Sea Fog Events Research Articles

Multi-process atmospheric particle number size distribution over the Bohai Sea: Insights from cruise and onboard unmanned aerial vehicle observations

A better understanding of the particle number size distribution aids to better assess the direct and indirect effects of particles on air quality and climate. In this study, particle number concentrations (PNCs) and size distribution along with chemical components, gaseous pollutants, and meteorological parameters were measured during a cruise campaign over the Bohai Sea. Four distinct periods were identified, showing significant impacts from continental outflows and ocean emissions on the particle number size distributions. Under the strong terrestrial pollution transport period, an obvious increase of PNCs below 1 μm was observed. The significant correlation between black carbon (BC) and PNCs in the size range of 0.35–1.0 μm suggested aging of aerosols during the long-range transport. An invaded dust pollution significantly reduced PNCs below 0.5 μm, while tripled the number concentration of particles larger than 0.5 μm. The ocean moisture facilitated the mixing between dust and secondary aerosols. An intense sea fog event observed during the cruise slightly increased PNCs smaller than 1 μm while decreasing larger ones. The correlations between PNCs below 0.5 μm and BC as well as SO2 demonstrated the impact of shipping activities on the formation of fresh particles. Aerosol vertical profiles in the marine boundary layer were measured by an unmanned aerial vehicle platform. The vertical patterns of particle number size distribution were largely governed by the origins of transported air masses and profiles of wind fields. Observations showed that the particle distribution in the lower marine boundary layer was typically inhomogeneous and rapidly evolving with time, highlighting the importance of strengthening the vertical observations of atmospheric parameters over the ocean.

Short‐Term Sea Fog Area Forecast: A New Data Set and Deep Learning Approach

AbstractPrompt and precise forecast of sea fog regions ensures maritime navigational safety. This paper establishes a prompt and precise forecast of sea fog regions that ensure maritime navigational safety. This paper establishes a multivariable sea fog forecast (MV‐SFF) data set and proposes a deep learning‐based forecast method named rich‐element aggregated (REA) for short‐term forecasts. The MV‐SFF data set contains 122 sea fog events from 2010 to 2020. Each event in the MV‐SFF data set includes meteorological variables from reanalysis data and geostationary ocean color imager satellite images captured hourly on the day of the sea fog occurrence. This data set aims to comprehensively utilize meteorological elements and remote sensing observations to predict the spatial changes of sea fog areas in the next 7 hours. The proposed REA model can extract and integrate features from historical meteorological elements of different types, times, and spatial locations. In addition, REA utilizes a position‐aware edge detection mechanism to locate the exact position of sea fog. We perform a seven‐hour ahead forecast starting from 09:16 local time and show promising forecasting results, which demonstrate the fog area forecast ability of our model. Compared with existing advanced deep learning networks, the REA model is superior in performance and stability.

Daytime Sea Fog Identification Based on Multi-Satellite Information and the ECA-TransUnet Model

Sea fog is a weather hazard along the coast and over the ocean that seriously threatens maritime activities. In the deep learning approach, it is difficult for convolutional neural networks (CNNs) to fully consider global context information in sea fog research due to their own limitations, and the recognition of sea fog edges is relatively vague. To solve the above problems, this paper puts forward an ECA-TransUnet model for daytime sea fog recognition, which consists of a combination of a CNN and a transformer. By designing a two-branch feed-forward network (FFN) module and introducing an efficient channel attention (ECA) module, the model can effectively take into account long-range pixel interactions and feature channel information to capture the global contextual information of sea fog data. Meanwhile, to solve the problem of insufficient existing sea fog detection datasets, we investigated sea fog events occurring in the Yellow Sea and Bohai Sea and their territorial waters, extracted remote sensing images from Moderate Resolution Imaging Spectroradiometer (MODIS) data at corresponding times, and combined data from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), cloud and sea fog texture features, and waveband feature information to produce a manually annotated sea fog dataset. Our experiments showed that the proposed model achieves 94.5% accuracy and an 85.8% F1 score. Compared with the existing models relying only on CNNs such as UNet, FCN8s, and DeeplabV3+, it achieves state-of-the-art performance in sea fog recognition.

