Advection Fog Events Research Articles

An Online Assimilation Method to Improve the Numerical Forecast of Sea Fog Using Microwave Radiometer‐Retrieved Cloud Water Path

AbstractNumerical forecast of the sea fog is sensitive to the initial moist stratification within the marine atmospheric boundary layer (MABL). This study develops an online assimilation method to improve the MABL thermal and moist structures in sea fog ensemble forecasts based on the Weather Research and Forecasting model and Grid‐point Statistical Interpolation/EnKF system. It uses the satellite‐retrieved cloud water path (CWP) as the indicator of sea fog and low‐level stratus to determine the best ensemble members at each grid point. The relative humidity and cloud water profiles are extracted from the best members to generate a series of pseudo‐observations, which are assimilated to update all the members by EnKF method. The new method significantly improves the ensemble forecast of five widespread advection fog events over the Yellow Sea, which can be attributed to the decrease of both missed and spurious fog areas. The case study shows that assimilating the information of both humidity and cloud water outperforms assimilating either of them, while the impact of directly assimilating CWP observation is insignificant. The analysis increments of cloud water, thermal and moist structures in MABL together contribute to the correction of forecasted sea fog. The generation of pseudo‐observations can use the dynamic compatibility of the model to alleviate the impact of erroneous data in the observation, leading to the low sensitivity of the new method to CWP retrieval error.

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

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

WRF sensitivity simulations of a dense advection fog event in Istanbul

WRF sensitivity simulations of a dense advection fog event in Istanbul

Fog probabilistic forecasting using an ensemble prediction system at six airports in Iran for 10 fog events

AbstractLow visibility conditions due to fog affect air traffic and, in some cases, are the leading cause of aviation accidents. Accurate forecasting of fog can lead to a significant reduction of human and financial losses. Numerical weather prediction (NWP) models have limitations in simulating fog accurately for operational purposes. However, recent studies have confirmed that ensemble‐based forecasts are effective in fog forecasting. In this study, a multi‐physics ensemble prediction system (EPS) was used to simulate the occurrence of several radiation, cloud base lowering (CBL), and advection fog events that occurred at Arak, Ardebil, Hamedan, Orumiyeh, Rasht and Shahrekord airports located across Iran, during January and December 2015. The multi‐physics EPS including a reference deterministic forecast consists of 16 different configurations of the weather research and forecasting (WRF) model and was run from 12 UTC 12 January to 12 UTC 16 January 2015 and from 00 UTC 27 December to 12 UTC 31 December 2015 to simulate fog occurrences at six airports. SW99 and G2009 visibility algorithms were applied to the EPS outputs to predict the fog. The advantages of probabilistic fog forecasting in this study were shown by comparing the reference forecast and the ensemble‐based forecasts of fog events. The results showed that by considering a probability threshold of 37.5%, 50% and 62.5% for the ensemble forecasts, the EPS outperformed the deterministic fog forecasts obtained from a deterministic reference forecast. Such that, with 37.5% and 50% probability thresholds, the equitable threat score (ETS) was much higher than the ETS for the reference deterministic fog forecast. Also, the EPS with 37.5% and 50% probability thresholds could correctly predict 3 and 4 (out of 10) more fog events compared with the reference deterministic forecast.

A Probability-Based Daytime Algorithm for Sea Fog Detection Using GOES-16 Imagery

Fog is a hazardous weather event that can endanger navigation, aviation, and transportation. While human has several limitations in detecting and forecasting offshore fog, satellite remote sensing offers cost-effective images. In this study, a probability-based daytime sea fog detection algorithm, applied to geostationary operational environmental satellite (GOES) 16 satellite data over the Grand Banks offshore Eastern Canada, is presented and compared with the National Oceanographic and Atmospheric Administration (NOAA)'s Low Instrument Flight Rules (LIFR) probability map. Initially, clear-sky and ice cloud classes were delineated in the GOES-16 image and then the remaining pixels were assigned a fog probability by conducting small droplet proxy, spatial homogeneity, and temperature difference tests. Moreover, a green band was linearly interpolated using the first three bands of GOES-16 images to generate pseudotrue color composites. The resulting maps were evaluated both during an extended sea fog event and using several statistical measures. The average detection probability for the observed advection fog events was 66% for the proposed method, while that for NOAA's LIFR map was 38%. Furthermore, by thresholding the generated maps at the probability of 60%, the false alarm rate, probability of detection, hit rate, and Hanssen–Kuiper skill score were 0.09, 0.77, 0.83, and 0.68, respectively. The proposed method is operationally being used in this region to detect and monitor sea fog, facilitating safe navigation and aviation. This is the first study that uses GOES-16 for daytime fog detection and discusses a satellite-based solution for fog modeling in Grand Banks, NL.

