Fog Events Research Articles

Effect of Biomass Burning, Diwali Fireworks, and Polluted Fog Events on the Oxidative Potential of Fine Ambient Particulate Matter in Delhi, India.

We investigated the influence of biomass burning (BURN), Diwali fireworks, and fog events on the ambient fine particulate matter (PM2.5) oxidative potential (OP) during the postmonsoon (PMON) and winter season in Delhi, India. The real-time hourly averaged OP (based on a dithiothreitol assay) and PM2.5 chemical composition were measured intermittently from October 2019 to January 2020. The peak extrinsic OP (OPv: normalized by the volume of air) was observed during the winter fog (WFOG) (5.23 ± 4.6 nmol·min-1·m-3), whereas the intrinsic OP (OPm; normalized by the PM2.5 mass) was the highest during the Diwali firework-influenced period (29.4 ± 18.48 pmol·min-1·μg-1). Source apportionment analysis using positive matrix factorization revealed that traffic + resuspended dust-related emissions (39%) and secondary sulfate + oxidized organic aerosols (38%) were driving the OPv during the PMON period, whereas BURN aerosols dominated (37%) the OPv during the WFOG period. Firework-related emissions became a significant contributor (∼32%) to the OPv during the Diwali period (4 day period from October 26 to 29), and its contribution peaked (72%) on the night of Diwali. Discerning the influence of seasonal and episodic sources on health-relevant properties of PM2.5, such as OP, could help better understand the causal relationships between PM2.5 and health effects in India.

Observation and simulation of a heavy fog event in Qiongzhou Strait

From February 14th to February 25th, 2018, the Qiongzhou Strait, China, experienced a persistent heavy fog that was unprecedented in the past 67 years. Observations and simulation results are utilized to diagnose causes of the heavy fog event. This persistent heavy fog was closely related to the continuous occurrence of fog in various types. More fog was observed along the northern coast than the southern coast. The fog over sea lasted longer than that over land. Fog along the southern coast was more sensitive to humidity, whereas fog on the northern coast was more sensitive to wind. The wind speed on the northern coast is beneficial to the generation of advection fog. Contrastingly, the southern coast was in either a weak wind or static wind state, which was in favor of the generation of radiation fog. A low altitude inversion layer appeared before the heavy fog occurred, controlled by the warm and humid airflow in the south. When the heavy fog dissipated, the wind velocity gradually increased, controlled by the dry and cold airflow in the northeast.The variation characteristics of meteorological conditions were successfully reproduced using the WRF model. Simulated minimum visibility and sea fog area were consistent with observations. Air-sea temperature difference is of vital importance to the formation of the heav fog. The stability of atmosphere boundary layer below 150 m was sensitive to the change in sea surface temperature (SST). The higher SST led to higher boundary layer and lower stability. which further resulted in a shorter duration and smaller area of sea fog. The decrease in the SST led to the lower height of boundary layer and stronger stability, which further resulted in a longer duration and expanding area of the sea fog.

A dataset of temperature, humidity, and liquid water path retrievals from a network of ground-based microwave radiometers dedicated to fog investigation

The database presented in this study has been acquired during the SOuth west FOGs 3D (SOFOG3D) experiment for processes study. This international campaign led by Météo-France during the winter 2019–2020 aimed at deploying a unique network of both in situ and remote sensing measurements in order to document spatial and temporal variabilities of fog events. To support this scientific objective but also to conduct first data assimilation experiments within the French convective scale model AROME, an un-precedented network of 8 ground-based microwave radiometers (MWR) has been deployed in 7 different locations known to be prone to fog occurrences. The database gives access to vertical profiles of temperature and humidity (both absolute and relative) from the surface up to 10 km altitude as well as integrated water vapor and liquid water path estimates. The retrieved profiles offer a very large database that can be exploited for several scientific purposes: fog process studies at specific location, documentation on the variability of fog properties at the regional scale, better understanding of the atmospheric boundary layer (ABL) height and dynamics during wintertime conditions, development of nowcasting products dedicated to fog alerts, data assimilation experiments to improve fog forecasts, development of synergetical advanced products, and evaluation of new model configurations with advanced parameterization or resolutions.