Data-to-data translation-based nowcasting of specific sea fog using geostationary weather satellite observation

Dense sea fog events are responsible for many traffic accidents and fatalities. Research has demonstrated the difficulty in distinguishing sea fog from clouds. This study presents a sea fog nowcasting method that uses brightness temperature (BT) at a mid-wave infrared (MWIR) 3.7 μm bands and the BT difference between MWIR and long-wave infrared 10.8 μm bands of communication, ocean, and meteorological satellite data through a data-to-data (D2D) translation based on a conditional generative adversarial networks technique. Sixty fog events that occurred in the Yellow Sea from 2017 to 2020 were used to develop the model. The D2D prediction model for predicting sea fog from 15-min to 2-h ahead with 15 min intervals was trained by stacking individual prediction time datasets in an array of 512 × 512 × 4 pixels for training and 512 × 512 × 8 pixels for testing. Otsu's thresholding method was used to determine sea fog pixels. The sea fog prediction model showed excellent statistical scores, such as the probability of detection = 0.923, false alarm ratio = 0.032, critical success index = 0.895, Heidke skill score = 0.909, and post agreement = 0.968 for 1 h-ahead of a sea fog forecast. Consequently, our study can make a scientific and operational contribution to fog forecasting as useful fog-related data, in addition to other numerical prediction data and ground observation data.

The Boundary Layer Characteristics and Development Mechanism of a Warm Advective Fog Event over the Yellow Sea

A large-scale persistent fog event occurred over the Yellow Sea of China from April 27 to May 4, 2015. In this study, we used satellite remote sensing data, ground meteorological observed data, global sounding data, and reanalysis data from the National Centers for Environmental Prediction (NCEP) and sea surface temperature (SST) data from the National Oceanic and Atmospheric Administration (NOAA) to analyze the evolutionary characteristics, the boundary-layer marine meteorological characteristics, and the development mechanism of the sea fog event. The results show that the sea fog event was a warm advective fog process. The Yellow Sea was at the rear of the warm high and the front of the continental low (circulation situation with high in the east and low in the west). The southerly and southeasterly winds transported warm and moist air from the Northwest Pacific northward to the Yellow Sea which served as the water vapor source. A thermal turbulence interface was formed during the sea fog development. The weak vertical wind shear below the interface was conducive to the maintenance and development of sea fog in the area of the temperature inversion in the boundary layer and the formation of a certain thickness of sea fog. The sea fog occurred in the area with water vapor convergence, where the 2-m dew point was slightly higher than the SST. The area with a relative humidity greater than 90% and an air–sea temperature difference from 0 to 2 °C overlapped with the sea fog area, and these two values indicate the extent of the sea fog.

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.

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.

Changes in aerosol particle composition during sea fog formation events in the sea ice regions of the Arctic Ocean

Water soluble ions (WSIs) of aerosol particles is one of the important parameters to study the aerosol-fog interactions. To study the changes of aerosol WSIs during sea fog events, hourly real-time measurements of WSIs (Na+, K+, Mg2+, Ca2+, MSA−, Cl−, NO3−, and SO42−) in total suspended aerosol particles (TSP) were conducted in the atmosphere over Arctic Ocean ice floe regions from August 1 to 12, 2017, when the sea fog events were frequently observed. There were significant differences in Na+, Cl−, Mg2+, methanesulfonic acid (MSA−), sea salt sulfate (ss-SO42) and non-sea salt sulfate (nss-SO42-) ions during sea fog events vs. non-sea fog periods. The mass concentrations of sea salt ions, such as Na+, Mg2+, Cl− and ss-SO42-, clearly increased before the occurrence of sea fog and then decreased substantially with the formation of sea fog; however, nss-SO42- levels did not decrease but remained high during the sea fog processes. These results suggest that sea salt aerosol particles were more likely to serve as condensation nuclei for fog and could be more effectively removed by sea fog than nss-SO42- particles. MSA− only combined with sea salt particles, which were likely to serve as condensation nuclei and be removed by sea fog. Condensation may be the primary factor leading to the reduction of sea salt ions during the sea fog periods, while other factors like the presence of dense sea ice and long-range transport input may also affect the decreasing rate.