The Impact of Turbulent Diffusion Driven by Fog‐Top Cooling on Sea Fog Development

AbstractTurbulent mixing driven by cloud/fog‐top radiative and evaporative cooling (hereafter top‐driven diffusion), including top‐down mixing and top‐cooling entrainment, is critical for the development of cloud/fog. Previous work mainly focused on impacts of top‐driven diffusion on stratocumulus‐topped planetary boundary layer (PBL) and radiation fog over land. However, its exact role in sea fog process is yet unclear. Using the Weather Research and Forecasting (WRF) model with the updated Yonsei University (YSU) PBL scheme, in which the ysu_topdown_pblmix option is responsible for the parameterization of the top‐driven diffusion, the present study investigates respective effects of the top‐cooling entrainment and top‐down mixing on an advection fog event over the Yellow Sea that occurred on 20–21 May 2014. Evaluation of the modeling results confirms that the top‐driven diffusion significantly improves the sea fog simulation. The top‐cooling entrainment allows more relatively quiescent air above the fog top to mix into the sea fog layer, leading to a higher mixed layer with increased temperature and decreased humidity. With the enhancement of buoyant turbulence, the warming and dehumidifying moderately lift the sea fog bottom and reduce the false fog area. The top‐down mixing performs quite well in reproducing a stable structure near the fog top, which results from the added turbulent mixing determined by a revised K profile. Note that the effect of top‐driven diffusion shows high sensitivities to shortwave radiation and vertical resolution. Besides the fog‐top cooling, the fog‐droplet sedimentation is necessary to be included in the top‐cooling entrainment.

Automatic nighttime sea fog detection using GOES-16 imagery

Accurately detecting sea fog is important for oil and gas operations in the Grand Banks, Newfoundland and Labrador (NL), Canada. Although the Grand Banks is one of the foggiest places in the world, there is no remote sensing technique specifically developed for fog detection in this region. Therefore, an automatic approach was proposed in this study to detect Nighttime Sea Fog (NSF) and distinguish it from clear sky and ice cloud. To this end, Geostationary Operational Environmental Satellite system-16 (GOES-16) imagery along with several ancillary datasets were employed. Selecting the Optimum Threshold Value (OTV) for identifying NSF in satellite images acquired at different times was also extensively discussed. The NSF maps obtained by the proposed method for 25 advection fog events in the study area were compared to surface-based weather observations (i.e., visibility data) and the National Oceanic and Atmospheric Administration's (NOAA) global fog/low cloud products. The Probability of Detection (POD), False Alarm Rate (FAR), Hanssen-Kuiper Skill Score (KSS), and Equitable Threat Score (ETS) were 0.80, 0.08, 0.72, and 0.57, respectively, demonstrating the potential of the proposed NSF detection algorithm. Additionally, the results showed that the proposed method was better at discriminating Grand Banks fog than NOAA's algorithm in terms of both visual and statistical accuracies.

Sensitivity of WRF simulations with the YSU PBL scheme to the lowest model level height for a sea fog event over the Yellow Sea

The lowest model level is the interface of energy and mass exchanging between the surface and planetary boundary layer (PBL). Previous studies mostly examined the role of the lowest model level height (z1) in simulating the continental PBL processes. The impact of z1 on simulating marine processes (e.g., sea fog), however, remains unclear. The present study explores the sensitivity of the Weather Research and Forecasting (WRF) model with the Yonsei University (YSU) PBL scheme to z1 for an advection fog event occurred on 27 March 2012 over the Yellow Sea. Seven experiments with various z1 (28, 22, 14, 8, 4, 1 and 0.4 m) are conducted.Evaluations for the continental PBL indicate that z1 below 8 m is irrational in simulating surface temperature and PBL height over land. However, the model with z1= 8 m gives the best performance in terms of reproducing sea fog. When z1 gets below 8 m, the sea fog occurs too early and the fog area is too small. As z1 exceeds 8 m, the fog forms too late and the fog area becomes underestimated. These model sensitivities can be explained by the impact of z1 on virtual potential temperature at z1 [θv(z1)]. Since the heat capacity of the air in the lowest model layer is proportional to z1, a lower (higher) z1 causes a quicker (slower) response of θv(z1) to surface cooling, thus leading to an earlier (later) sea fog formation. After the fog onset, especially for a lower z1, the variation of θv(z1) is dominated by turbulent heating that transports warmer air above to the very shallow lowest model layer, resulting in a lower vertical growth and even earlier dissipation of the sea fog.