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

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

Experimental study on the evolution of droplet size distribution during the fog life cycle

Abstract. The evolution of the droplet size distribution (DSD) during the fog life cycle remains poorly understood and progress is required to reduce the uncertainty of fog forecasts. To gain insights into the physical processes driving the microphysical properties, intensive field campaigns were conducted during the winters of 2010–2013 at the Instrumented Site for Atmospheric Remote Sensing Research (SIRTA) in a semi-urban environment southwest of Paris city center to monitor the simultaneous variations in droplet microphysical properties and their potential interactions at the different evolutionary stages of the fog events. Liquid water content (LWC), fog droplet number concentration (Nd) and effective diameter (Deff) show large variations among the 42 fog events observed during the campaign and for individual events. Our findings indicate that the variability of these parameters results from the interaction between microphysical, dynamical and radiative processes. During the formation and development phases, activation of aerosols into fog droplets and condensational growth were the dominant processes. When vertical development of radiation fog occurred under the influence of increasing wind speed and subsequent turbulent motion, additional condensational growth of fog droplets was observed. The DSDs with single mode (around 11 µm) and double mode (around 11 and 22 µm) were observed during the field campaign. During the development phase of fog with two droplet size modes, a mass transfer occurred from the smaller droplets into the larger ones through collision–coalescence or Ostwald ripening processes. During the mature phase, evaporation due to surface warming induced by infrared radiation emitted by fog was the dominant process. Additional droplet removal through sedimentation is observed during this phase for fog with two droplet size modes. Because of differences in the physical processes involved, the relationship between LWC and Nd is largely driven by the DSD. Although a positive relationship is found in most of the events due to continuous activation of aerosol into fog droplets, LWC varies at a constant Nd in fog with large Deff (>17 µm) due to additional collision–coalescence and Ostwald ripening processes. This work illustrates the need to accurately estimate the supersaturation for simulating the continuous activation of aerosols into droplets during the fog life cycle and to include advanced parameterizations of relevant microphysical processes such as collision–coalescence and Ostwald ripening processes, among others, in numerical models.

A climatological study of fog in Turkey

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

Sensitivity analysis of an aerosol-aware microphysics scheme in Weather Research and Forecasting (WRF) during case studies of fog in Namibia

Abstract. Aerosol-aware microphysics parameterisation schemes are increasingly being introduced into numerical weather prediction models, allowing for regional and case-specific parameterisation of cloud condensation nuclei (CCN) and cloud droplet interactions. In this paper, the Thompson aerosol-aware microphysics scheme, within the Weather Research and Forecasting (WRF) model, is used for two fog cases during September 2017 over Namibia. Measurements of CCN and fog microphysics were undertaken during the AErosols, RadiatiOn and CLOuds in southern Africa (AEROCLO-sA) field campaign at Henties Bay on the coast of Namibia during September 2017. A key concept of the microphysics scheme is the conversion of water-friendly aerosols to cloud droplets (hereafter referred to as CCN activation), which could be estimated from the observations. A fog monitor 100 (FM-100) provided cloud droplet size distribution, number concentration (Nt), liquid water content (LWC), and mean volumetric diameter (MVD). These measurements are used to evaluate and parameterise WRF model simulations of Nt, LWC, and MVD. A sensitivity analysis was conducted through variations to the initial CCN concentration, CCN radius, and the minimum updraft speed, which are important factors that influence droplet activation in the microphysics scheme of the model. The first model scenario made use of the default settings with a constant initial CCN number concentration of 300 cm−3 and underestimated the cloud droplet number concentration, while the LWC was in good agreement with the observations. This resulted in droplet size being larger than the observations. Another scenario used modelled data as CCN initial conditions, which were an order of magnitude higher than other scenarios. However, these provided the most realistic values of Nt, LWC, MVD, and droplet size distribution. From this, it was concluded that CCN activation of around 10 % in the simulations is too low, while the observed appears to be higher reaching between 20 % and 80 %, with a mean (median) of 0.55 (0.56) during fog events. To achieve this level of activation in the model, the minimum updraft speed for CCN activation was increased from 0.01 to 0.1 m s−1. This scenario provided Nt, LWC, MVD, and droplet size distribution in the range of the observations, with the added benefit of a realistic initial CCN concentration. These results demonstrate the benefits of a dynamic aerosol-aware scheme when parameterised with observations.

Numerical study on advective fog formation and its characteristic associated with cold water upwelling.

