Fog Events Research Articles

Fog in Sofia 2010–2019: Objective Circulation Classification and Fog Indices

Low visibility caused by fog events can lead to disruption of every type of public transportation, and even loss of life. The focus of this study is the synoptic conditions associated with fog formation. The data used in this study was collected over the course of ten years (2010–2019) in Sofia, Bulgaria. The forecast skills of the Fog Stability Index (FSI) and the local Sofia Stability Index (SSI), as well as the relation between the Integrated Water Vapor (IWV) and fog from the Global Navigation Satellite System (GNSS), were tested. Both fog indices are used for fog nowcasting as their lead times are short and unclear. The Jenkinson–Collison Type method was used for extracting the predominant synoptic-scale pressure systems which provide suitable weather conditions for fog formation. Surface observations from two synoptic stations were used to calculate and evaluate the performance of the two fog indices and of the ground-based GNSS receiver for the IWV. The forecast skills provided by Probability of Detection (POD) and False Alarm Ratio (FAR), for both fog and no-fog periods, were obtained by discriminant analysis. Additionally, several weather parameters, such as surface wind speed, relative humidity and IWV, were added in order to improve the results of the local index (SSI). This led to a 77.9% hit rate. The cyclonic system influence and zonal flows from the west and the southwest are both responsible for a number of fog cases that are comparable to those associated with the anticyclonic system. The IWV was not found to improve the forecast skill of the fog indices. However, it was found that its values had a larger spread during no-fog periods in comparison to fog periods.

How Important Are Fog and the Cloud Forest as a Water Supply in Eastern Mexico?

The water balance is the volume of water flowing through the hydrological cycle, and one of its main components is fog. Fog is considered a type of low-lying cloud and is heavily influenced by water bodies, topography, and wind conditions. Fog incorporates water from the atmosphere to the terrestrial surface and for some ecosystems (e.g., cloud forests) represents a great water contribution. In this work, we aimed to answer the following questions: (1) What is the fog-water contribution to the water balance? (2) How does the presence of vegetation affect the water supply to the ecosystem? We took as a case study the Central Mountain Region of Veracruz, in eastern Mexico, and measured components of the water balance; this included precipitation (gross and net), fog water, interception, transpiration, evapotranspiration, and condensation, and we estimated water gain and loss of the water balance. We registered 510 precipitation events distributed throughout the year with three peaks (October, May, and January). Of these, 386 were fog events, 41 were rain events, and 83 were events combining fog and rain. Fog had a substantial contribution of water to the system, with a volume 22 times greater than that of rain (4311.14 mm vs. 197.5 mm). From the total water gain, the highest proportion (91%) was contributed by fog interception. Fog was considered a constant source of moisture throughout the year; however, water intercepted during the dry season was higher (56%) compared with the wet season (36%). Our results highlight the importance of the fog as a source of water for the region.

Design and Evaluation of ACFC—An Automatic Cloud/Fog Collector

Cloud and fog droplets are essential in atmospheric chemistry since they affect the distribution and chemical transformation of pollutants. Collecting sufficient volumes using cloud/fog samplers is the premise of cloud fog chemical studies. Accurate identification of fog events and high collection efficiency are the basic principles of sampler design. Therefore, an automatic cloud/fog collector (ACFC) has been designed, fabricated, and extensively tested for collecting samples of cloud/fog water. The control box and standard sensors for air temperature, relative humidity, and instantaneous rainfall were used to ensure sampling automation. Airflow measurement was used to guarantee the stability of the airspeed on the inlet section, and the airspeed is 7.5 m s−1. Moreover, the median collection rate of ACFC was 160–220 mL h−1, which was tested via a simulation experiment. To evaluate the actual performance of the device in the field, we obtained eight samples of cloud fog water from Shanghuang Observatory in eastern China from the summer through the fall of 2022. Collection rates varied from 62 to 169 mL h−1. For a cloud/fog sampler equipped with multiple sensors, the ACFC has excellent sampling efficiency in thick fog, sufficient cloud fog water samples can be collected in weak fog, and it can precisely identify fog mingled with rain.