WRF model sensitivity to choice of PBL and microphysics parameterization for an advection fog event at Barkachha, rural site in the Indo-Gangetic basin, India

The present study evaluates the performance of four planetary boundary layer (PBL) parameterization schemes combined with five cloud microphysics schemes in Weather Research Forecasting (WRF) model, specifically for an advection fog event occurred during 4–6 December 2014 at Barkachha, rural site in the Indo-Gangetic plain (IGP). For this purpose, the model was configured over the IGP with 2-km horizontal resolution, and results are compared with detailed micrometeorological data (surface meteorological parameters and fluxes, radiative fluxes, and surface layer wind profiles) gathered during the Cloud Aerosol Interaction and Precipitation Enhancement Experiment (CAIPEEX) Integrated Ground Observational Campaign (IGOC) site located in the IGP. The meteorological conditions conducive for the fog formation have been evaluated. All of the tested PBL-microphysics combination showed substantial bias for surface temperature, radiation fluxes, and wind speed. None of the combination found to be superior in predicting the fog event; however, the local MYNN2.5 combination with the WSM3, WSM6, and Lin microphysics obtained slightly better result at the study location. In general, judging from all simulations of liquid water content (as an indicator for the fog), the above combinations were able to simulate the current fog event but the fog onset, duration, and dissipation were particularly offset.

Numerical simulations of an advection fog event over Shanghai Pudong International Airport with the WRF model

A series of numerical simulations is conducted to understand the formation, evolution, and dissipation of an advection fog event over Shanghai Pudong International Airport (ZSPD) with the Weather Research and Forecasting (WRF) model. Using the current operational settings at the Meteorological Center of East China Air Traffic Management Bureau, the WRF model successfully predicts the fog event at ZSPD. Additional numerical experiments are performed to examine the physical processes associated with the fog event. The results indicate that prediction of this particular fog event is sensitive to microphysical schemes for the time of fog dissipation but not for the time of fog onset. The simulated timing of the arrival and dissipation of the fog, as well as the cloud distribution, is substantially sensitive to the planetary boundary layer and radiation (both longwave and shortwave) processes. Moreover, varying forecast lead times also produces different simulation results for the fog event regarding its onset and duration, suggesting a trade-off between more accurate initial conditions and a proper forecast lead time that allows model physi-cal processes to spin up adequately during the fog simulation. The overall outcomes from this study imply that the complexity of physical processes and their interactions within the WRF model during fog evolution and dissipation is a key area of future research.

Atmospheric Boundary Layer Structure and Turbulence during Sea Fog on the Southern China Coast

Abstract Small-scale turbulence has an essential role in sea-fog formation and evolution, but is not completely understood. This study analyzes measurements of the small-scale turbulence, together with the boundary layer structure and the synoptic and mesoscale conditions over the life cycle of a cold advection fog event and a warm advection fog event, both off the coast of southern China. The measurement data come from two sites: one on the coast and one at sea. These findings include the following: 1) For cold advection fog, the top can extend above the inversion base, but formation of an overlaying cloud causes the fog to dissipate. 2) For warm advection fog, two layers of low cloud can merge to form deep fog, with the depth exceeding 1000 m, when strong advection of warm moist air produces active thermal-turbulence mixing above the thermal-turbulence interface. 3) Turbulence near the sea surface is mainly thermally driven for cold advection fog, but mechanically driven for warm advection fog. 4) The momentum fluxes of both fog cases are below 0.04 kg m−1 s−2. However, the sensible and latent heat flux differ between the cases: in the cold advection fog case, the sensible and latent heat fluxes are roughly upward, averaging 2.58 and 26.75 W m−2, respectively; however, in the warm advection fog case, the sensible and latent heat flux are mostly downward, averaging −6.98 and −6.22 W m−2, respectively. 5) Low-level vertical advection is important for both fogs, but has a larger influence on fog development in the warm advection fog case.

The turbulent structure and transport in fog layers observed over the Tianjin area

This paper investigates the vertical structure and turbulence activities in fog events. Three fog cases that occurred in the winter of 2010 over Tianjin, China were selected, including two advection–radiation fog events and an advection fog event. Field observations collected at a 255-m tall meteorological tower in Tianjin were analyzed, including turbulence measurements using the eddy covariance systems installed at three levels, measurements of temperature, horizontal wind and humidity collected at 15 levels, surface radiation fluxes and horizontal visibility. The results suggest that the advection fog was more enduring and thicker than the advection–radiation fog. The fog events were characterized by low wind speed throughout the fog layer. A temperature inversion and low-level jet were observed above the advection–radiation fog layer. The surface net radiation reflected some differences among the fog events. The collapse of turbulence was a necessity for the formation fog, and moderate turbulence was favorable to the development and maintenance. The heat and water vapor fluxes in the advection–radiation fog were weaker than those in the advection fog, in which the stratification was slightly unstable. The relationships among the turbulent transport efficiencies of water vapor, temperature and momentum were examined. The results suggest the applicability of local similarity in the fog layer for the momentum transport efficiency.

