Dense Fog Research Articles

Driving through dense fog: a study of the effects and control of sustainable public procurement of electric cars

Governments are large buyers of vehicles, thus contributing to pollution. To promote sustainability, policies have been shaped to replace government-owned fossil fuel cars with electric cars. Public procurement is seen as a strategic tool for the government to transition. This study identifies a research gap due to a lack of studies on how stakeholders at different levels identify and calculate the sustainability effects of public procurement of cars. Our approach uses a multilevel perspective to explore how various stakeholders perceive and assess the effects of sustainable public procurement. The data were obtained through a qualitative research design with documents and semi-structured interviews with stakeholders in Norway ranging from government agencies, public procurement officers, car suppliers, and end-users. (End-users in this setting are the ones who ultimately use the vehicles). The study’s findings are two-fold. First, it contributes to understanding that perceived effects of sustainable public procurement vary from the stakeholders' perspectives and that public procurement initiatives perceive to have cultural effects in addition to innovation, environmental, economic, and social impacts. Second, it contributes to understanding the importance of feedback mechanisms in public procurement to align the assessment of the effects. A better understanding of how effects are identified, and improved feedback mechanisms could help government representatives control the procurement system and accomplish the intended effects.

Correction to: WRF sensitivity simulations of a dense advection fog event in Istanbul

Correction to: WRF sensitivity simulations of a dense advection fog event in Istanbul

Application of a ground-based microwave radiometer in aviation weather forecasting in Indian Air Force

Time and intensity-specific very short-term forecasting or nowcasting is the biggest challenge faced by an aviation meteorologist. Ground-based microwave radiometer (MWR) has been used for nowcasting convective activity and it was established that there is a good comparison between thermodynamic parameters derived from MWR and GPS radiosonde observations, indicating that MWR observations can be used to develop techniques for nowcasting severe convective activity. In this study, efforts have been made to bring out the efficacy of MWR in nowcasting thunderstorms and fog. Firstly, the observations of MWR located at Palam, New Delhi, India have been compared with the nearest radiosonde data to ascertain the variation in respective profiles. Large differences were found in relative humidity (RH), whereas temperatures from MWR were found to be close to radiosonde observed temperature up to 3.5 km. Subsequently, the scatter plots and correlation coefficients of thermodynamic indices/parameters indicated that most of the parameters are either not correlated or have moderate correlation only for 12:00 UTC profiles. The superepoch technique of lagged composite for various thermodynamic indices/parameters to obtain a combined picture of all the thunderstorm and dense fog cases on the time series could not determine any pattern to predict thunderstorm and dense fog with lead time of 2-4 hours. MWR profile for a case of occurrence of thunderstorm was analyzed. No significant variation was observed in most of the indices (as calculated from MWR observed parameters) prior to the occurrence of thunderstorm. RH at freezing level and between 950 and 700 hPa levels were the only parameters, which increased four hours prior to the occurrence.

Weak Incoherent Optical Signal Amplification Based on Modulation Instability for Imaging Through Fog

Weak optical signal processing based on nonlinear effects offers new approaches for imaging through scattering media. A novel incoherent optical signal amplification method based on spatial modulation instability is proposed for imaging through fog. We experimentally demonstrated the amplification and recovery of degraded weak incoherent optical image signals after passing through dense fog in a photorefractive crystal. Our experimental results indicate that the intensity profiles of the output images can be redistributed from disordered to ordered when the nonlinear strength exceeds the threshold of incoherent modulation instability, which shows that the partially disordered incoherent probe light intensities are orderly transferred to enhance the signal intensity profiles and the residuals become a uniform background. The restored nonlinear output images with high visibility were observed for a proper optical thickness of fog, and weak optical imaging from undetectable to detectable with relatively poor visibility for a larger optical thickness was also realized in the experiment. This incoherent optical signal amplification method based on modulation instability has a potential application for image recovery in atmospheric scattering imagings.