Fog scavenging of particulate matters in air pollution events: Observation and simulation in the Yangtze River Delta, China

Stable atmospheric boundary layer is conducive to the accumulation of atmospheric pollution and the occurrence of fog, and fog has a removal effect on air pollution. In this study, we use the observation and WRF-Chem (Weather Research and Forecasting Model with Chemistry) simulation to analyze the factors affecting the removal efficiency in a continuous fog and haze episode from November 26 to 28, 2018 in Jiangsu Province, such as fog thickness and duration. The results show that the WRF-Chem simulation well reproduces the boundary layer characteristics in the stages of fog formation, development and dissipation. The atmospheric boundary layer provides favorable conditions for the maintenance of fog and air pollution. The inversion layer, with the maximum intensity of 3 °C per 100 m, creates favorable thermal conditions, and the water vapor advection is also conducive to the fog maintenance. The ground observation verifies the wet scavenging of PM2.5 during dense fog events. The scavenging effect is related to the fog duration, and the correlation is positive when the fog is just formed and negative when the fog is dissipating. The PM2.5 concentration decreases from 159 μg m−3 to 38 μg m−3 after the fog lasts for 11 h. The fog has a remarkable scavenging effect on PM2.5 in the vertical direction, due to the deposition effect of fog droplets on the pollutant particles. The PM2.5 concentration on the ground is lower than the vertical average in the fog area, and the scavenging effects during the dense fog periods on November 27 and 28 are 47.7 μg m−3 and 36.1 μg m−3, respectively. The fog duration is mostly concentrated in 3–17 h. When the duration of fog is 4–8 h, the scavenging effect on PM2.5 reaches the strongest, with an average PM2.5 concentration decrease of >70 μg m−3.

An advection fog event response to future climate forcing in the 2030s–2080s: a case study for Shanghai

An advection fog event response to future climate forcing in the 2030s–2080s: a case study for Shanghai

Assessment of WRF planetary boundary layer schemes in the simulation of fog events over Hungary

Accurate depiction of meteorological conditions, especially within the planetary boundary layer (PBL), is essential for fog forecasting. This study examines the sensitivity of the performance of the Weather Research and Forecast (WRF) model to the use of four different PBL schemes [Yonsei University (YSU), asymmetric convective model version 2 (ACM2), quasinormal scale elimination (QNSE), and Mellor-Yamada-Nakanishi-Niino version 3.0 (MYNN3)]. For this case study we have taken the fog event occurred in November 23-24, 2020. Surface observed temperature and relative humidity, furthermore, sounding data are compared with the output of the 36 hours, high-resolution weather forecast. The horizontal extension of the simulated fog is compared with satellite observations. The visibility is calculated from the prognostic variables of drop number concentration and mixing ratio. The simulated visibility and fog duration are validated by the visibility and fog duration evaluated by ceilometer observations. Validation of thermodynamical values such as 2-m temperature and relative humidity reveals, that during most of the simulation time, the bias is significant between the simulated and observed data. Results show that the PBL parameterization scheme significantly impacts fog microphysics also. The QNSE scheme results in unrealistic early formation of the fog, and too large liquid water content. YSU and ACM2 simulated the duration of fog to be rather short comparing with the other two PBL schemes. The best fitting with observed data is found in the case of MYNN3 PBL schemes.

Determinants and Impact of Fog, Mist and Haze phenomena: The case of Rajiv Gandhi International Airport, Hyderabad

The study aims to analyze the three phenomena - fog, mist, and haze over nine years and their impact on air traffic at Rajiv Gandhi International Airport, Hyderabad. Longer duration events of deteriorated visibility are analysed using surface in-situ data from the airport routine meteorological observations, satellite imageries, backward trajectories, reanalysed data and aerological diagrams. The climatological data predominantly explained long-lasting fog events. The stable layers of the atmosphere, availability of moisture in the planetary boundary layer, light winds, divergence (downward motion/subsidence), and radiational cooling supported the formation of fog and its sustenance. Low visibility disrupted air traffic at the study site by way of flight delays and consequential economic loss.