Dual-Branch Neural Network for Sea Fog Detection in Geostationary Ocean Color Imager

Sea fog significantly threatens the safety of maritime activities. This paper develops a sea fog dataset (SFDD) and a dual branch sea fog detection network (DB-SFNet). We investigate all the observed sea fog events in the Yellow Sea and the Bohai Sea (118.1{\deg}E-128.1{\deg}E, 29.5{\deg}N-43.8{\deg}N) from 2010 to 2020, and collect the sea fog images for each event from the Geostationary Ocean Color Imager (GOCI) to comprise the dataset SFDD. The location of the sea fog in each image in SFDD is accurately marked. The proposed dataset is characterized by a long-time span, large number of samples, and accurate labeling, that can substantially improve the robustness of various sea fog detection models. Furthermore, this paper proposes a dual branch sea fog detection network to achieve accurate and holistic sea fog detection. The poporsed DB-SFNet is composed of a knowledge extraction module and a dual branch optional encoding decoding module. The two modules jointly extracts discriminative features from both visual and statistical domain. Experiments show promising sea fog detection results with an F1-score of 0.77 and a critical success index of 0.63. Compared with existing advanced deep learning networks, DB-SFNet is superior in detection performance and stability, particularly in the mixed cloud and fog areas.

Joint Monitoring and Analysis of Sea Fog Using Dual Visibility Lidar in Ningbo, China

Visibility lidar has obvious monitoring advantages over forward scatter visibility sensors or fog droplet spectrometers; it can measure visibility information over a large area. In 2021, two visibility lidar instruments (1064 or 532 nm wavelengths) were installed in Beilun, Ningbo Zhoushan Port, to monitor sea fog. Comparing their monitoring data to those of forward scatter visibility sensors and a fog droplet spectrometer revealed that the visibility lidar instruments could obtain energy progress information section-by-section in the monitoring path, and could directly reflect sea fog changes. The 1064 nm lidar outperformed the 532 nm lidar regarding sea fog detection. The effective detection range decreased significantly with decreasing visibility; the reliability decreased in low-visibility, uneven atmospheres. In a low-visibility but uniform atmosphere, however, lidar data corresponded well with forward dispersion data. The 532 nm and 1064 nm lidar data sometimes differed at the same monitoring position owing to differing heights and particle reflection angles. During a sea fog event on May 9, 2021, the maximum droplet concentration was 14 cm−3, the maximum liquid water content was 0.21 g·m−3, and the maximum equivalent diameter was 49 μm. The formation of this sea fog was dominated by large particles.

An observational and modeling study of a sea fog event over the yellow and east China seas on 17 March 2014

The Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery, weather charts, objectively reanalyzed data, the observational data and station sounding data were analyzed to investigate a sea fog event occurred over the Yellow and East China Seas on March 17, 2014. The sounding profiles, weather situations and the related meteorological factors during the development and dissipation stages of this sea fog event were documented. Weather Research Forecast (WRF) model was applied to simulate this sea fog case. The simulated horizontal atmospheric visibility, cloud water, humidity, and vertical wind profile during the different stages of this fog event were analyzed. During the development stage of this sea fog, a southerly lower-jet with 16–18 ms-1, an inversion layer and a cold center over the Yellow Sea were detected. The relative humidity in the fog area was above 95%. The specific humidity over the East China Sea was higher than that over the Yellow Sea. Southerly was dominated in fog area. However, during the dissipation stage of this sea fog, westerly replaced the southerly and at the lower level, southerly jet disappeared. A dry air area formed over the Shandong Peninsula and moved eastwards. Moreover, the WRF modeling result showed that the simulated atmospheric horizontal visibility and cloud water were approximately consistent with the MODIS satellite imagery. Most of cloud water concentrated below 200–400 m, and the cloud water in the southern part of fog area extended to a higher height than the northern part. While both of air temperature and dew-point temperature were close to sea surface temperature.