RadarSLAM: A robust simultaneous localization and mapping system for all weather conditions

A Simultaneous Localization and Mapping (SLAM) system must be robust to support long-term mobile vehicle and robot applications. However, camera and LiDAR based SLAM systems can be fragile when facing challenging illumination or weather conditions which degrade the utility of imagery and point cloud data. Radar, whose operating electromagnetic spectrum is less affected by environmental changes, is promising although its distinct sensor model and noise characteristics bring open challenges when being exploited for SLAM. This paper studies the use of a Frequency Modulated Continuous Wave radar for SLAM in large-scale outdoor environments. We propose a full radar SLAM system, including a novel radar motion estimation algorithm that leverages radar geometry for reliable feature tracking. It also optimally compensates motion distortion and estimates pose by joint optimization. Its loop closure component is designed to be simple yet efficient for radar imagery by capturing and exploiting structural information of the surrounding environment. Extensive experiments on three public radar datasets, ranging from city streets and residential areas to countryside and highways, show competitive accuracy and reliability performance of the proposed radar SLAM system compared to the state-of-the-art LiDAR, vision and radar methods. The results show that our system is technically viable in achieving reliable SLAM in extreme weather conditions on the RADIATE Dataset, for example, heavy snow and dense fog, demonstrating the promising potential of using radar for all-weather localization and mapping.

The 10th anniversary of the scientific description of the black snub-nosed monkey (Rhinopithecus strykeri): It is time to initiate a set of new management strategies to save this critically endangered primate from extinction.

Traditionally, the genus Rhinopithecus (Milne-Edwards, 1872, Primates, Colobinae) included four allopatric species, restricted in their distributions to China and Vietnam. In 2010, a fifth species, the black snub-nosed monkey (Rhinopithecus strykeri) was discovered in the Gaoligong Mountains located on the border between China and Myanmar. Despite the remoteness, complex mountainous terrain, dense fog, and armed conflict that characterizes this region, over this past decade Chinese and Myanmar scientists have begun to collect quantitative data on the ecology, behavior and conservation requirements of R. strykeri. In this article, we review the existing data and present new information on the life history, ecology, and population size of R. strykeri. We discuss these data in the context of past and current conservation challenges faced by R. strykeri, and propose a series of both short-term and long-term management actions to ensure the survival of this Critically Endangered primate species. Specifically, we recommend that the governments and stakeholders in China and Myanmar formulate a transboundary conservation agreement that includes a consensus on bilateral exchange mechanisms, scientific research and monitoring goals, local community development, cooperation to prevent the hunting of endangered species and cross-border forest fires. These actions will contribute to the long-term conservation and survival of this Critically Endangered species.

Dehazing Based on Long-Range Dependence of Foggy Images

Deep neural networks (DNNs) with long-range dependence (LRD) have attracted more and more attention recently. However, LRD of DNNs is proposed from the view on gradient disappearance in training, which lacks theory analysis. In order to prove LRD of foggy images, the Hurst parameters of over 1,000 foggy images in SOTS are computed and discussed. Then, the Residual Dense Block Group (RDBG), which has additional long skips among two Residual Dense Blocks to fit LRD of foggy images, is proposed. The Residual Dense Block Group can significantly improve the details of dehazing image in dense fog and reduce the artifacts of dehazing image.

WRF sensitivity simulations of a dense advection fog event in Istanbul

WRF sensitivity simulations of a dense advection fog event in Istanbul

Joint Occurrence of Heavy PM2.5 Pollution Episodes and Persistent Foggy Days in Central East China

Although many severe pollution events in Central and East China have been analyzed in recent years, the heavy PM2.5 pollution episode happened on persistent foggy days from January 13 to 18, 2018 was unique, characterized by explosive increase and sharp decrease in PM2.5 (particles with kinetic equivalent diameter less than or equal to 2.5 microns) concentration. Based on hourly data of ground level meteorological parameters, PM2.5 data and CALIPSO-based (the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) aerosol data, combined with ECMWF (European Centre for Medium-Range Weather Forecasts) reanalysis data and radiosonde temperature profile, a comprehensive analysis was conducted to reveal the meteorological reasons for the evolution of the episode at horizontal and vertical scales. The PM2.5 concentration experienced four stages: a slow-increase phase, rapid-increase phase, rapid-decrease phase, and rebound phase. Results show that because Central and East China (CEC) were located at the back of a high-pressure system, humid southerly winds and near surface inversion (NSI) were responsible for the slow accumulation of pollutants. The rapid-increase phase was attributed to pollution transport at both ground level and in the lower troposphere because of weak cold air invasion. The significant subsidence at 500 hPa and 700 hPa intensified the NSI and led to dense fog. In that case, corresponding to the supersaturated atmosphere, the particles entered the fog droplets and were scavenged partly by deposition at night and were resuspended on the next day when the atmosphere was unsaturated. Our findings provide convincing evidence that surface PM2.5 rapid-decrease phase and the rebound phase were closely associated with dense fog process.