ANALYSIS OF THE FOGGY DAYS AND ITS KEY MECHANISMS OVER THE YANGON FLIGHT REGION


 
 
 A preliminary study of the foggy day analysis at Yangon International Airport was carried out from the previous author’s first study by using a statistical approach. This opens up a fresh line of inquiry and the opportunity to consider climatology from a different angle. Fog has occurred most frequently in the winter months of December and January. During the 19 years period from 2003 to 2021, we count 187038 occurrences of observation from Yangon International Airport using half-hourly METAR data. This information included 32966 instances of meteorological phenomena, including 379 incidences of mist and 7911 instances of foggy weather. The total number of days with fog varies from 23 to 91 per year, with the year 2014 having the most days with fog (91), and the year 2010 having the fewest days with fog (23). Winter has nearly six times the amount of fog days as summer and 1.5 times the amount of fog days as autumn. The most frequent time of day for fog was between 2300 and 01:00 UTC. Fog most usually occurs between 1800 and 2000 UTC following rains in the summer and early autumn. Data during the four to six hours prior to the commencement of the fog are subjected to classification criteria, 7610 of the 8289 fog events are only radiation and advection fog types. The first three months of the year have the highest incidence of radiation fog onset. The greatest frequencies of radiation fog onset are in February (17 %) and March (18 %). Fog episodes in 19 years were observed to be accompanied by stratus clouds 53.3 percent of the time, with low clouds being absent in 46.7 percent of them. The departure of the beginning and dissipation from the sunrise and sunset, respectively, can be used to analyze the behavior of each form of fog. At Yangon International Airport, the conditions where the fog was most likely to form were those when the minimum temperature fell between 16 and 27 °C (liquid fog). The latest discovery raises a new topic and a new option for interpreting climatology from a new perspective and making considerable progress in the predictability study, according to a well- known physicist in another setting.
 
 

Evaluation of a low-cost dryer for a low-cost optical particle counter

Abstract. The use of low-cost sensors for air quality measurements has become very popular in the last few decades. Due to the detrimental effects of particulate matter (PM) on human health, PM sensors like photometers and optical particle counters (OPCs) are widespread and have been widely investigated. The negative effects of high relative humidity (RH) and fog events in the mass concentration readings of these types of sensors are well documented. In the literature, different solutions to these problems – like correction models based on the Köhler theory or machine learning algorithms – have been applied. In this work, an air pre-conditioning method based on a low-cost thermal dryer for a low-cost OPC is presented. This study was done in two parts. The first part of the study was conducted in the laboratory to test the low-cost dryer under two different scenarios. In one scenario, the drying efficiency of the low-cost dryer was investigated in the presence of fog. In the second scenario, experiments with hygroscopic aerosols were done to determine to which extent the low-cost dryer reverts the growth of hygroscopic particles. In the second part of the study, the PM10 and PM2.5 mass concentrations of an OPC with dryer were compared with the gravimetric measurements and a continuous federal equivalent method (FEM) instrument in the field. The feasibility of using univariate linear regression (ULR) to correct the PM data of an OPC with dryer during field measurement was also evaluated. Finally, comparison measurements between an OPC with dryer, an OPC without dryer, and a FEM instrument during a real fog event are also presented. The laboratory results show that the sensor with the low-cost dryer at its inlet measured an average of 64 % and 59 % less PM2.5 concentration compared with a sensor without the low-cost dryer during the experiments with fog and with hygroscopic particles, respectively. The outcomes of the PM2.5 concentrations of the low-cost sensor with dryer in laboratory conditions reveal, however, an excess of heating compared with the FEM instrument. This excess of heating is also demonstrated in a more in-depth study on the temperature profile inside the dryer. The correction of the PM10 concentrations of the sensor with dryer during field measurements by using ULR showed a reduction of the maximum absolute error (MAE) from 4.3 µg m−3 (raw data) to 2.4 µg m−3 (after correction). The results for PM2.5 make evident an increase in the MAE after correction: from 1.9 µg m−3 in the raw data to 3.2 µg m−3. In light of these results, a low-cost thermal dryer could be a cost-effective add-on that could revert the effect of the hygroscopic growth and the fog in the PM readings. However, special care is needed when designing a low-cost dryer for a PM sensor to produce FEM similar PM readings, as high temperatures may irreversibly change the sampled air by evaporating the most volatile particulate species and thus deliver underestimated PM readings. New versions of a low-cost dryer aiming at FEM measurements should focus on maintaining the RH at the sensor inlet at 50 % and avoid reaching temperatures higher than 40 ∘C in the drying system. Finally, we believe that low-cost dryers have a very promising future for the application of sensors in citizen science, sensor networks for supplemental monitoring, and epidemiological studies.