Monitoring and analysis of sea fog in an offshore waterway using lidar

Until now, sea fog monitoring relied mainly on point monitoring equipment, such as a forward scatter visibility sensor mounted on the shore or on an island, which produce non-ideal data during a variable and unevenly distributed sea fog. In 2019, a visibility lidar instrument for sea fog monitoring was installed in the Beilun area of the Ningbo Zhoushan Port, China. Two sea fog events were monitored in February 2020, and the data from the lidar were compared with those from a forward scatter visibility sensor. The results show that the visibility lidar instrument is advantageous for sea fog monitoring, and the correlation of the lidar instrument data with the forward scatter sensor data demonstrates the utility and potential of the lidar for sea fog detection.

Advection Fog over the Eastern Yellow Sea: WRF Simulation and Its Verification by Satellite and In Situ Observations

An observed sea fog event over the Eastern Yellow Sea on 15–16 April 2012 was reproduced in the Weather Research and Forecasting (WRF) simulation with high-resolution to investigate the roles of physical processes and synoptic-scale flows on advection fog with phase transition. First, it was verified by a satellite-based fog detection algorithm and in situ observation data. In the simulation, longwave (infrared) radiative cooling (LRC) with a downward turbulent sensible heat flux (SHF), due to the turbulence after sunset, triggered cloud formation over the surface when warm-moist air advection occurred. At night, warm air advection with continuous cooling due to longwave radiation and SHF near the surface modulated the change of the SHF from downward to upward, resulting in a drastic increase in the turbulent latent heat flux (LHF) that provided sufficient moisture at the lower atmosphere (self-moistening). This condition represents a transition from cold-sea fog to warm-sea fog. Enhanced turbulent mixing driven by a buoyancy force increased the depth of the sea fog and the marine atmospheric boundary layer (MABL) height, even at nighttime. In addition, cold air advection with a prevailing northerly wind at the top of the MABL led to a drastic increase in turbulent mixing and the MABL height and rapid growth of the height of sea fog. After sunrise, shortwave radiative warming in the fog layers offsetting the LRC near the surface weakened thermal instability, which contributed to the reduction in the MABL height, even during the daytime. In addition, dry advection of the northerly wind induced dissipation of the fog via evaporation. An additional sensitivity test of sea surface salinity showed weaker and shallower sea fog than the control due to the decrease in both the LHF and local self-moistening. Detailed findings from the simulated fog event can help to provide better guidance for fog detection using remote sensing.

A Coupled Numerical Modeling Study of a Sea Fog Case After the Passage of Typhoon Muifa Over the Yellow Sea in 2011

AbstractSatellite remote sensing data revealed a widespread sea fog event over the central/southern Yellow Sea (YS), following Typhoon Muifa passage in August 2011. Despite the importance of sea fog prediction for coastal safety, improving accuracy remains a challenging issue. By analyzing results from air‐sea coupled and uncoupled simulations, this study aims to investigate how the air‐sea coupling improves the sea fog formation and duration and examine atmospheric responses to spatiotemporally varying sea surface temperature (SST) over the YS. Unlike the uncoupled model, the SST simulated by the coupled model dramatically decreased and maintained its low temperature for more than a week after the typhoon passed over the YS, showing better agreement with the observations. The sharp SST decrease over the YS cools the air temperature at low‐level atmosphere and enhances horizontal convergence in the moisture flux over the cooler ocean, which provides favorable conditions for sea fog formation. The long‐lasting oceanic cooling stabilizes the atmospheric boundary layer and suppresses atmospheric vertical mixing, delaying the dissipation of sea fog for more than a week. This study highlights that air‐sea coupling can improve the sea fog simulation by providing more realistic oceanic conditions.

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 Microphysical Properties of a Sea-Fog Event along the West Coast of the Yellow Sea in Spring

The microphysics and visibility of a sea-fog event were measured at the Qingdao Meteorological Station (QDMS) (120°19′ E, 36°04′ N) from 5 April to 8 April 2017. The two foggy periods with low visibility (<200 m) lasted 31 h together. The mean value of the average liquid water content (LWC) was 0.057 g m−3, and the mean value of the number concentration (NUM) was 64.4 cm−3. We found that although large droplets only constituted a small portion of the total number of the concentration; they contributed the majority of the LWC and therefore determined ~76% of total extinction of the visibility. The observed droplet-size distribution (DSD) exhibited a new bimodal Gaussian (G-exponential) distribution function, rather than the well-accepted Gamma distribution. This work suggests a new distribution function to describe fog DSD, which may help to improve the microphysical parameterization for the Yellow Sea fog numerical forecasting.

