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Analyses for the 12 September fog event. GOES East IR images for 1200 UTC 12 September (a) and 0000 UTC 13 September (b). At 1800 UTC 12 September is the NARR sea-level pressure (c) and 2-m RH (d). NARR Reanalysis provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, at https://www.esrl.noaa.gov/psd/. Satellite images from Environment Canada at https://weather.gc.ca/satellite/index_e.html
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The goal of this work is to summarize synoptic meteorological conditions during the Coastal Fog (C-FOG) field project that took place onshore and offshore of the Avalon Peninsula, Newfoundland, from 25 August until 8 October 2018. Visibility was measured at three locations at the Ferryland supersite that are about 1 km from each other, and at two a...
Citations
... The primary feature was a cyclonic system over the IOP region that generated low-level clouds and lowered the cloud base by convergence and subsidence. This is consistent with Dorman et al. (2021), who found that a cyclonic disturbance passing over the IOP region was required for fog development at Atlantic Canadian land stations in September, and the warm-air advection itself did not control the fog as usually seen in early summer (Gultepe et al. 2009). Backtrajectories confirm that all parcels were lifted along the streamlines and saturated when they reached the fog region, which is well known for cyclones (Bluestein 1992) and reported as a possible mechanism for fog development (reviewed in Gultepe et al. 2009;Koračin et al. 2014;Koračin and Dorman 2017). ...
Two stratus-lowering marine fog events observed on 28 September and 4 October 2018 during the Coastal Fog (C-Fog) field campaign that took place offshore of eastern Canada from 1 September to 6 October 2018 are described. In situ, profiling, and remote sensing observations were made at selected land sites in eastern Newfoundland, Nova Scotia, and aboard the research vessel Hugh R. Sharp cruising in adjoining coastal waters. Synoptic-scale analysis shows that both fog episodes result from the interaction between synoptic-scale surface-level low-pressure systems and a contiguous high-pressure system. At the same time, back trajectories reveal that the bulk of the fog layer is formed due to differential advection. The diameter of the fog droplets at the surface gradually decreases from the centre of the fog layer to its leading/trailing edges. The bimodal fog-droplet diameter distribution with peaks at 5–10 µm and 20–25 µm provide clues on droplet collision and coalescence processes. The observed difference between microphysical variables and droplet distribution between the two fog events and within the same fog layer might be governed by the atmospheric-boundary-layer (e.g., humidity conditions and turbulence) that prevailed in the fog layer. Overall, it is concluded that the life cycle of observed stratus-lowering coastal-fog episodes depends on synoptic conditions and atmospheric-boundary-layer characteristics such as stability, cloud-top cooling, and entrainment.
... The time series of Vis and PR on 29 September (Fig. 5d) was a continuation of the fog event that started on 29 September and was likely related to Tropical Storm Leslie, located far south of Atlantic Canada (see Dorman et al. 2021), and a deep low located in the north. From 0000 to 1400 UTC, Vis was less than 1 km, then increased to almost 6 km at 1900 UTC with some drizzle and fog observed. ...
The objective of this work is to evaluate GOES-R (Geostationary Operational Environmental Satellites-R series) data-based fog conditions which occurred during the C-FOG (Toward Improving Coastal Fog Prediction) field campaign. The C-FOG campaign was designed to advance understanding of fog formation, development, and dissipation over coastal environments to improve predictability. The project took place along coastlines and open water environments of eastern Canada (Nova Scotia, and the Island of Newfoundland) during August−October of 2018 where environmental conditions play an important role for late season fog formation. During the C-FOG field campaign, coastal instruments were mainly located at the Ferryland supersite, Newfoundland, with two main sites, and five satellite sites, as well as on the Research Vessel Hugh R. Sharp. Key in-situ measurement instruments included microphysical, meteorological, radiation, and aerosol sensors. A fog spectral probe was used for measuring droplet spectra from 1–50 µm at the Ferryland supersite. A laser precipitation monitor with 100 µm to 10 mm size range and an optical particle counter with 0.3–17 µm at 16 spectral channels provided information for fog and drizzle discrimination. Remote sensing platforms, e.g. profiling microwave radiometer, ceilometer, microwave rain radar, lidar, meteorological towers, tethered balloons, and GOES-R products for fog coverage, and droplet size and liquid water path) were used to evaluate fog over horizontal and vertical dimensions. Results suggest that effective radius, phase, liquid water path, and liquid water content values obtained from GOES-R and the profiling microwave radiometer are comparable to ground-based in-situ observations. It is concluded that integration of observations and nowcasting products may help improve short-term local fog predictions.
... Wang et al. (2021) also focuses on the impact of the fog layer on optical propagation using contrasting measurements at Ferryland and on the U.S. west coast. In addition, large-scale synoptic events affecting local fog formation are summarized by Dorman et al. (2021). An overview of the C-FOG project is given in Fernando et al. (2021). ...
