exponent (SAE_450/700) were 35.20 Mm−1, 5.07 Mm−1, 0.14 and 0.57 and 1.82, respectively. σsp and g decreased by 2.05 Mm−1/year, and 0.009/year, respectively, whereas b increased by 0.004/year. The selected aerosol properties were strongly correlated with particle number, mass, area and volume concentrations in the 200–800 nm mode. A decrease in SAE_450/700 implies a shift towards larger particle sizes, and evolutions in b and g suggest a higher backscattering portion to total scattering. The highest σsp and σbsp levels were observed in the cold seasons together with higher concentrations of air pollutants (carbonaceous aerosol, SO2 and NOx). This could be explained by enhanced emissions from human activities such as domestic heating combined with the higher stability of the atmosphere (poor atmospheric dispersion). In the summer, decreases in particle size and higher rates of backscatter fraction were attributed to secondary organic aerosol (SOA) formation. The accumulation mode showed the strongest aerosol scattering potential, with SAE_450/700 values < 2. Aerosol light scattering was significantly higher during overcast and foggy days compared to fine, cloudy, partly cloudy and no-fog days, possibly due to the particle hygroscopic growth under high relative humidity (shift of particle size towards larger sizes), which in turn can lead to a lower aerosol backscattering fraction. Source identification linked the changes in ASP to the synoptic situation over Europe rather than to specific sources of pollution.

RETRACTED ARTICLE: Dual Siamese Anchor Points Adaptive Tracker with Transformer for RGBT Tracking

Due to environmental conditions, such as rainy days, foggy days, and dim lighting, objects in visible light images are not prominently displayed, leading to an easy loss of targets during tracking. In recent years, many RGB visible light trackers have achieved significant success in addressing visual tracking challenges. However, these trackers perform poorly when tracking targets under special conditions, such as occlusions and low-light scenarios. In contrast, objects in thermal infrared images are more distinct in poor lighting conditions. Given this characteristic, researchers have shown increased interest in the development of trackers that combine thermal infrared and visible light imagery. However, some mainstream RGBT (red–green–blue and thermal) algorithms, such as MANET and ADNET, are based on the anchor-based theory, requiring consideration of anchor box sizes and introducing a substantial number of hyperparameters. This can lead to suboptimal performance when tracking dynamically changing targets. Moreover, these models rely on convolutional neural networks for feature extraction, which have limitations in capturing global features. In this paper, we introduce a novel training network model called DAPAT, which combines the anchor-free concept with Transformer theory. DAPAT differs from previous models in several ways. Specifically, we have designed a straightforward model to extract precise global features from template and search images. We have also incorporated two enhancement modules into the model to improve template and search images of different sizes while suppressing the impact of non-target images. We employ a dual-stream feature fusion network to reduce the loss of image feature information due to feature correlation operations. Finally, we compare the performance of the tracking model proposed in this paper with some advanced RGBT trackers on three data sets (RGBT234, RGBT210, and GTOT). The test results demonstrate that our tracker exhibits improvements in robustness and success rate, among other performance aspects.

Object Detection for Aircraft Turnover Milestone based on Modified

In the target detection task of aircraft turnaround milestone in foggy scenario, there are some problems such as unstable location of prediction frame boundary, high error detection rate and poor detection effect of small target. A new target detection method BTM-YOLO (Broad-sighted upsample and three-dimensional attention multiple detection head YOLO) is proposed, which is based on YOLOv7 network. Add a small target detection head to improve the ability of small target detection; The up-sampling module OVRAFE is introduced to reduce the information loss in the up-sampling process. Replace CIoU with Median Wise IoU (MWIoU) to suppress the problem of poor sample swelling in data sets. The improved model makes up for the performance shortcomings of small target detection in foggy days, and the average detection accuracy on the real foggy day test set is 76.2%, which is 3.32% higher than that of the original model, basically meeting the task requirements.

Image Defogging Based on Regional Gradient Constrained Prior

Foggy days limit the functionality of outdoor surveillance systems. However, it is still a challenge for existing methods to maintain the uniformity of defogging between image regions with a similar depth of field and large differences in appearance. To address above problem, this article proposes a regional gradient constrained prior (RGCP) for defogging that uses the piecewise smoothing characteristic of the scene structure to achieve accurate estimation and reliable constraint of the transmission. RGCP first derives that when adjacent similar pixels in the fog image are aggregated and spatially divided into regions, clusters of region pixels in RGB space conform to a chi-square distribution. The offset of the confidence boundary of the clusters can be regarded as the initial transmission of each region. RGCP further uses a gradient distribution to distinguish different regional appearances and formulate an interregional constraint function to constrain the overestimation of the transmission in the flat region, thereby maintaining the consistency between the estimated transmission map and the depth map. The experimental results demonstrate that the proposed method can achieve natural defogging performance in terms of various foggy conditions.

Evaluation of histogram image defogging methods based on histogram equalization, dark channel prior, and convolutional neural network

Air pollution condition is getting worse with the advancement of society development, environmental pollution has gradually intensified, and smog frequently occurs in more and more cities. On foggy day, the saturation and contrast of an image could be low, and colors tend to drift and distortion. As a result, seeking a simple and effective image de-fogging technique is important for the subsequent research. In this study, three existing classical de-fogging algorithms are reproduced: histogram equalization, dark channel prior method, and convolutional neural network. The three de-fogging algorithms were compared respectively under the conditions of thin fog, thick fog, high brightness, and low brightness, so as to analyze their advantages and disadvantages. It is concluded that there is no obvious difference among the three algorithms in the de-fogging effect under the conditions of thick fog and high brightness, but relatively speaking, the de-fogging image generated by the dark channel prior is more real. When the fog is thin, the dark channel prior and convolutional neural network work better. Under the condition of low brightness, the histogram equalization has a better de-fogging effect.

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.

A foggy day in London dispersion town.

A foggy day in London dispersion town.