Development Of Fog Research Articles

On the Conditions for Onset and Development of Fog Over New Delhi: An Observational Study from the WiFEX

On the Conditions for Onset and Development of Fog Over New Delhi: An Observational Study from the WiFEX

Intelligent Control and Security of Fog Resources in Healthcare Systems via a Cognitive Fog Model

There have been significant advances in the field of Internet of Things (IoT) recently, which have not always considered security or data security concerns: A high degree of security is required when considering the sharing of medical data over networks. In most IoT-based systems, especially those within smart-homes and smart-cities, there is a bridging point (fog computing) between a sensor network and the Internet which often just performs basic functions such as translating between the protocols used in the Internet and sensor networks, as well as small amounts of data processing. The fog nodes can have useful knowledge and potential for constructive security and control over both the sensor network and the data transmitted over the Internet. Smart healthcare services utilise such networks of IoT systems. It is therefore vital that medical data emanating from IoT systems is highly secure, to prevent fraudulent use, whilst maintaining quality of service providing assured, verified and complete data. In this article, we examine the development of a Cognitive Fog (CF) model, for secure, smart healthcare services, that is able to make decisions such as opting-in and opting-out from running processes and invoking new processes when required, and providing security for the operational processes within the fog system. Overall, the proposed ensemble security model performed better in terms of Accuracy Rate, Detection Rate, and a lower False Positive Rate (standard intrusion detection measurements) than three base classifiers (K-NN, DBSCAN, and DT) using a standard security dataset (NSL-KDD).

Is a more physical representation of aerosol activation needed for simulations of fog?

Abstract. Aerosols play a crucial role in the fog life cycle, as they determine the droplet number concentration and hence droplet size, which in turn controls both the fog's optical thickness and lifespan. Detailed aerosol-microphysics schemes which accurately represent droplet formation and growth are unsuitable for weather forecasting and climate models, as the computational power required to calculate droplet formation would dominate the treatment of the rest of the physics in the model. A simple method to account for droplet formation is the use of an aerosol activation scheme, which parameterises the droplet number concentration based on a change in supersaturation at a given time. Traditionally, aerosol activation parameterisation schemes were designed for convective clouds and assume that supersaturation is reached through adiabatic lifting, with many imposing a minimum vertical velocity (e.g. 0.1 m s−1) to account for the unresolved subgrid ascent. In radiation fog, the measured updraughts during initial formation are often insignificant, with radiative cooling being the dominant process leading to saturation. As a result, there is a risk that many aerosol activation schemes will overpredict the initial fog droplet number concentration, which in turn may result in the fog transitioning to an optically thick layer too rapidly. This paper presents a more physically based aerosol activation scheme that can account for a change in saturation due to non-adiabatic processes. Using an offline model, our results show that the equivalent cooling rate associated with the minimum updraught velocity threshold assumption can overpredict the droplet number by up to 70 % in comparison to a typical cooling rate found in fog formation. The new scheme has been implemented in the Met Office Natural Environment Research Council (NERC) Cloud (MONC) large eddy simulation (LES) model and tested using observations of a radiation fog case study based in Cardington, UK. The results in this work show that using a more physically based method of aerosol activation leads to the calculation of a more appropriate cloud droplet number. As a result, there is a slower transition to an optically thick (well-mixed) fog that is more in line with observations. The results shown in this paper demonstrate the importance of aerosol activation representation in fog modelling and the impact that the cloud droplet number has on processes linked to the formation and development of radiation fog. Unlike the previous parameterisation for aerosol activation, the revised scheme is suitable to simulate aerosol activation in both fog and convective cloud regimes.