Recent rapid industrial development in the Korean Peninsula has increased the impacts of meteorological disasters on marine and coastal environments. In particular, marine fog driven by summer cold water masses can inhibit transport and aviation; yet a lack of observational data hinders our understanding of this phenomena. The present study aimed to analyze the differences in cold water mass formation according to sea surface temperature (SST) resolution and its effects on the occurrence and distribution of sea fog over the Korean Peninsula from June 23-July 1, 2016, according to the Weather Research and Forecasting model. Data from the Final Operational Model Global Tropospheric Analyses were provided at 1° and 0.25° resolutions and NOAA real-time global SST (RTG-SST) data were provided at 0.083°. While conventional analyses have used initial SST distributions throughout the entire simulation period, small-scale, rapidly developing oceanic phenomena (e.g., cold water masses) lasting for several days act as an important mediating factor between the lower atmosphere and sea. RTG-SST was successful at identifying fog presence and maintained the most extensive horizontal distribution of cold water masses. In addition, it was confirmed that the difference in SST resolution led to varying sizes and strengths of the warm pools that provided water vapor from the open sea area to the atmosphere. On examining the horizontal water vapor transport and the vertical structure of the generated sea fog using the RTG-SST, water vapors were found to be continuously introduced by the southwesterly winds from June 29 to 30, creating a fog event throughout June 30. Accordingly, high-resolution SST data must be input into numerical models whenever possible. It is expected that the findings of this study can contribute to the reduction of ship accidents via the accurate simulation of sea fog.

Fog-Haze Transition and Drivers in the Coastal Region of the Yangtze River Delta.

Low-visibility events (LVEs) are severe weather phenomena that are closely linked with anthropogenic pollution, which negatively affects traffic, air quality, human health, and the environment. This study conducted a two-month (from October to December 2019) continuous measurement campaign on Chongming Island in Shanghai to characterize the LVEs transition and its drivers. The LVEs accounted for 38% of the time during the campaign, of which mist accounted for 14%, fog–haze for 13%, haze for 6%, and fog for 5%. The fog and mist mainly occurred from midnight to early morning, while haze mostly occurred during the daytime. Different LVEs were interdependent and transitioned from one to another. Fog generally turned into haze after sunrise, while haze turned into fog after sunset. Their formation and evolution were caused by the combined impacts of meteorological conditions and aerosol particles. It was found that temperature difference was the dominant meteorological factor driving the evolution of LVEs. Within the short term, cooling led to a greater increase in relative humidity than humidification. Radiative cooling during the night promoted the formation of fog and mist. During fog and mist events, cloud condensation nuclei (CCN) were mainly internally mixed due to the impact of fog droplet removal and aqueous/heterogeneous aerosol reactions occurring under high humidity. Increased CCN concentration appeared to increase the fog droplet number and liquid water content in fog events. Overall, conditions of high humidity and high particle loading were conducive to LVEs, whereas conditions of sufficient water vapor at a low particle level and sufficient particles at a low humidity level also caused LVEs. This study provided insights into LVEs classification, evolution scheme, and aerosol roles from a micro point of view. The findings could be useful for improving forecasts of local radiative fog and other LVEs.

Seasonal fog enhances crop water productivity in a tropical rubber plantation

• The effect of fog on rubber trees ( Hevea brasiliensis ) was quantified for 3 years. • Visibility, sap flux density and crop water productivity (WP c ) were studied. • Under the fog days, rubber trees were physiologically less active. • Carbon assimilation and evapotranspiration were suppressed but WP c enhanced by fog. • Dry seasonal fog can reduce water demand and alleviate drought stress. The rapid conversion of tropical rainforests into monoculture plantations of rubber ( Hevea brasiliensis ) in Southeast Asia (SEA) necessitates understanding of rubber tree physiology under local climatic conditions. Frequent fog immersion in the montane regions of SEA may affect the water and carbon budgets of the rubber trees and the plantation ecosystems. We studied the effect of fog on various plant physiological parameters in a mature rubber plantation in southwest China over 3 years. During the study period, an average of 141 fog events occurred every year, and the majority occurred during the dry season, when the temperature was relatively low. In addition to the low temperature, fog events were also associated with low vapor pressure deficit, atmospheric water potential, relative humidity and frequent wet-canopy conditions. We divided the dry season into cool dry (November-February) and hot dry (March-April) seasons and classified days into foggy (FG) and non-foggy (non-FG) days. During the FG days of the cool dry season, the physiological activities of the rubber trees were suppressed where carbon assimilation and evapotranspiration showed reductions of 4% and 15%, respectively, compared to the cool dry non-FG days. Importantly, the unequal declines in carbon assimilation and evapotranspiration led to enhanced crop water productivity (WP c ) on cool dry FG days but insignificant WP c values were found between FG and non-FG days of the hot dry season. Our results suggest that, by regulating plant physiology, fog events during the cool dry season significantly reduce water demand and alleviate water stress for the trees through improved WP c .