Fog Detection and Estimation Using CALIPSO Lidar Observations

AbstractMost fog detection from space cannot differentiate fog and low stratus clouds, and cannot estimate fog deposition. This study assessed the feasibility of using spaceborne lidar observations from the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) in fog detection and estimation. We tested the method in the central Namib Desert, Namibia, where frequent fog events occur and fog observations are available. Results showed the CALIPSO backscatter signal at 532 nm can differentiate low clouds and fog due to its high‐resolution vertical profiles. Backscatter signals during fog events were significantly higher than those during non‐fog periods. The R2 between backscatter signals and fog observations was 0.85. Moreover, the backscatter signal was also sensitive to relative humidity variation (R2 = 0.66). These results indicate that the CALIPSO data are feasible to estimate fog occurrence and deposition, providing a new perspective for space‐based fog studies.

The role of radiative cooling and leaf wetting in air–leaf water exchange during dew and radiation fog events in a temperate grassland

During prolonged dry periods, non-rainfall water (NRW) plays a vital role as water input into temperate grasslands, affecting the leaf surface water balance and plant water status. Previous chamber and laboratory experiments investigated air–leaf water exchange during dew deposition, but overlooked the importance of radiative cooling on air–leaf water exchange because the chamber is a heat trap, preventing radiative cooling. To complement these previous studies, we conducted a field study, in which we investigated the effect of radiatively-induced NRW inputs on leaf water isotope signals and air–leaf water exchange in a temperate grassland during the dry-hot summers of 2018 and 2019. We carried out field measurements of the isotope composition of atmospheric water vapor, NRW droplets on foliage, leaf water, xylem water of root crown, and soil water, combined with meteorological and plant physiological measurements. We combined radiation measurements with thermal imaging to estimate leaf temperatures using different methods, and computed the corresponding leaf conductance and air–leaf water exchange. Our results indicate that radiative cooling and leaf wetting induced a switch of direction in the net water vapor exchange from leaf-to-air to air-to-leaf. The leaf conductance and air–leaf water exchange varied by species due to the species-specific biophysical controls. Our results highlight the ecological relevance of radiative cooling and leaf wetting in natural temperate grasslands, a process which is expected to influence land surface water budgets and may impact plant survival in many regions in a drier climate.

Regional atmospheric circulation patterns driving consecutive fog events in the United Arab Emirates

In this study, the link between the occurrence of consecutive fog days in the United Arab Emirates (UAE) and the associated synoptic-scale circulation is investigated. This is particularly pertinent, as such a link may provide an important predictive skill for a phenomenon that has a pronounced impact on road and air traffic but is still poorly simulated by numerical models. A cluster analysis of all consecutive fog days from January 1983 to December 2021 indicated that the positive phase of the East Atlantic/Western Russia teleconnection pattern, Eastern Pacific La Nina events, and the circumglobal wavenumber 5 pattern promote the occurrence of multiple fog days in the UAE. The fog's radiative impacts, as estimated from satellite data, revealed that the fog in the UAE is more optically thick than that observed elsewhere. A trend analysis over the period 1983 to 2021 revealed that consecutive fog events have become more frequent and longer-lasting but less intense (i.e., associated with higher values of visibility). The fog's spatial extent over the UAE at its mature stage has also decreased over time. An analysis of the trends in the surface and top of atmosphere (TOA) radiative fluxes indicated that over the period 2000–2021, the fog clouds have likely become less reflective, with a statistically significant decrease in the surface downward shortwave and TOA upward longwave radiation fluxes. Long-term measurements of fog microphysics in the region are needed to better understand the variability in the properties of the fog cloud droplets.