The Shallowing Surface Temperature Inversions in the Arctic

AbstractTemperature inversions play an important role in various physical processes by affecting the atmospheric stability, regulating the development of clouds and fog, and controlling the transport of heat and moisture fluxes. In the past few decades, previous studies have analyzed the spatiotemporal variability of Arctic inversions, but few studies have investigated changes in temperature inversions. In this study, the changes in the depth of Arctic inversions in the mid-twenty-first century are projected based on a 30-member ensemble from the Community Earth System Model Large Ensemble (CESM-LE) project. The ERA-Interim, JRA-55, and NCEP–NCAR reanalysis were employed to verify the model results. The CESM-LE can adequately reproduce the spatial distribution and trends of present-day inversion depth in the Arctic, and the simulation is better in winter. The mean inversion depth in the CESM-LE is slightly underestimated, and the discrepancy is less than 11 hPa, within a reasonable range. The model results show that during the mid-twenty-first century, the inversion depth will strongly decrease in autumn and slightly decrease in winter. The shallowing of the inversion is most obvious over the Arctic Ocean, and the maximum decrease is over 65 hPa in the Pacific sector in autumn. In contrast, the largest decrease in the inversion depth, which is more than 45 hPa, occurs over the Barents Sea in winter. Moreover, the area where the inversion shallows is consistent with the area where the sea ice is retreating, indicating that the inversion depth over the Arctic Ocean in autumn and winter is likely regulated by the sea ice extent through modulating surface heat fluxes.

BPAF: Blockchain-Enabled Reliable and Privacy-Preserving Authentication for Fog-Based IoT Devices

The development of IoT and fog computing promotes various kinds of authentication mechanisms for IoT devices. Traditional IoT authentication schemes are based on Public Key Infrastructure (PKI) where a centralized certificate authority is introduced. To mitigate the security, privacy, and reliability issues bring from the centralization, some blockchain-based authentication schemes have been presented to achieve decentralized authentication. Unfortunately, they cannot be directly used under the fog-based IoT environment, which consists of resource-constrained IoT devices. To mitigate these issues, we present a Blockchain-enabled reliable, and Privacy-preserving Authentication for Fog-based IoT devices, named BPAF. BPAF achieves reliable authentication of fog nodes without violating the privacy of authenticated users during the authentication process. Security analysis and experimental evaluations show that BPAF achieves privacy-preserving and reliable authentication with high efficiency for both the fog nodes and full nodes participating in the authentication process.

Analysis of Meteorological Conditions for a Sea Fog Process in 2016

The visibility characteristics and meteorological conditions of a sea fog process on 27th February, 2016 are analyzed and the heavy fog process is simulated by the Weather Research and Forecasting (WRF) model in this paper. The forecast results show that the visibility in Qingdao coastal area is light fog on the night of the 26th. In the WRF simulation, it can be observed from the sea-level pressure that the wind direction of Qingdao and the coastal area turned southerly with the eastward movement of the low pressure system on surface from 1200 UTC to 1800 UTC on the 26th. A large amount of water vapor brought by easterly and southerly wind provides sufficient water vapor conditions for the formation and development of the sea fog. At 975 hPa, there is a strong warm tongue over Shandong Peninsula at 0600 UTC on the 26th, while the offshore is affected by the cold tongue, where the horizontal temperature gradient is large and there is a strong baroclinicity. At 850 hPa, there is a weak warm ridge over Qingdao at 1200 UTC on the 26th, which means that it is an inversion layer, which is conducive to the maintenance of fog.

A distributed ensemble design based intrusion detection system using fog computing to protect the internet of things networks

With the development of internet of things (IoT), capabilities of computing, networking infrastructure, storage of data and management have come very close to the edge of networks. This has accelerated the necessity of Fog computing paradigm. Due to availability of Internet, most of our business operations are integrated with IoT platform. Fog computing has enhanced the strategy of collecting and processing, huge amount of data. On the other hand, attacks and malicious activities has adverse consequences on the development of IoT, Fog, and cloud computing. This has led to development of many security models using fog computing to protect IoT network. Therefore, for dynamic and highly scalable IoT environment, a distributed architecture based intrusion detection system (IDS) is required that can distribute the existing centralized computing to local fog nodes and can efficiently detect modern IoT attacks. This paper proposes a novel distributed ensemble design based IDS using Fog computing, which combines k-nearest neighbors, XGBoost, and Gaussian naive Bayes as first-level individual learners. At second-level, the prediction results obtained from first level is used by Random Forest for final classification. Most of the existing proposals are tested using KDD99 or NSL-KDD dataset. However, these datasets are obsolete and lack modern IoT-based attacks. In this paper, UNSW-NB15 and actual IoT-based dataset namely, DS2OS are used for verifying the effectiveness of the proposed system. The experimental result revealed that the proposed distributed IDS with UNSW-NB15 can achieve higher detection rate upto 71.18% for Backdoor, 68.98% for Analysis, 92.25% for Reconnaissance and 85.42% for DoS attacks. Similarly, with DS2OS dataset, detection rate is upto 99.99% for most of the attack vectors.