Operational Constraints on Flight Navigation due to Fog and Consequent Economic Implications at the Rajiv Gandhi International Airport, Hyderabad, Telangana, India

Fog is a frequent deterrent during the winter season. The reduction in horizontal visibility and other adverse conditions results in flight delays, cancellations, and flight de-route/diversions, leading to economic implications for rescheduling plans by the air-travellers. Clear skies, calm/light winds, a good amount of relative humidity, and fall in temperatures due to radiation cooling during winter nights are conducive. The intensity and duration of fog constrain flight operations. In the light of a fair number of fog occurrences compelling flight delays and diversions over Hyderabad Airport (RGIA, Shamshabad), the present study is undertaken to analyse the meteorological aspects of fog, and its frequency, duration, and economic implications. During the period 2014-2019, 74 instances of fog occurrence were recorded over the RGIA. The majority of the incidents (45.9%) were shallow fog events. The dense fog (2) and very dense fog (1) events were rare. Maximum fog occurrences (26 out of 74 events) were reported in December. The onset of fog was observed between 1800 UTC and 0400 UTC, while most of them (43.2%) occurred between 0000 UTC and 0200 UTC. Most of the fog events lasted up to 1 hour, and there were six instances of fog lasting between 2-3 hrs.

Understanding the genesis of a dense fog event over Delhi using observations and high-resolution model experiments

Understanding the genesis of a dense fog event over Delhi using observations and high-resolution model experiments

Improving simulation of the fog life cycle with high-resolution land data assimilation: A case study from WiFEX

The present study highlights the role of high-resolution land data assimilation in improving the prediction of the radiation fog and near-surface meteorological variables. The performance of the Weather Research and Forecasting (WRF) model coupled with the High-Resolution Land Data Assimilation System (HRLDAS) is evaluated for a dense fog event that occurred on 24–25 January 2018 using detailed observations from the Winter Fog EXperiment (WiFEX) over the Delhi (India) region. The Noah-MP Land-Surface Model (LSM) based HRLDAS framework was executed in uncoupled mode to develop fine-grid soil states for soil moisture (SM) and soil temperature (ST) covering the Indo Gangetic Plain (IGP) region at 2 km horizontal grid resolution. The quality of soil states (SM/ST) from the HRLDAS and reanalysis (CNTL) dataset was verified with observed SM/ST at the Indira Gandhi International (IGI) airport, New Delhi, during 2017–18 winter months (December–January). It was found that the soil state in the CNTL dataset is moist, despite the actual soil condition being dryer at the observation site. HRLDAS simulated SM reasonably agrees with observations at IGI by reducing wet mean bias by about 56%. Subsequently, four sensitive experiments were carried out using Noah-MP (NM) and Pleim-Xiu (PX) land-surface parameterisations in the WRF model initialised with CNTL and HRLDAS soil states. We found that the bias in micro-meteorological variables (T2, RH2, WS10) and Turbulent Kinetic Energy (TKE) during the fog event was significantly improved with Pleim-Xiu (PX) land-surface parameterisations and HRLDAS soil states (HRLDAS_PX). Statistical performance of the micro-meteorological variables (T2, RH2 and WS10) exhibited low variance (0.13°C, 0.10% and 0.75 m s−1), high correlation (0.93, 0.92 and 0.82) and high index of agreement (0.92, 0.87 and 0.78). As a result, the error in fog onset timing was notably reduced to 02 h, and the vertical representation of fog was skillfully demonstrated in the HRLDAS_PX simulation. The sensitivity experiments revealed that there is a need to revisit soil states to improve the skill of the fog forecast.

Formation of fog due to stratus lowering: An observational and modelling case study

AbstractWe numerically investigate the processes responsible for a fog event formed by stratus cloud lowering, observed on December 1–2, 2016, during the experimental campaign in the northeast of France. The observations revealed a complex temporal evolution with stratus followed by a relatively drier period and then its reformation leading to fog formation by stratus lowering. Microphysical observations below a tethered balloon exhibit different vertical profiles of liquid water content and droplet concentration between the stratus and the fog formed below. A simulation at 100‐m grid spacing reproduced the main observed characteristics of the cloud life cycle despite a time lag in stratus formation due to large‐scale conditions. The advection of cloud water in the stratus and at its top appears crucial to feed the stratus lowering, resulting in radiative cooling, vertical transport, droplet sedimentation, evaporation, and cooling of the sub‐cloud layer. The advection of cold or warm air in the lowest 250 m, mainly driven by fine‐scale orographic circulations, impacts the fog formation due to stratus cloud lowering. When non‐local conditions are favourable, the most important microphysical process to favour fog formation is the droplet sedimentation, leading to the cooling and moistening in the sub‐cloud layer by evaporation. Droplet sedimentation appears more efficient when the droplet concentration is low, and a two‐moment microphysical scheme more appropriate than a one‐moment scheme to reproduce the observed variability of the droplet concentration. Given the predominance of non‐local processes on this case study, a three‐dimensional high‐resolution model appears crucial to perform accurate forecasts of fog by stratus lowering.