Cold waves in Terai region of Nepal and farmer’s perception of the effect of fog events and cold waves on agriculture

Cold waves are considered one of the important extreme weather events affecting winter crop production in the Indo-Gangetic Plain (IGP). In spite of media coverage of extreme cold events in the Terai area of Nepal (Nepal section of IGP) in recent years, few studies on this topic were found. This study investigates cold waves and their impact on agriculture during winter in the Terai region of Nepal. Historical daily maximum and minimum temperature data from six stations in the Terai (Dhangadhi, Nepalgunj, Bhairahawa, Simara, Janakpur, and Biratnagar) during 1971–2015 were analyzed to study the occurrence of cold days, cold nights, extreme cold days, extreme cold nights, cold wave days, and extreme cold wave days in the Terai. The average number of cold days per annum ranges from 15.6 to 17.9 days and the extreme cold days per annum ranges from 3.2 to 3.6 days in the Terai. Except for Nepalgunj, all the Terai stations show statistically significant increasing trends in the frequency of cold days and extreme cold days over the last four decades. Similarly, the average number of cold wave days varies from 9.2 to 13.8 per annum and the average number of extreme cold wave days varies from 1.4 to 3.8 days in the Terai region of Nepal. By comparing the co-occurrence of foggy days and cold and extreme cold wave days at Biratnagar, Simara, Bhairahawa, and Nepalgunj airport, it is also observed that most of the cold and extreme cold wave days are also foggy days. The perception of farmers regarding the effect of fog and cold wave events was explored through focus group discussions at Dhanusha and Sunsari districts of Nepal and found that the fog and cold events have significantly affected their winter crops, livestock, and their day-to-day life.

The Effect of Sea Surface Temperature on Relative Humidity and Atmospheric Visibility of a Winter Sea Fog Event over the Yellow-Bohai Sea

Sea fog is one of the main types of dangerous weather affecting offshore operations. The sea surface temperature (SST) has an important influence on the water vapor content and intensity of sea fog. In order to study the impact of SST on local relative humidity and atmospheric visibility, a sea fog episode that occurred over the Yellow Sea and Bohai Sea on 21 January 2013 was investigated through observational data, reanalysis data, and Weather Research and Forecasting (WRF) simulation. The results show that the influence of SST on the distribution of sea fog with different properties is inconsistent. Based on the time-varying equation of relative humidity, the changes in the advection, radiation, and turbulence effects on the relative humidity with respect to SST are explored through control and sensitivity experiments. The results show that the advection effect plays a decisive role in the generation and dissipation stages of sea fog. The increase (decrease) in SST weakens (strengthens) the radiative cooling and relative humidity. The contribution magnitude of advection effect to relative humidity is 10−5, while those of radiation and turbulence are 10−6 and 10−7, respectively. The atmospheric visibilities in the Bohai Sea and northern Yellow Sea decrease with increasing SST, which are mainly affected by the positive turbulence effect; whereas the atmospheric visibility in the central and southern Yellow Sea increases with SST, which is mainly influenced by the combined effects of U-direction advection, radiation, and turbulence. The stability related to boundary layer height plays an important role in water vapor condensation.

Source apportionment and evolution of N-containing aerosols at a rural cloud forest in Taiwan by isotope analysis

Abstract. Ammonium and nitrate are major N-containing aerosol components. The deposition of N-containing aerosols has impacts on regional ecology and the biogeochemical cycle. In this study, aerosols in a rural cloud forest (Xitou in Taiwan) were studied using 15N and 18O isotope analysis to assess the sources and formation pathways of the local N-containing aerosols linking to a metropolitan. Aerosol samples of different size ranges were collected using a micro-orifice uniform deposit impactor (MOUDI) on a half-day basis in December 2018. The chemical functional groups were analyzed using a Fourier-transform infrared spectroscopy with attenuated total reflection (FTIR-ATR) technique, while the isotope analysis was performed using a gas chromatography–isotope ratio mass spectrometer (GC–IRMS). The average measured aerosol concentration (PM10) was 0.98 (ranging from 0.15 to 3.31) and 0.25 (ranging from 0.00 to 1.51) µg m−3 for NH4+ and NO3-, respectively. In general, a higher concentration than nighttime was observed during the daytime by a factor of 1.5–6, likely due to the transportation of pollutants from upper-stream urban and industrial regions through the local sea breeze combined with valley wind. The presence of fog can further elevate the concentration by a factor of 2–3, resulting from the stronger inversion and lower boundary layer height. The higher NH4+ concentration in fine particles under foggy conditions corresponds to submicron-sized NO3- formation via aqueous-phase dissolution with NH4+ neutralization. Furthermore, the higher RH during fog events shifted the mass distribution of aerosol functional groups to a larger mode size. By comparing the δ15N value directly or through the analysis using a statistical isotope mixing model, MixSIAR, NH4+ probably originated from the industries, coal-fired power plants (CFPPs), or fertilizer plants, while NO3- might be contributed from the CFPP, industrial or urban sources. The overall δ18O of NO3- is +72.66 ‰ ± 3.42 ‰, similar to that in other winter Asian studies, suggesting the major formation pathway via O3 oxidation (δ18O=+72.5 ‰ to 101.67 ‰). However, a lower δ18O (<+67 ‰) for particles less than 0.56 µm during foggy daytime suggests the local contribution via the peroxyl radical oxidation before partitioning into aerosol phase under foggy conditions. Overall, the δ15N and δ18O distribution profiles as a function of particle size in the studied rural forest site reveal the evolution of aerosol composition from remote coastal regions with chemical processes along the transport process, which can be further affected by weather conditions such as fog events.