Determinants of fog and low stratus occurrence in continental central Europe – a quantitative satellite-based evaluation

The formation and development of fog and low stratus clouds (FLS) depend on meteorological and land surface conditions and their interactions with each other. While analyses of temporal and spatial patterns of FLS in Europe exist, the interactions between FLS determinants underlying them have not been studied explicitly and quantitatively at a continental scale yet. In this study, a state-of-the-art machine learning technique is applied to model FLS occurrence over continental Europe, using meteorological and land surface parameters from geostationary satellite and reanalysis data. Spatially explicit model units are created to test for spatial and seasonal differences in model performance and FLS sensitivities to changes in predictors, and effects of different data preprocessing procedures are evaluated. The statistical models show good performance in predicting FLS occurrence during validation, with R2>0.9 especially in winter high pressure situations.The predictive skill of the models seems to be dependent on data availability, data preprocessing, time period, and geographic characteristics. It is shown that atmospheric proxies are more important determinants of FLS presence than surface characteristics, in particular mean sea level pressure, near-surface wind speed and evapotranspiration are crucial, together with FLS occurrence on the previous day. Higher wind speeds, higher land surface temperatures and higher evapotranspiration tend to be negatively related to FLS. Spatial patterns of feature importance show the dominant influence of mean sea level pressure on FLS occurrence throughout the central European domain. When only high pressure situations are considered, wind speed (in the western study region) and evapotranspiration (in the eastern study region) gain importance, highlighting the influence of moisture advection on FLS occurrence in the western parts of the central European domain. This study shows that FLS occurrence can be accurately modeled using machine learning techniques in large spatial domains based on meteorological and land surface predictors. The statistical models used in this study provide a novel analysis tool for investigating empirical relationships in the FLS – land surface system and possibly infer processes.

On the Investigation of the Typology of Fog Events in an Arid Environment and the Link with Climate Patterns

AbstractThe central aim of this work is to investigate the characteristics of fog events over the United Arab Emirates (UAE) and identify the underlying physical processes responsible for fog initiation and dissipation. To achieve this, hourly meteorological measurements at eight airport stations, along with ERA5 reanalysis data (1995–2018), are utilized. The analysis indicates the dominance of radiation fog (RAD) as, on average, 70% of the observed events fall under this category. Fog in the UAE typically forms between 2000 and 0200 local time (LT) and dissipates between 0600 and 0900 LT. During a typical dense fog event recorded during 22–23 December 2017, cooling and moistening tendencies of up to 1.2 K h−1 and 0.7 g kg−1 h−1 are observed ~5–6 h before fog onset. In the vertical, a dry and warm layer above 750 hPa gradually descends from above 500 hPa to promote the development of fog. Similar conclusions are reached when analyzing composites of fog events. Further, the variability of fog occurrence associated with El Niño–Southern Oscillation (ENSO) patterns is explored. It is concluded that the El Niño (warm) and La Niña (cold) phases exhibit very different spatial characteristics with respect to surface meteorological variables. In particular, during El Niño events, the near-surface atmosphere is cooler and moister compared to La Niña events, favoring RAD fog formation over the UAE. Besides, fog events during El Niño years tend to last longer compared to La Niña years due to an earlier onset.

Nature-inspired design of conical array for continuous and efficient fog collection application

By taking inspiration from the cactus plant, which collects water from the fog with the aid of cone-like, spines a continuous and efficient fog collecting structure can be designed. The optimization study is conducted for the development of smart fog collecting structures. The conical structures were made with 3D printing technology due to its number of advantages. The effect of the structure dimensions, such as cone tip angle (θ), cone height (H), and the separation distance (S) between the cones, on its water collection efficiency is analyzed. Furthermore, the effect of the humidifier separation distance (d) with the substrate, the impact of cone inclination angle (α) with the flowing fog, and the effect of surface wettability on water collection efficiency are also observed in this work. Results reveal that water collection efficiency is highest when the cones are parallel and near to the flowing fog.