Fog type classification using a modified Richardson number for Christchurch, New Zealand

AbstractSituated on a coastal plain between the Southern Alps and Banks Peninsula, Christchurch, New Zealand, experiences around 49 fog days every year. Given its complex topography, accurate fog forecasting is difficult at Christchurch International Airport (CHA). Climatological analysis of local fog events is an important first step to gain insight into the processes involved in the fog lifecycle. In this study, fog events were identified using 12 years of meteorological observations from an automatic weather station situated at CHA. A novel fog type classification method was developed using the modified Richardson number (MRi). The MRi fog type classification method assesses the local dynamic stability of a 1.25 m shallow layer of near‐surface air. Here, the MRi is used as a quantitative index to classify advection fog, advection–radiation fog, and radiation fog. Vertical gradients of air temperature and wind speed were derived for prefog and fog periods, and a number of criteria were applied to the MRi for the fog type classification. The fog type classification results were examined in correspondence with the derived fog intensity, duration, diurnal and seasonal variability of frequency of occurrences, and synoptic and local wind flows. In agreement with other fog studies across the world, fog occurs most frequently during local winter and spring. Radiation fog is the predominant type of fog identified at CHA, and its formation and development usually coincide with the local drainage northwesterlies. This study is the first to use long‐term observational data to investigate the fog climatology and typology at CHA in detail. The fog climatological characteristics presented in this study will serve as the basis of future fog studies in Christchurch. The presented MRi fog type classification method can potentially be used in fog characteristic studies worldwide.

Ankara Esenboga International Airport (Turkey) fog analysis and synoptical investigation of the fog event dated 17–19 December 2019

AbstractThe aviation industry has developed greatly in recent years. Millions of people choose air transport as a means of transportation every year, and flight safety is critically important. However, meteorological conditions can cause disruptions in flight activities and lead to accidents. One of the meteorological conditions that adversely affect flight activities is fog. Prediction of fog incidents increases flight safety. In this study, fog analysis of Ankara Esenboga International Airport was carried out between 2011 and 2020. The data used in the fog analysis are taken from the observations of Aviation Routine Weather Report (METAR) and Aviation Selected Special Weather Report (SPECI). The distribution of fog events in a 10‐year period was determined according to years, months, days, hours, meteorological parameters (wind, pressure, cloud cover/height, temperature, and prevailing view), and types. Over a 10‐year period, 802.02 hr of fog occurred. The average number of foggy days was determined as 17.9. According to the fog formation types, the fogs occurring at Esenboga Airport were classified as 62% radiation fog, 14% lowering cloud base, 9% precipitation fog, 2% advection fog, and 12% unknown fog. CAT categories in the foggy hours of Esenboga Airport were determined. In cases where there were foggy observations for approach and landing activities during the 10‐year period, these were found to be 40.34% according to CAT I, 72.8% according to CAT II, 93.8% according to CAT IIIA, and 100% according to CAT IIIB. Synoptic analysis of a particular fog incident, which started at 2220 UTC on December 16 and continued for 55.83 hr, was also made. When the synoptic maps and Skew‐T Log P diagrams were examined, the deepening of the high pressure center in and around Ankara and the low thermal advection caused a very stable atmosphere, resulting in a prolonged and intense fog event.

Application of visibility new algorithms on fog event

To improve the visibility calculation accuracy of numerical weather predict model, the paper tries to explore two new visibility calculation methods. Firstly, a multi-element visibility algorithm is established based on air temperature, dew point temperature and relative humidity. Then a visibility fusion method is developed. Verifications show that the calculation accuracy of multi-element visibility algorithm gains an advantage over a single relative humidity method, and the visibility fusion algorithm can describe the regional distribution of fog event. visibility fusion algorithm can overcome the limitation of single visibility algorithm and improves the prediction accuracy of fog.

Nematode communities indicate diverse soil functioning across a fog gradient in the Namib Desert gravel plains.