Simulations of sea fog case impacted by air–sea interaction over South China Sea

A sea fog event in the South China Sea was simulated using a coupled ocean–atmosphere model (WRF for the atmosphere and ROMS for the ocean). Offshore and onshore visibility, liquid water content, air temperature, humidity, and wind speed observations and MICAPS data were utilized to validate the model results. The results of the coupled model were also compared with those of the uncoupled atmosphere model. Sea fog duration in the coupled model was closer to offshore and onshore observations, but the uncoupled model emptily forecasted offshore fog, and underreported onshore fog. Air–sea temperature difference played an important role in regulating the formation and dissipation of sea fog. The decrease of sea surface temperature in the coupled model cooled the low-level atmosphere, promoted the condensation of low-level water vapor, and increased the low-level water vapor. The decrease of air–sea temperature difference strengthened the low-level stable stratification, which weakened the horizontal wind speed and favored the formation and development of sea fog. Rising wind speed was the major driver of fog dissipation.

Operational Probabilistic Fog Prediction Based on Ensemble Forecast System: A Decision Support System for Fog

One of the well-known challenges of fog forecasting is the high spatio-temporal variability of fog. An ensemble forecast aims to capture this variability by representing the uncertainty in the initial/lateral boundary conditions (ICs/BCs) and model physics. The present study highlights a new operational Ensemble Forecast System (EFS) developed by the Indian Institute of Tropical Meteorology (IITM), Pune, to predict the fog over the Indo-Gangetic Plain (IGP) region using the visibility (Vis) diagnostic algorithm. The EFS framework comprises the WRF model with a 4 km horizontal resolution, initialized by 21 ICs/BCs. The advantages of probabilistic fog forecasting have been demonstrated by comparing control (CNTL) and ensemble-based fog forecasts. The forecast is verified using fog observations from the Indira Gandhi International (IGI) airport during the winter months of 2020–2021 and 2021–2022. The results show that with a probability threshold of 50%, the ensemble forecasts perform better than the CNTL forecasts. The skill scores of EFS are relatively promising, with a Hit Rate of 0.95 and a Critical Success Index of 0.55; additionally, the False Alarm Rate and Missing Rate are low, with values of 0.43 and 0.04, respectively. The EFS could correctly predict more fog events (37 out of 39) compared with the CNTL forecast (31 out of 39) and shows the potential skill. Furthermore, EFS has a substantially reduced error in predicting fog onset and dissipation (mean onset and dissipation error of 1 h each) compared to the CNTL forecasts.

Fog nowcasting over the IGI airport, New Delhi, India using decision tree

In the modeling framework, forecasting fog onset and its dissipation time is challenging work, which typically becomes a threshold problem in very dense fog (50m <Vis < 0m) cases. In addition, poor/inaccurate fog forecasts may create dangerous situations for the airline sector, where the accuracy of short-range forecasts is very essential. In the present study, we have developed a statistical tool based on real-time observational data to nowcast the dense fog events at Indira Gandhi International (IGI) Airport, New Delhi, India. The high temporal resolution observational dataset is available from three years of the Winter Fog Experiment (WiFEX) campaign. The performance of this tool for 6 dense fog events is verified with observed visibility data. The results reveal that this tool has the significant nowcasting skill for very dense fog prediction with a success rate of around 66%. This satisfactory agreement between the nowcasting tool and visibility builds the confidence to predict more dense/very dense fog events in the future after some fine-tuning in the present nowcasting tool.