Towards a Better Representation of Fog Microphysics in Large-Eddy Simulations Based on an Embedded Lagrangian Cloud Model

The development of radiation fog is influenced by multiple physical processes such as radiative cooling and heating, turbulent mixing, and microphysics, which interact on different spatial and temporal scales with one another. Once a fog layer has formed, the number of fog droplets and their size distribution have a particularly large impact on the development of the fog layer due to their feedback on gravitational settling and radiative cooling at the fog top, which are key processes for fog. However, most models do not represent microphysical processes explicitly, or parameterize them rather crudely. In this study we simulate a deep radiation fog case with a coupled large-eddy simulation (LES)–Lagrangian cloud model (LCM) approach for the first time. By simulating several hundred million fog droplets as Lagrangian particles explicitly (using the so-called superdroplet approach), we include a size-resolved diffusional growth including Köhler theory and gravitational sedimentation representation. The results are compared against simulations using a state of the art bulk microphysics model (BCM). We simulate two different aerosol backgrounds (pristine and polluted) with each microphysics scheme. The simulations show that both schemes generally capture the key features of the deep fog event, but also that there are significant differences: the drop size distribution produced by the LCM is broader during the formation and dissipation phase than in the BCM. The LCM simulations suggest that its spectral shape, which is fixed in BCMs, exhibits distinct changes during the fog life cycle, which cannot be taken into account in BCMs. The picture of the overall fog droplet number concentration is twofold: For both aerosol environments, the LCM shows lower concentrations of larger fog droplets, while we observe a higher number of small droplets and swollen aerosols reducing the visibility earlier than in the BCM. As a result of the different model formulation we observe higher sedimentation rates and lower liquid water paths for the LCM. The present work demonstrates that it is possible to simulate fog with the computational demanding approach of LCMs to assess the advantages of high-resolution cloud models and further to estimate errors of traditional parameterizations.

Study QoS Optimization and Energy Saving Techniques in Cloud, Fog, Edge, and IoT

With an increase of service users’ demands on high quality of services (QoS), more and more efficient service computing models are proposed. The development of cloud computing, fog computing, and edge computing brings a number of challenges, e.g., QoS optimization and energy saving. We do a comprehensive survey on QoS optimization and energy saving in cloud computing, fog computing, edge computing, and IoT environments. We summarize the main challenges and analyze corresponding solutions proposed by existing works. This survey aims to help readers have a deeper understanding on the concepts of different computing models and study the techniques of QoS optimization and energy saving in these models.

PM2.5 vertical variation during a fog episode in a rural area of the Yangtze River Delta, China

A dense radiation fog event occurred at the Shouxian site, Anhui Province, China, from the evening of January 2 to noon on January 3, 2017. During this event, vertical profiles of particulate matter (PM) and meteorological parameters within the lower troposphere (0–1000 m) were collected using a tethered balloon. This study assessed the evolution of the PM2.5 profile with the planetary boundary layer (PBL) structure and the effects of fog on the PM2.5 concentration. The results showed the following: (1) At the surface, the average diurnal variation in Aitken mode, accumulation mode and coarse mode particles had bimodal patterns before fog formation and was mainly influenced by diurnal variation in the mixing level depth (MLD). The aerosol number concentrations decreased remarkably, and the PM2.5 was strongly scavenged from 150 μg/m3 to 45 μg/m3 during the fog process. (2) In the vertical direction, the PM2.5 distribution was affected by the PBL height and the vertical fog structure. At 05:00 LT (local time) (i.e., early morning before the fog event), the PM2.5 concentration was slightly higher in the stable layer (260 μg/m3) than in the residual layer (200 μg/m3). At 14:00 LT (haze period), PM2.5 was well mixed below 500 m, with a concentration of 310 μg/m3. After 20:00 LT, when fog formed, PM2.5 was scavenged from the surface to the upper layers, and the scavenging height was controlled by the fog top height. (3) The vertical development of fog was promoted by turbulent mixing and radiation cooling at the fog top. Turbulent mixing enhanced the particle scavenging efficiency of fog droplets by the collision-coalescence process. The PM2.5 scavenging height was corresponded to the turbulence height. Therefore, turbulence development in the fog was the essential dynamic factor driving PM2.5 reduction.

