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Fog Research: A Review of Past Achievements and Future Perspectives
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.[PUBLICATION ABSTRACT]
Journal Article
From the mist
\"Mysterious mist horses have come to Pixie Hollow. Kate befriends one, but Silvermist finds an old legend that says they might be dangerous\"-- Provided by publisher.
Book
Fog and fauna of the Namib Desert: past and future
The future of fog‐dependent habitats under climate change is unknown but likely precarious; many have experienced recent declines in fog. Fog‐dependent deserts particularly will be threatened, because, there, fog can be the main water source for biota. We review the interactions between fog and fauna of the Namib Desert, about which there is 50 yr of research. We resynthesize the data, seeking patterns and mechanisms that could provide a framework for predicting outcomes of changes in fog regime in other fog‐dependent deserts. In the Namib, fog constitutes the most‐predictable form of free water. At least 48 Namib animal species consume free water from fog, or are likely to do so, employing both liquid and vapor phase. Fog also sustains plants that form the base for metabolic water production and wets the diet to provide pre‐formed water. So fog provides or underpins all the water intake of Namib fauna. Only a few species are active fog‐harvesters, though. Among Namib beetles, two species are unique in that they fog‐bask; they assume stereotyped postures in wind‐driven fog and droplets deposit on their carapaces. Some Namib beetle species construct surface ridges that trap fog water, which they consume. Some arthropods emerge from their subsurface habitats, or occupy its wet top layers, to access fog water, at times and in conditions outside their usual surface activity. Many more taxa, including vertebrates, use fog water opportunistically. They do not actively seek it out but use it when available. Acquiring fog water from droplets requires overcoming spherical surface tension so is possible only for animals heavier than ~100 mg. Smaller animals extract water from films or acquire it in the vapor phase. Some Namib animals use hygroscopic surfaces to extract vapor from unsaturated air, at ambient humidities attained in fog or sometimes between fogs. Rapid acquisition of water during episodic fog events creates problems for storage and osmoregulation, which some Namib animals have solved in enterprising ways, including long‐term internal storage of water and sequestering of osmolytes. Although not yet comprehensive, the body of research reviewed, and the principles that we have elucidated underlying fog usage, should inform future research on fauna throughout fog‐dependent deserts.
Journal Article
Fog Water: A General Review of Its Physical and Chemical Aspects
Studies concerning fog water have been rapidly increasing due to its negative impacts on different environmental processes. However, fog water harvesting has become beneficial in various countries to overcome water scarcity. Accurate fog forecasting remains a challenging issue due to its spatio-temporal variability and uncertainties despite the development and efforts made to understand its chemistry and microphysics. The literature proved that the decrease in fog frequency over time in most countries is mainly attributed to the improvement in air quality or the change in regional climatic conditions. The current fog review summarizes its different types and collectors, life cycle, and impacts, the effects of aerosols, and the latest results concerning its forecast challenges and frequency. It also highlights the major chemical processes along with the main field studies performed on fog water. The aim of this work is not to provide a criticism about fog but to present a general comprehensive review of its physical and chemical aspects covering up to 330 research and review papers aimed to serve as a basis for new challenges and findings about fog water.
Journal Article
Physical Processes Affecting Radiation Fog Based on WRF Simulations and Validation
The goal of this work is to assess the sensitivity of the Weather Research and Forecasting (WRF) model to various microphysical and land surface model parameterizations, as well as their effectiveness in capturing a radiation fog event that occurred on 15–16 February 2000 over Delhi in the Indo-Gangetic Plain (IGP). Fog forecasting using state-of-the-art mesoscale models is challenging due to limitations in understanding of atmosphere-land surface feedbacks and fog microphysics. Fog can have adverse effects because of low visibility on transport and aviation but affects positively agriculture and forestry by absorbing fog water. In India, the IGP is particularly susceptible to fog during the winter months of December, January, and February (DJF). To reach the goal in this work, preliminary investigation is carried out with five model experiments centered at Delhi for testing model sensitivity to nesting and gravitational settling of fog. The non-nested domain fares better at fog forecasting as compared to the nested domain. Accounting for the gravitational settling of fog droplets in the model further enhances model performance. Thereafter, to assess the model sensitivity to parameterization schemes, 40 model suite combinations with five microphysical (MP) schemes, two planetary boundary layer (PBL) schemes, and four land surface model (LSM) schemes are compared. The validations for fog formation, development, and dissipation are performed using observations collected over Safdarjung airport in Delhi. We conclude that the Thompson MP scheme, along with the Mellor-Yamada Nakanishi-Niino (MYNN 2.5) PBL and Rapid Update Cycle (RUC) LSM scheme, is able to capture the radiation fog event better than other schemes, and it is critical for evaluating the life cycle of radiation fog.