Imaging of transient phenomena with low coherence lasers comprising arrays of independent microbeams: A laser version of Harold Edgerton’s stroboscope

A longstanding barrier to laser imaging with high spatial and temporal resolution is speckle, the granular interference pattern arising from the coherent interaction of laser radiation with the topography of an illuminated surface. Over the past five decades, scores of mechanical and optical approaches to mitigating or eliminating the impact of speckle have been proposed, including dynamic diffusers, degenerate optical cavities, and random lasers. We describe a laser resonator architecture that allows the spatial coherence and the associated speckle contrast ratio (C) of the laser output to be varied continuously while providing the power necessary for optical imaging of dynamic objects and phenomena with sub-10 ns resolution. Stabilization of a Fabry–Pérot optical cavity with an internal array of microlenses generates thousands of mutually incoherent, parallel microlaser beams, which merge in the far field to form a single beam having a near-Gaussian transverse intensity distribution. For this laser illuminator, C scales as 1/N, where N is the number of microlasers in the array. When Ti:Al2O3 serves as the gain medium, composite beams comprising N > 1000 microbeams are generated with a divergence angle of ∼5 mrad and C < 0.03 for single pulse energies of 8 mJ (∼1 MW peak power). To illustrate the capability of this tunable spatial-coherence laser, images of Drosophila melanogaster in flight and turbomolecular pump vanes rotating at 56 000 rpm are presented. Owing to the brightness and pulse energies available with this laser, imaging a target at a distance of 5 m through dense fog with ∼250 μm resolution has been demonstrated.

Fog attenuation penalty analysis in terrestrial optical wireless communication-modified duo-binary return-to-zero system with various receiver pointing errors

In metropolitan communication infrastructures a revolutionary technique is emerge known as terrestrial optical wireless communication (OWC), which makes a high-rise building connection is possible. Even with this solution, there are many other problems like the influence of haze and fog in the propagation channel which obstruct and scatter OWC propagation light and consequently led to a big attenuation, due to propagate in temporal, angular and spatial of the light signal. Not to mention the minimum visibility that discourages the implementation of the pointing errors (PE) and tracking system. This present work aims to analyze the interrelation between multiple scattering (dense fog, heavy fog, light fog, heavy haze and light haze) and receiver PE under modified duo-binary return-to-zero (MDRZ) system. We found that PE caused by beam swag is the main controlling factor and industriously minimize the link margin, signal-to-noise ratio (SNR), and raise the bit error rate (BER) when there is an increasing the turbulence strength and the track length. We recommended to guarantee transmitter– receiver alignment by installing a variable field of view (FOV) receiver (a tracking system) to overcome the scattering impact of the fog that make render urban laser communication effective in the presence of PE.

From the redwood forest to the gulf stream waters - drought resistance in coast redwoods and giant sequoias.

From the redwood forest to the gulf stream waters - drought resistance in coast redwoods and giant sequoias.

Enhanced secondary aerosol formation driven by excess ammonia during fog episodes in Delhi, India

The Indo-Gangetic Plain (IGP) has high wintertime fine aerosol loadings that significantly modulate the widespread fog formation and sustenance. Here, we investigate the potential formation of secondary inorganic aerosol driven by excess ammonia during winter fog. Physicochemical properties of fine aerosols (PM1 and PM2.5) and trace gases (HCl, HONO, HNO3, SO2, and NH3) were simultaneously monitored at hourly resolution using Monitor for AeRosols and Gases in Ambient air (MARGA-2S) for the first time in India. Results showed that four major ions, i.e., Cl−, NO3−, SO42−, and NH4+ contributed approximately 97% of the total measured inorganic ionic mass. The atmosphere was ammonia-rich in winter and ammonium was the dominant neutralizer with aerosol neutralization ratio (ANR) close to unity. The correlation between ammonium and chloride was ≥0.8, implying the significant formation of ammonium chloride during fog in Delhi. Thermodynamical model ISORROPIA-II showed the predicted PM1 and PM2.5 pH to be 4.49 ± 0.53, and 4.58 ± 0.48 respectively which were in good agreement with measurements. The ALWC increased from non-foggy to foggy periods and a considerable fraction of fine aerosol mass existed in the supermicron size range of 1–2.5 μm. The sulfur oxidation ratio (SOR) of PM1, PM2.5 reached up to 0.60, 0.75 in dense fog and 0.74, 0.87 when ambient RH crossed a threshold of 95%, much higher than non-foggy periods (with confidence level of ≥95%) pointing to enhanced formation of secondary aerosol in fog.