Predictability of an Advection Fog Event over North China. Part I: Sensitivity to Initial Condition Differences

Abstract The predictability of a dense advection fog event on 21 February 2007 over north China (NC) is investigated with ensemble simulations using the Weather Research and Forecasting Model (WRF). Members with the best and worst simulation are selected from the ensemble, and their initial condition (IC) differences are explored. To test the sensitivity of fog simulation to those differences, the model is initialized with ICs that change linearly from the worst member to the best member, and the changes in simulated results are examined. The improvement in simulations due to the linear improvement of ICs is found to be monotonic. The IC differences at lower levels are of more influence to the simulation than IC differences at higher levels. By removing the IC differences of each meteorological variable individually, it is found that improvements in potential temperature and horizontal wind are more important than that of water vapor mixing ratio in this case. Additionally, the linear improvement in each meteorological variable also contributes monotonically to the simulated results. The budget analyses of the tendency of potential temperature and water vapor mixing ratio show that turbulence mixing and advection are the major factors contributing to the formation of fog. The correct initial temperature field ensures the formation and maintenance of an inversion, and the correct initial wind field ensures the correct transport of temperature and moisture in this case. Further discussion examines the reasons for the monotonic behavior in the simulation improvement.

Climatological Characteristics of Fog at Cape Town International Airport

Abstract The character of fog at Cape Town International Airport (CTIA), South Africa, is investigated, using 13 yr of historical hourly data during the period 1997–2010. Hourly surface observations are used to identify fog types that most frequently affect CTIA, by using an objective fog-type classification method that classifies fog events according to their primary formation mechanisms. Fog-type characteristics, such as the minimum visibility, duration, and time of onset and dissipation, are determined. Self-organizing maps (SOMs) are used to determine the dominant synoptic circulation types associated with fog at CTIA. Results show that radiative processes are the most common cause of fog, with an enhanced likelihood of radiation fog in winter. Cloud-base-lowering fog and advection fog events are more likely at the start of the fog season. As the fog season (March–August) progresses, synoptic circulations associated with fog change from a dominant low pressure pattern along the west coast in March and April to a dominant interior high pressure pattern toward July and August. The techniques presented may be used to provide aviation forecasters with a detailed description of the types of fog that frequently occur, their characteristics, and the synoptic circulation associated therewith.

Comparison of advection and steam fogs: From direct observation over the sea

Sea fog occurs frequently over the Yellow Sea in spring and summer, which causes costly or even catastrophic events including property damage, marine accidents, public health and financial losses. Case studies of advection and steam fogs using direct observation over the sea are constructed to better understand their formation, evolution and dissipation. A southerly wind supplies moisture to initiate advection fog events (AFs). Approximately −100 to −200W m−2 of latent heat flux and −70W m−2 of sensible heat flux during mature AFs are characterized with stable stratification which maintains dense fog by limiting downward mixing of dryer air. Steam fogs (SFs) develop from flow of cold air over warmer water, but are normally of limited persistence. During the SFs, a northerly wind decreases the air temperature below the sea surface temperature, which increases the relative humidity through evaporation from the warmer ocean. During mature SF, 360W m−2 of latent heat flux and 150W m−2 of sensible heat flux are characterized with neutral and unstable atmospheric conditions. The increase in wind speed and wind shear mixes dry air downward to the surface and limits the duration of the SF.

Event-Based Climatology and Typology of Fog in the New York City Region

Abstract The character of fog in a region centered on New York City, New York, is investigated using 20 yr of historical data. Hourly surface observations are used to identify fog events at 17 locations under the influence of various physiographic features, such as land–water contrasts, land surface character (urban, suburban, and rural), and terrain. Fog events at each location are classified by fog types using an objective algorithm derived after extensive examination of fog formation processes. Events are characterized according to frequency, duration, and intensity. A quantitative assessment of the likelihood with which mechanisms leading to fog formation are occurring in various parts of the region is obtained. The spatial, seasonal, and diurnal variability of fog occurrences are examined and results are related to regional and local influences. The results show that the likelihood of fog occurrence is influenced negatively by the presence of the urban heat island of New York City, whereas it is enhanced at locations under the direct influence of the marine environment. Inland suburban and rural locations also experience a considerable amount of fog. As in other areas throughout the world, the overall fog phenomenon is a superposition of various types. Precipitation fog, which occurs predominantly in winter, is the most common type. Fog resulting from cloud-base lowering also occurs frequently across the region, with an enhanced likelihood in winter and spring. A considerable number of advection fog events occur in coastal areas, mostly during spring, whereas radiation fog occurs predominantly at suburban and rural locations during late summer and early autumn but also occurs during the warm season in the coastal plain of New Jersey as advection–radiation events.