Soil nematodes are fundamentally aquatic animals, requiring water to move, feed, and reproduce. Nonetheless, they are ubiquitous in desert soils because they can enter an anhydrobiotic state that allows them to persist when water is biologically unavailable. In the hyper‐arid Namib Desert of Namibia, rain is rare, but fog routinely moves inland from the coast and supports plant and animal life. Very little is understood about how this fog may affect soil organisms. We investigated the role of fog moisture in the ecology of free‐living, soil nematodes across an 87‐km fog gradient in the gravel plains of the Namib Desert. We found that nematodes emerged from anhydrobiosis and became active during a fog event, suggesting that they can utilize fog moisture to survive. Nematode abundance did not differ significantly across the fog gradient and was similar under shrubs and in interplant spaces. Interplant soils harbor biological soil crusts that may sustain nematode communities. As fog declined along the gradient, nematode diversity increased in interplant soils. In areas where fog is rare, sporadic rainfall events can stimulate the germination and growth of desert ephemerals that may have a lasting effect on nematode diversity. In a 30‐day incubation experiment, nematode abundance increased when soils were amended with water and organic matter. However, these responses were not evident in field samples, which show no correlations among nematode abundance, location in the fog gradient, and soil organic matter content. Soil nematodes are found throughout the Namib Desert gravel plains under a variety of conditions. Although shown to be moisture‐ and organic matter‐limited and able to use moisture from the fog for activity, variation in fog frequency and soil organic matter across this unique ecosystem may be biologically irrelevant to soil nematodes in situ.

Meteorological characteristics of fog events in Korean smart cities and machine learning based visibility estimation

To address various urban issues such as fine dust, traffic congestion, and water shortage caused by rapid urbanization, a national pilot Smart City is planned in two Korean cities, Sejong and Busan. As weather data is crucial for improving the environment and operating future transportation while constructing a smart city, preparing for future weather disasters by analyzing the characteristics of various meteorological phenomena in the planned development area is necessary. This study analyzed the fog generation characteristics for the period of 2016–2020 at the automatic weather system sites of the Korea Meteorological Administration in Sejong and Busan, and the characteristics of the meteorological elements during fog were investigated. Additionally, three machine learning based models, including Random Forest (RF) and Deep Neural Network DNN-1 and DNN-2, were constructed for estimating visibility using meteorological input variables. In Sejong, approximately 50 fog days were observed annually for the analysis period, with the highest frequency of these days being during autumn. The fog hours were distributed from nighttime to before sunrise and 94.3% of the fog occurred when the wind speed was less than 1.5 m/s, showing the characteristics of radiative fog. Most of the fog occurred during summer in Busan, and the maximum number of fog days was observed in July. The average wind speed during fog was approximately 2.5 m/s, which was relatively large, suggesting that advection fog was likely to occur. As a result of estimating visibility from the ML-based models, the RF model had the highest R2 (0.66 and 0.53) in both regions, but the visibility estimated from the RF model had an over- and under-estimation for short and long visibility ranges, respectively. DNN-1 and DNN-2 models have lower biases. In the detection of fog-possible weather based on the estimated visibility from the models, the best precisions (0.85 and 0.84) and F1-scores (0.76 and 0.74) were from the RF model, but model recall was better in the DNN-1 model. The model recalls for the detection of the thick and dense fog were also better in the DNN models. ML-based models presented reasonable performance but revealed their weaknesses and strengths depending on performance indicators.

Using Machine Learning Algorithm to Detect Blowing Snow and Fog in Antarctica Based on Ceilometer and Surface Meteorology Systems

Blowing snow is a common weather phenomenon in Antarctica and plays an important role in the water vapor cycle and ice sheet mass balance. Although it has a significant impact on the climate of Antarctica, people do not know much about this process. Fog events are difficult to distinguish from blowing snow events using existing detection algorithms by a ceilometer. In this study, based on ceilometer, the meteorological parameters observed by surface meteorology systems are further combined to detect blowing snow and fog using the AdaBoost algorithm. The weather phenomena recorded by human observers are ‘true’. The dataset is collected from 1 January 2016 to 31 December 2016 at the AWARE site. Among them, three-quarters of the data are used as the training set and the rest of the data as the testing set. The classification accuracy of the proposed algorithm for the testing set is about 94%. Compared with the Loeb method, the proposed algorithm can detect 89.12% of blowing snow events and 76.10% of fog events, while the Loeb method can only identify 64.29% of blowing snow events and 31.87% of fog events.