Our goal is to provide an overview of the microphysical measurements made during the C-FOG (Toward Improving Coastal Fog Prediction) field project. In addition, we evaluate microphysical parametrizations using the C-FOG dataset. The C-FOG project is designed to advance understanding of liquid fog formation, particularly its development and dissipation in coastal environments, so as to improve fog predictability and monitoring. The project took place along eastern Canada’s (Nova Scotia and Newfoundland) coastlines and open water environments from August−October 2018, where environmental conditions play an important role for late-season fog formation. Visibility, wind speed, and atmospheric turbulence along coastlines are the most critical weather-related factors affecting marine transportation and aviation. In the analysis, microphysical observations are summarized first and then, together with three-dimensional wind components, used for fog intensity (visibility) evaluation. Results suggest that detailed microphysical observations collected at the supersites and aboard the Research Vessel Hugh R. Sharp are useful for developing microphysical parametrizations. The fog life cycle and turbulence-kinetic-energy dissipation rate are strongly related to each other. The magnitudes of three-dimensional wind fluctuations are higher during the formation and dissipation stages. An array of cutting-edge instruments used for data collection provides new insight into the variability and intensity of fog (visibility) and microphysics. It is concluded that further modifications in microphysical observations and parametrizations are needed to improve fog predictability of numerical-weather-prediction models.
... According to the International Civil Aviation Organization's present weather code (ICAO 2007), mist is defined as a visibility of between 1 and 5 km. Based on the data collected during the C-FOG (Coastal-Fog) field campaign, Dorman et al. (2021) found that "1-6 km visibility is a good marker of near-fog conditions due to water droplets", which can be considered as the range of mist visibility. Thus, Dorman et al. (2021) suggested a visibility threshold of 1 km for fog and an upper visibility threshold of 6 km for mist (i.e., mist is a visibility of between 1 and 6 km). ...
... Based on the data collected during the C-FOG (Coastal-Fog) field campaign, Dorman et al. (2021) found that "1-6 km visibility is a good marker of near-fog conditions due to water droplets", which can be considered as the range of mist visibility. Thus, Dorman et al. (2021) suggested a visibility threshold of 1 km for fog and an upper visibility threshold of 6 km for mist (i.e., mist is a visibility of between 1 and 6 km). We also adhere to this definition of fog and mist here. ...
... As mentioned, we use atmospheric turbulence data collected at a supersite located near a small seaside fishing town called Ferryland (on the Island of Newfoundland) during the C-FOG field campaign. General information about the C-FOG program and the field experiments can be found in the review article by Fernando et al. (2021) and in a series of specific studies by Bardoel et al. (2021), Dorman et al. (2021), Gultepe et al. (2021), Perelet et al. (2021) and others in this special issue. The town of Ferryland is located approximately 80 km south of St. John's on the south-eastern coast of the Avalon Peninsula exposed to the north-west Atlantic where large storms frequently affect coastal zones. ...
Measurements of atmospheric turbulence at a site in Ferryland (Newfoundland) during the C-FOG (Coastal-Fog) field campaign in September–October 2018 are used to study meteorological parameters, turbulence statistics, internal boundary layers, and scaling laws for turbulent mixing in the coastal zone. We observe stable/unstable shallow internal boundary layers with a region of unstable/stable stratification above with onshore flow from a relatively warm/cold sea onto the cold/heated land during the night/day. This study compares surface fluxes and other turbulence statistics as well as different scaling laws with and without fog. While both complexity of the coastal landforms and foggy conditions nominally violate assumptions underlying Monin–Obukhov similarity theory (MOST), our observations show that the non-dimensional standard deviations of the velocity components and the dissipation rate of turbulence kinetic energy obey MOST reasonably well for all measurement levels, stability conditions, and wind directions for both fog and no fog cases. However, the data scatter for the normalized dissipation rate is somewhat greater compared with the normalized standard deviations of the wind components. The bias and relatively larger scatter of normalized standard deviations for scalars in near-neutral conditions is likely associated with the underlying inhomogeneous coastal surface. According to the C-FOG data, during a fog event the moisture flux data become irregular and the latent heat flux is often negative (downward). Our observations also demonstrate poor agreement between normalized standard deviations of specific humidity with MOST for foggy conditions; its statistical dependence on the MOST stability parameter is weak at best in fog.