Journal Article
Fog Computing and the Internet of Things: A Review
With the rapid growth of Internet of Things (IoT) applications, the classic centralized cloud computing paradigm faces several challenges such as high latency, low capacity and network failure. To address these challenges, fog computing brings the cloud closer to IoT devices. The fog provides IoT data processing and storage locally at IoT devices instead of sending them to the cloud. In contrast to the cloud, the fog provides services with faster response and greater quality. Therefore, fog computing may be considered the best choice to enable the IoT to provide efficient and secure services for many IoT users. This paper presents the state-of-the-art of fog computing and its integration with the IoT by highlighting the benefits and implementation challenges. This review will also focus on the architecture of the fog and emerging IoT applications that will be improved by using the fog model. Finally, open issues and future research directions regarding fog computing and the IoT are discussed.
Journal Article
Fog Density Analysis Based on the Alignment of an Airport Video and Visibility Data
The density of fog is directly related to visibility and is one of the decision-making criteria for airport flight management and highway traffic management. Estimating fog density based on images and videos has been a popular research topic in recent years. However, the fog density estimated results based on images should be further evaluated and analyzed by combining weather information from other sensors. The data obtained by different sensors often need to be aligned in terms of time because of the difference in acquisition methods. In this paper, we propose a video and a visibility data alignment method based on temporal consistency for data alignment. After data alignment, the fog density estimation results based on images and videos can be analyzed, and the incorrect estimation results can be efficiently detected and corrected. The experimental results show that the new method effectively combines videos and visibility for fog density estimation.
Journal Article
Study of Stratus-Lowering Marine-Fog Events Observed During C-FOG
Two stratus-lowering marine fog events observed on 28 September and 4 October 2018 during the Coastal Fog (C-Fog) field campaign that took place offshore of eastern Canada from 1 September to 6 October 2018 are described. In situ, profiling, and remote sensing observations were made at selected land sites in eastern Newfoundland, Nova Scotia, and aboard the research vessel Hugh R. Sharp cruising in adjoining coastal waters. Synoptic-scale analysis shows that both fog episodes result from the interaction between synoptic-scale surface-level low-pressure systems and a contiguous high-pressure system. At the same time, back trajectories reveal that the bulk of the fog layer is formed due to differential advection. The diameter of the fog droplets at the surface gradually decreases from the centre of the fog layer to its leading/trailing edges. The bimodal fog-droplet diameter distribution with peaks at 5–10 µm and 20–25 µm provide clues on droplet collision and coalescence processes. The observed difference between microphysical variables and droplet distribution between the two fog events and within the same fog layer might be governed by the atmospheric-boundary-layer (e.g., humidity conditions and turbulence) that prevailed in the fog layer. Overall, it is concluded that the life cycle of observed stratus-lowering coastal-fog episodes depends on synoptic conditions and atmospheric-boundary-layer characteristics such as stability, cloud-top cooling, and entrainment.
Journal Article
Design and Enhancement of a Fog-Enabled Air Quality Monitoring and Prediction System: An Optimized Lightweight Deep Learning Model for a Smart Fog Environmental Gateway
Effective air quality monitoring and forecasting are essential for safeguarding public health, protecting the environment, and promoting sustainable development in smart cities. Conventional systems are cloud-based, incur high costs, lack accurate Deep Learning (DL)models for multi-step forecasting, and fail to optimize DL models for fog nodes. To address these challenges, this paper proposes a Fog-enabled Air Quality Monitoring and Prediction (FAQMP) system by integrating the Internet of Things (IoT), Fog Computing (FC), Low-Power Wide-Area Networks (LPWANs), and Deep Learning (DL) for improved accuracy and efficiency in monitoring and forecasting air quality levels. The three-layered FAQMP system includes a low-cost Air Quality Monitoring (AQM) node transmitting data via LoRa to the Fog Computing layer and then the cloud layer for complex processing. The Smart Fog Environmental Gateway (SFEG) in the FC layer introduces efficient Fog Intelligence by employing an optimized lightweight DL-based Sequence-to-Sequence (Seq2Seq) Gated Recurrent Unit (GRU) attention model, enabling real-time processing, accurate forecasting, and timely warnings of dangerous AQI levels while optimizing fog resource usage. Initially, the Seq2Seq GRU Attention model, validated for multi-step forecasting, outperformed the state-of-the-art DL methods with an average RMSE of 5.5576, MAE of 3.4975, MAPE of 19.1991%, R2 of 0.6926, and Theil’s U1 of 0.1325. This model is then made lightweight and optimized using post-training quantization (PTQ), specifically dynamic range quantization, which reduced the model size to less than a quarter of the original, improved execution time by 81.53% while maintaining forecast accuracy. This optimization enables efficient deployment on resource-constrained fog nodes like SFEG by balancing performance and computational efficiency, thereby enhancing the effectiveness of the FAQMP system through efficient Fog Intelligence. The FAQMP system, supported by the EnviroWeb application, provides real-time AQI updates, forecasts, and alerts, aiding the government in proactively addressing pollution concerns, maintaining air quality standards, and fostering a healthier and more sustainable environment.
Journal Article