Vehicular Visible Light Positioning Using Receiver Diversity with Machine Learning

This paper proposes a 2-D vehicular visible light positioning (VLP) system using existing streetlights and diversity receivers. Due to the linear arrangement of streetlights, traditional positioning techniques based on triangulation or similar algorithms fail. Thus, in this work, we propose a spatial and angular diversity receiver with machine learning (ML) techniques for VLP. It is shown that a multi-layer neural network (NN) with the proposed receiver scheme outperforms other ML algorithms and can offer high accuracy with root mean square (RMS) error of 0.22 m and 0.14 m during the day and night time, respectively. Furthermore, the NN shows robustness in VLP across different weather conditions and road scenarios. The results show that only dense fog deteriorates the performance of the system due to reduced visibility across the road.

The propagation of fog and its related pollutants in the Central and Eastern China in winter

A widespread dense fog event associated with heavy air pollution occurred in the central and eastern regions of China from 30 December 2016 to 5 January 2017. The fog event was characterized by three distinct propagation processes on different spatial scales. This study investigated the propagation of fog and variations of pollutants during the fog event in order to better understand the mechanisms of fog. The cold high-pressure system moved southward, and the southerly wind on the western flank of the high-pressure system enhanced the temperature inversion, both of which were favorable for fog propagation. In addition, the invasion of the weak cold air and radiative cooling, and the different land-sea thermodynamic properties also contributed to the formation and propagation of the fog. Heavy pollution related to this severe fog was also investigated. The field experiment indicates that amounts of aerosol particles accounted for the enhancement of fog droplets through its role of being the condensation nuclei. As a result, the PM2.5 concentration decreased when the fog became strong. The strong temperature inversion and gas to particle phase conversion led to an increase of the PM2.5 concentration and a decrease of pollution gases (SO2, NO2) concentration during the mature stage.

New Encoder Learning for Captioning Heavy Rain Images via Semantic Visual Feature Matching

Image captioning generates text that describes scenes from input images. It has been developed for high-quality images taken in clear weather. However, in bad weather conditions, such as heavy rain, snow, and dense fog, poor visibility as a result of rain streaks, rain accumulation, and snowflakes causes a serious degradation of image quality. This hinders the extraction of useful visual features and results in deteriorated image captioning performance. To address practical issues, this study introduces a new encoder for captioning heavy rain images. The central idea is to transform output features extracted from heavy rain input images into semantic visual features associated with words and sentence context. To achieve this, a target encoder is initially trained in an encoder-decoder framework to associate visual features with semantic words. Subsequently, the objects in a heavy rain image are rendered visible by using an initial reconstruction subnetwork (IRS) based on a heavy rain model. The IRS is then combined with another semantic visual feature matching subnetwork (SVFMS) to match the output features of the IRS with the semantic visual features of the pretrained target encoder. The proposed encoder is based on the joint learning of the IRS and SVFMS. It is trained in an end-to-end manner, and then connected to the pretrained decoder for image captioning. It is experimentally demonstrated that the proposed encoder can generate semantic visual features associated with words even from heavy rain images, thereby increasing the accuracy of the generated captions.

Atmospheric boundary layer response sequence to the 2019 annular solar eclipse over a tropical rural terrain

ABSTRACT Transient alterations in the surface meteorological parameters and atmospheric boundary layer (ABL) due to annual solar eclipse occurred on 26 December 2019 have been investigated using collocated ground-based remote-sensing and in situ measurements over a tropical rural station, Gadanki (13.5 ºN, 79.2 ºE). This event had provided an opportunity to assess the eclipse effect on atmospheric parameters as it occurred during initial hours of the boundary layer evolution (between 08:07 IST and 11:15 IST with peak at 09:32 IST over Gadanki). As eclipse commenced, the observations showed a dramatic reduction in the incoming shortwave radiation and consequent changes in air temperature and wind patterns throughout the ABL. Nevertheless, the amplitude of the changes in various parameters is not analogous to the obscuration proportions but is significantly dictated by the background meteorological conditions such as prevalence of dense fog and cloudiness during the eclipse timings. Ceilometer and wind profiler observations have provided the high-resolution continuous signatures of clouds and fog, strong subsidence, thick layers above the ABL, and a clear change in ABL height (ABLH) during the eclipse epoch. Interestingly, the delay in the evolution of ABL due to the eclipse is clearly manifested. The ABLH, which remained shallow even after the eclipse peak, has shown a rapid increase by more than 1000 m within a short span of an hour just before and after the eclipse culmination. However, significant delay (of about 2 hours after the eclipse) is observed in the surface variables to fall in the range of their climatological values. The observations elusively suggest that commence and termination phases of eclipse analogously influenced the surface and boundary layer parameters in descending sequence (eclipse effect is subtly seen first in Ceilometer and wind profiler attributes followed by surface state variables).