A micrometeorological fog experiment was carried out in Budapest, Hungary during the winter half year of 2020–2021. The field observation involved (i) standard meteorological and radiosonde measurements; (ii) surface radiation balance and energy budget components, and (iii) ceilometer measurements. 23 fog events occurred during the whole campaign. Foggy events were categorized based on two different methods suggested by Tardif and Rasmussen (2007) and Lin et al. (2022). Using the Present Weather Detector and Visibility sensor (PWD12), duration of foggy periods are approximately shorter (~ 9%) compared to ceilometer measurements. The categorization of fog based on two different methods suggests that duration of radiation fogs is lower compared to that of cloud base lowering (CBL) fogs. The results of analysis of observed data about the longest fog event suggest that (i) it was a radiation fog that developed from the surface upwards with condition of a near neutral temperature profile. Near the surface the turbulent kinetic energy and turbulent momentum fluxes remained smaller than 0.4 m² s–2 and 0.06 kg m–1 s–2, respectively. In the surface layer the vertical profile of the sensible heat flux was near constant (it changes with height ~ 10%), and during the evolution of the fog, its maximum value was smaller than 25 W m–2, (ii) the dissipation of the fog occurred due to increase of turbulence, (iii) longwave energy budget was close to zero during fog, and a significant increase of virtual potential temperature with height was observed before fog onset. The complete dataset gives an opportunity to quantify local effects, such as tracking the effect of strengthening of wind for modification of stability, surface layer profiles and visibility. Fog formation, development and dissipation are quantified based on the micrometeorological observations performed in suburb area of Budapest, providing a processing algorithm for investigating various fog events for synoptic analysis and for optimization of numerical model parameterizations.
The spatiotemporal variation of fog reflects the complex interactions among fog, boundary layer thermodynamics and synoptic systems. Previous studies revealed that fog can present a fast spatial propagation feature and attribute it to the boundary layer low-level jet (BLLJ), but the effect of the BLLJ on fog propagation is not quantitatively understood. Here we analyze a large-scale fog event in Jiangsu, China, from 20 to 21 January 2020. Satellite retrievals show that fog propagates from the southeast coastal area to the northwest inland area with a speed of 9.6 m s−1, which is 3 times larger than the ground wind speeds. The ground meteorologies are insufficient to explain the fast fog propagation, which is further investigated by Weather Research and Forecasting model (WRF) simulations. The fast fog propagation could be attributed to the BLLJ occurring between 50 and 500 m, because the wind speeds (10 m s−1) and directions (southeast) of the BLLJ core are consistent with fog propagation. Through sensitive experiments and process analysis, three possible mechanisms of the BLLJ are revealed: (1) the abundant oceanic moisture is transported inland, increasing the humidity of the boundary layer and promoting condensation; (2) the oceanic warm air is transported inland, enhancing the inversion layer and favoring moisture accumulation; and (3) the moisture advection probably promotes low-stratus formation, and later it subsides to become ground fog by turbulent mixing of fog droplets. The fog propagation speed would decrease notably by 6.4 m s−1 (66 %) in the model if the BLLJ-related moisture and warm advections were turned off.
In this study, a marine fog event that occurred from 0000 to 1800 UTC on 7 September 2018 near Canada’s Grand Banks is used to investigate the sensitivity of simulated fog properties to six model parameters found primarily in the microphysics scheme. To do so, we ran a large suite of regional simulations that spanned the life cycle of the fog event using the Regional Atmospheric Modeling System (RAMS). We randomly selected parameter combinations for the simulation suite and used Gaussian Process Regression to emulate the response of a variety of simulated fog properties to the parameters. We find that the microphysics shape parameter, which controls the relative width of the droplet size distribution, and the aerosol number concentration have the greatest impact on fog in terms of spatial extent, duration, and surface visibility. In general, parameters that reduce mean fall speed of droplets and/or suppress drizzle formation lead to reduced visibility in fog but also delayed onset, shorter lifetimes, and reduced spatial extent. The importance of the distribution width suggests a need for better characterization of this property for fog droplet distributions and better treatment of this property in microphysics schemes.
Sea fog often penetrates adjacent coastal areas, a process called sea fog penetration (SFP). SFP can cause traffic accidents and other economic losses. Qingdao, an international port city with a dense population, suffers from SFP originating over the Yellow Sea in the boreal spring (March–May); the process, however, is not well-studied. Based on hourly observations from buoys and automatic weather stations distributed in Qingdao and its adjacent islands, we composite SFP events to reveal their spatiotemporal features and to investigate the mechanisms involved. Results show that these SFP events often penetrate inland areas from southeast to northwest and last 5–8 h at night. We further use reanalysis data to reveal that during the daytime before SFP, strong moisture advection at 925–975 hPa brings sufficient water vapor from the Yellow Sea to Qingdao; the water vapor then transfers downward to the surface via background descending motion and turbulent mixing. The daytime anomalous moistening, together with the following diurnal cooling at night, saturates the surface atmosphere and, hence, facilitates SFP. The strength of SFP depends on the strength of daytime anomalous moistening. Considering that moistening leads SFP by about a day, we use this relationship to predict the intensity of SFP. The accuracy of predicting SFP events could reach 50–80%, which highlights the predictability of intensity of SFP in Qingdao.