Seasonal and Microphysical Characteristics of Fog at a Northern Airport in Alberta, Canada

Reduction in visibility (Vis) due to fog is one of the deadliest severe weather hazards affecting aviation and public transportation. Nowcasting/forecasting of Vis reduction due to fog using current models is still problematic, with most using some type of empirical parameterization. To improve the models, further observational studies to better understand fog microphysics and seasonal variability are required. To help achieve these goals, the seasonal and microphysical characteristics of different fog types at Cold Lake airport (CYOD), Alberta, Canada were analyzed using hourly and sub-hourly METAR data. Microphysical and meteorological measurements obtained using the DMT Fog Monitor FM-120 and the Vaisala PWD22 were examined. The results showed that radiation fog (RF) dominates at CYOD in summer while precipitation, advection and cloud-base-lowering fogs mostly occur in fall and winter. All fog types usually form at night or early morning and dissipate after sunrise. The observed dense fog events (Vis < 400 m) were mainly caused by RF. The observed mean fog particle spectra (n(D)) for different fog types and temperatures showed bimodal n(D) (with two modes near 4 μm and 17–25 μm; the maximum total number concentration (Nd) was 100 cm−3 and 20 cm−3, respectively, corresponding to each mode). Parameterizations of Vis as a function of liquid water content (LWC) and Nd were developed using both the observed Vis and calculated Vis based on n(D). It was found that the observed Vis was higher than the calculated Vis for warm fog with LWC > 0.1 gm−3 and most of the mass was contributed by the large drops. Based on the observed Vis, the relative error of the visibility parameterization as a function of both LWC and Nd (32%) was slightly lower than that (34%) using LWC alone for warm fogs.

An optimal estimation algorithm for the retrieval of fog and low cloud thermodynamic and micro-physical properties

Abstract. A new generation of cloud radars, with the ability to make observations close to the surface, presents the possibility of observing fog properties with better insight than was previously possible. The use of these instruments as part of an operational observation network could improve the prediction of fog events, something which is still a problem for even high-resolution numerical weather prediction models. However, the retrieval of liquid water content (LWC) profiles from radar reflectivity alone is an under-determined problem, something which ground-based microwave radiometer observations can help to constrain. In fact, microwave radiometers are not only sensitive to temperature and humidity profiles but are also known to be instruments of reference for the liquid water path. By providing the thermodynamic state of the atmosphere, to which the formation and evolution of fog events are highly sensitive, in addition to accurate liquid water path, which can be used to constrain the LWC retrieval from the cloud radar alone, combining microwave radiometers with cloud radars seems a natural next step to better understand and forecast fog events. To that end, a newly developed one-dimensional variational (1D-Var) algorithm designed for the retrieval of temperature, specific humidity and liquid water content profiles with both cloud radar and microwave radiometer (MWR) observations is presented in this study. The algorithm was developed to evaluate the capability of cloud radar and MWR to provide accurate LWC profiles in addition to temperature and humidity in view of assimilating the retrieved profiles into a 3D- and 4D-Var operational assimilation system. The algorithm is firstly tested on a synthetic dataset, which allows the evaluation of the developed algorithm in idealised conditions. This dataset was constructed by perturbing a high-resolution forecast dataset of fog and low-cloud cases by its expected errors. The algorithm is then tested with real data from the recent field campaign SOFOG-3D, carried out with the use of LWC measurements made from a tethered balloon platform. As expected, results from the synthetic dataset study were found to contain lower errors than those found from the retrievals on the dataset of real observations. It was found that LWC can be retrieved in idealised conditions with an uncertainty of less than 0.04 g m−3. With real data, as expected, retrievals with a good correlation (0.7) to in situ measurements were found but with a higher uncertainty than the synthetic dataset of around 0.06 g m−3 (41 %). This was reduced to 0.05 g m−3 (35 %) when an accurate droplet number concentration could be prescribed to the algorithm. A sensitivity study was conducted to discuss the impact of different settings used in the 1D-Var algorithm and the forward operator. Additionally, retrievals of LWC from a real fog event observed during the SOFOG-3D field campaign were found to significantly improve the operational background profiles of the AROME (Application of Research to Operations at MEsoscale) model, showing encouraging results for future improvement of the AROME model initial state during fog conditions.