Atmospheric Dry Deposition of Water-Soluble Nitrogen to the Subarctic Western North Pacific Ocean during Summer

To estimate dry deposition flux of atmospheric water-soluble nitrogen (N), including ammonium (NH4+), nitrate (NO3−), and water-soluble organic nitrogen (WSON), aerosol samples were collected over the subarctic western North Pacific Ocean in the summer of 2016 aboard the Korean icebreaker IBR/V Araon. During the cruise, concentrations of NH4+, NO3−, and WSON in bulk (fine + coarse) aerosols ranged from 0.768 to 25.3, 0.199 to 5.94, and 0.116 to 14.7 nmol m−3, respectively. Contributions of NH4+, NO3−, and WSON to total water-soluble N represented ~74%, ~17%, and ~9%, respectively. Water-soluble N concentrations showed a strong gradient from the East Asian continent to the subarctic western North Pacific Ocean, indicating that water-soluble N species were mainly derived from anthropogenic or terrestrial sources. During sea fog events, coarse mode NO3− was likely to be scavenged more efficiently by fog droplets than fine mode NO3−; besides, WSON was detected only in fine mode, suggesting that there may have been a significant influence of sea fog on WSON, such as the photochemical conversion of WSON into inorganic N. Mean dry deposition flux for water-soluble total N (6.3 ± 9.4 µmol m−2 d−1) over the subarctic western North Pacific Ocean was estimated to support a minimum carbon uptake of 42 ± 62 µmol C m−2d−1 by using the Redfield C/N ratio of 6.625.

A WRF Modeling Study on the Effects of Land Use Changes on Fog Off the West Coast of the Korean Peninsula

This study investigates, for the purpose of fog forecasting, the impacts of topography and land use changes on the characteristics of turbulence that directly contribute to the formation and dissipation of fog off the west coast of the Korean Peninsula using the Weather Research and Forecasting model version 3.5.1. During the investigation period, there are 59 coastal ground fog and 29 sea fog events. Local meteorological characteristics of coastal ground fog were similar to those of radiation fog typically seen over the land surface since the reclaimed island was constructed. After the sun rises, relative humidity over the land surface decreases rapidly—within a couple of hours—due to surface heating, which is controlled directly by shortwave radiation. Over the sea surface, however, the sea fog remains, with the relative humidity higher than 95% even during the daytime. For two selected cases, topography and land use were modified to identify turbulence characteristics through numerical modeling. This modification contributed to better forecasting the formation and dissipation of fog by changing characteristics of sensible and latent heat flux in the land surface model and then planetary boundary layer over the reclaimed island.

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.

Impact of Land‐Sea Thermal Contrast on the Inland Penetration of Sea Fog Over the Coastal Area Around the Korean Peninsula

AbstractBased on the difference between sea surface temperature (SST) and sea air temperature (SAT), sea fog can be classified as cold sea fog (SST < SAT) or warm sea fog (SST > SAT). First, two sea fog events over the Yellow Sea are selected and simulated using Weather Research and Forecasting model. The two sea fogs are designated as cold and warm types, and cooling and moistening rates of each case are calculated to understand the contributions of major factors to the sea fog formation. Turbulence plays a role in the cooling (warming) of the sea air, while advection is responsible for sea air warming (cooling) during the cold (warm) sea fog event. Thus, these two effects cancel each other out. However, the longwave radiative cooling effect is as stronger as or stronger than the other effects in both cases. We conducted sensitivity tests of SST and land skin temperature, which induced obvious changes in turbulent heat flux and land‐sea thermal contrast, respectively. In the increased (decreased) SST experiments, sea fog evaporates (expands) because of more (less) heat transfer from sea to air. The land skin temperature experiments show that increases (decreases) in land‐sea air temperature differences hinder (favor) sea fog penetration.