On the Formation and Development of Radiation Fog: An Observational Study

Observations from the Local and Non-local Fog EXperiment field campaign are used to investigate the formation and initial development of radiation fog. Data from six locations have been selected: five sites in Shropshire, and one in Bedfordshire, UK. These data show that during night-time clear-sky conditions, thermally-stable radiation fog only forms when the vertical velocity variance near the ground (at 2 m) decreases to 0.002–0.005 m2 s−2 or less. The variation in this threshold appears to be a function of the vertical relative humidity gradient near the ground. When the vertical velocity variance exceeds these values, saturation may still occur near the ground, but fog formation is inhibited due to the more efficient transport of moisture to the ground in the form of dew. Furthermore, once stable radiation fog forms, any increase in the vertical velocity variance to values greater than 0.002–0.005 m2 s−2 may cause the fog to dissipate. The subsequent development of shallow stable fog into deeper, weakly-unstable fog is also examined. Possible mechanisms for this are discussed, including in situ processes and non-local larger scale processes. Clear evidence of the latter is presented, but the relative importance of in situ and non-local processes for fog deepening requires further clarification.

Content Recommendation Algorithm for Intelligent Navigator in Fog Computing Based IoT Environment

With the development of the Internet and mobile technologies, the Internet of Things (IoT) era has arrived. Vehicle networking technology can not only facilitate people’s travel but also effectively alleviate traffic congestion. The development of fog computing technology provides unlimited possibilities for the Internet of Vehicles (IoV). Intelligent navigator is a very important part of human–computer interaction in IoV. It carries a large number of tasks of recommending content for users. In order to get more accurate recommendation content, we propose a weighted interest degree recommendation algorithm using association rules for intelligence in the IoV. First, the user data are analyzed to establish the association rule mining algorithm. Second, the user interest score is predicted by analyzing the relevance between user interests to recommend personalized service for the user. From the simulation results, we can see that the proposed algorithm can achieve higher recommendation accuracy.

Faster fog-aided private set intersectionwith integrity preserving

Private set intersection (PSI) allows two parties to compute the intersection of their private sets while revealing nothing except the intersection. With the development of fog computing, the need has arisen to delegate PSI on outsourced datasets to the fog. However, the existing PSI schemes are based on either fully homomorphic encryption (FHE) or pairing computation. To the best of our knowledge, FHE and pairing operations consume a huge amount of computational resource. It is therefore an untenable scenario for resource-limited clients to carry out these operations. Furthermore, these PSI schemes cannot be applied to fog computing due to some inherent problems such as unacceptable latency and lack of mobility support. To resolve this problem, we first propose a novel primitive called “faster fog-aided private set intersection with integrity preserving”, where the fog conducts delegated intersection operations over encrypted data without the decryption capacity. One of our technical highlights is to reduce the computation cost greatly by eliminating the FHE and pairing computation. Then we present a concrete construction and prove its security required under some cryptographic assumptions. Finally, we make a detailed theoretical analysis and simulation, and compare the results with those of the state-of-the-art schemes in two respects: communication overhead and computation overhead. The theoretical analysis and simulation show that our scheme is more efficient and practical.

LANFEX: A Field and Modeling Study to Improve Our Understanding and Forecasting of Radiation Fog

AbstractFog is a high-impact weather phenomenon affecting human activity, including aviation, transport, and health. Its prediction is a longstanding issue for weather forecast models. The success of a forecast depends on complex interactions among various meteorological and topographical parameters; even very small changes in some of these can determine the difference between thick fog and good visibility. This makes prediction of fog one of the most challenging goals for numerical weather prediction. The Local and Nonlocal Fog Experiment (LANFEX) is an attempt to improve our understanding of radiation fog formation through a combined field and numerical study. The 18-month field trial was deployed in the United Kingdom with an extensive range of equipment, including some novel measurements (e.g., dew measurement and thermal imaging). In a hilly area we instrumented flux towers in four adjacent valleys to observe the evolution of similar, but crucially different, meteorological conditions at the different sites. We correlated these with the formation and evolution of fog. The results indicate new quantitative insight into the subtle turbulent conditions required for the formation of radiation fog within a stable boundary layer. Modeling studies have also been conducted, concentrating on high-resolution forecast models and research models from 1.5-km to 100-m resolution. Early results show that models with a resolution of around 100 m are capable of reproducing the local-scale variability that can lead to the onset and development of radiation fog, and also have identified deficiencies in aerosol activation, turbulence, and cloud micro- and macrophysics, in model parameterizations.