Simulation of Radiation Fog Events over Dhaka, Bangladesh Using WRF Model and Validation with METAR and Radiosonde Data

Fog causes severe hazards in the fields of aviation, transportation, agriculture and public health over Dhaka, Bangladesh during the winter season every year. The characterization of fog occurrences, its onset, duration and dissipation time over Hazrat Shahjalal International Airport, Dhaka are the topics of interest in the present study. Attempts have therefore been made to investigate the climatological perspectives of fog over Dhaka, Bangladesh by conducting two selected dense fog events occurred during 24-25 December 2019 and 14-15 January 2020 using WRF-ARW model. The model performance is evaluated by analyzing different meteorological parameters namely visibility, relative humidity, temperature, and wind. The model outputs have been compared with METAR data from Dhaka Airport, Sounding data and INSAT 3D satellite images for validation purpose. Considering RMSE, the model underestimates of relative humidity. Model simulations are good for other meteorological parameters. Thermodynamic analysis reveals that calm wind persists at surface level during fog formation, southwesterly dry wind was over Dhaka and inversion layer is found to persist in the lower troposphere over Dhaka during the event dates. It is observed in the satellite images that fog/low-level cloud was present over Dhaka during the fog events.
 The Dhaka University Journal of Earth and Environmental Sciences, Vol. 9(1), 2020, P 39-47

The complex case of Chesapeake Bay restoration

Amid encouraging signs, researchers and activists are struggling to make progress in the face of agricultural inputs, climate change, and relentless development . On a warm October day, six researchers ventured out to a reef in Baines Creek, a tributary of Virginia’s Elizabeth River. Back in 2014, the Army Corps of Engineers had dumped tons of fossil oyster shells into the water in the hopes of attracting more of one of the key inhabitants of the Chesapeake Bay—oysters. Now the researchers, wearing masks and socially distancing as they wade through the water at low tide, are measuring the progress. Numerous tributaries feed into the Chesapeake Bay, such as the Severn River near Annapolis, MD. They enhance the beauty of the Bay’s watershed—they also shuttle pollutants that imperil the Bay’s ecosystems. Image credit: Shutterstock/Ladinn. What they are finding is just astounding, says Romuald Lipcius, professor of fisheries science at the Virginia Institute of Marine Science (VIMS) at Gloucester Point, and also the School of Marine Science at William & Mary University. The team counts hundreds of oysters growing on each square meter of reef. Some oysters had reached the ripe old age of five, once considered unachievable because of diseases that had been introduced into the Bay; oysters’ fecundity increases exponentially with size and age. “All these oysters peeking above the water is a beautiful sight,” says William & Mary professor of geology Rowan Lockwood, who studies fossil oysters. “It is a glimpse of what the Bay was like 6,000 years ago.” And maybe, what it could be like again. But that ideal future is still a long shot. A couple of weeks after Lipcius plumbed the depths of Baines Creek, Matt Pluta piloted his small boat through a dense fog on the Choptank River, which flows into the Bay from …

Stochastic Geometry-Based Interference Characterization for RF and VLC-Based Vehicular Communication System

In this article, we characterize various aspects of stochastic behavior of intervehicular interference by modeling location of road vehicles as a spatial Poisson point process. We make use of various analytical tools of stochastic geometry to provide an analytical framework to access the performance for both vehicular-radio frequency (V-RF) communication and vehicular-visible light communication (V-VLC) for dense, medium, and sparse traffic scenarios. The developed framework is also precise in terms of capturing the impact of reducing field-of-view (FOV) of receiver on the level of interference experienced from interferers for V-VLC. The performance has been evaluated and compared under normal atmospheric conditions as well as different environmental deterrents viz., light fog, dense fog, and dry snow conditions in terms of probability of successful transmission as a performance metric. Irrespective of any traffic scenario, the performance of V-VLC communication under normal atmospheric condition always outperforms V-RF communication. However, the performance of V-RF communication is comparatively better than V-VLC under various environmental deterrents. The proposed result motivates the benefit of employing RF-based or VLC-based vehicular-to-vehicular (V2V) communication which takes into account different environmental conditions as well as meets the diverse application requirements for future intelligent transportation system.