In this issue of Weather

This month, we focus on two papers that follow on from excellent presentations made at the 2017 Student Conference. The first of these, by James Warner, discusses the teleconnection between Arctic sea ice and the North Atlantic Oscillation. In ‘Arctic sea ice – a driver of the winter NAO’, beginning on p. 307, the effects of ice on the pressure systems of the Atlantic region are explored, showing that there is a link between the two and that further exploration of this might allow us to improve seasonal forecasts for winter.The second of these papers is a refinement of our knowledge of fog. In ‘Numerical modelling of the evolution of the boundary layer during a radiation fog event’ on p. 310, D K E Smith, I A Renfrew, J D Price and S R Dorling look at how well we are able to model the development of fog that forms under predominantly clear skies, the difficulties imposed by our lack of knowledge of some aspects of the process and how we might better predict its formation.On p. 322, Dani V and B K Pal explore ‘Climate variability and changes in local climate’ focussing on Angul (India). Here they identify trends in climatic variables consistent with a warming globe.The second in our series of short introductory papers on climate change appears on p. 332.The persistence or absence of snow on British mountains is also a well‐established marker of the weather and climate here. In the 8th of the series on p. 333: ‘Snow patches in England and Wales during spring and summer 2018’, Iain Cameron reveals that snow‐melt dates in 2018 were broadly in line with the mean, even though there was considerable snowfall across much of Britain in early spring.On p. 335, Prof. Stephen Belcher and Prof. Sir Brian Hoskins write about climate change and the summer just gone in ‘Summer temperatures 2018 – the “new normal”?’I encourage members to vote for those whose work with and for the Royal Meteorological Society deserves recognition through one of the Society's awards. And finally, please complete the Weather journal survey supplied with this issue (also available at https://www.rmets.org/news/weather‐journal‐survey). We want to know your views on the journal, so that the Editorial Board and I can guide its development in 2019 and beyond.

Data-Driven Design of Fog-Computing-Aided Process Monitoring System for Large-Scale Industrial Processes

Stimulated by the recent development of fog computing technology, in this paper, a fog-computing-aided process monitoring and control architecture is proposed for large-scale industrial processes, which enables reliable and efficient online performance optimization in each fog computing node without modifying predesigned control subsystems. Moreover, a closed-loop data-driven method is developed for the process monitoring system design and an adaptive configuration approach is proposed to deal with the problems caused by the changes of process parameters and operating points. The feasibility and effectiveness of the proposed design approaches are verified and demonstrated through the case study on the Tennessee Eastman benchmark system.

Modeling the Evolution and Life Cycle of Radiative Cold Pools and Fog

Abstract Despite an increased understanding of the physical processes involved, forecasting radiative cold pools and their associated meteorological phenomena (e.g., fog and freezing rain) remains a challenging problem in mesoscale models. The present study is focused on California’s tule fog where the Weather Research and Forecasting (WRF) Model’s frequent inability to forecast these events is addressed and substantially improved. Specifically, this was accomplished with four major changes from a commonly employed, default configuration. First, horizontal model diffusion and numerical filtering along terrain slopes was deactivated (or mitigated) since it is unphysical and can completely prevent the development of fog. However, this often resulted in unrealistically persistent foggy boundary layers that failed to lift. Next, changes specific to the Yonsei University (YSU) planetary boundary layer (PBL) scheme were adopted that include using the ice–liquid-water potential temperature to determine vertical stability, a reversed eddy mixing K profile to represent the consequences of negatively buoyant thermals originating near the fog (PBL) top, and an additional entrainment term to account for the turbulence generated by cloud-top (radiative and evaporative) cooling. While other changes will be discussed, it is these modifications that create, to a sizable degree, marked improvements in modeling the evolution and life cycle of fog, low stratus clouds, and adiabatic cold pools.