In recent years, machine learning (ML) has gained popularity in the field of weather forecasting, particularly in areas where numerical weather prediction (NWP) models face challenges. One such area is fog prediction. Reduced visibility due to fog events poses serious challenges to transportation and public safety. Accurate representation of microphysics processes, radiation, boundary layer turbulence, and air-surface interaction is crucial in fog prediction. Traditional NWP models face limitations in accurately forecasting fog due to those complex processes. In this study, we propose a post-processing approach that combines the Weather Research and Forecasting (WRF) model forecasts with a machine learning classifier to improve fog prediction by distinguishing between fog and no-fog conditions. Using 12 years of data (2012-2023) for St John's, Newfoundland and Labrador, and Yarmouth, Nova Scotia, Canada, the approach was tested on independent 2024 observations. Compared to forecasts based solely on liquid water content from WRF, the ML model achieved higher skill, with F1-score improvements of 13% at St John's and 18% at Yarmouth. According to the results obtained, this approach demonstrates the potential of ML techniques to enhance operational fog forecasting capabilities. [Traduit par la r & eacute;daction] Au cours des derni & egrave;res ann & eacute;es, l'apprentissage machine (AM) a gagn & eacute; en popularit & eacute; dans le domaine des pr & eacute;visions m & eacute;t & eacute;orologiques, en particulier dans les r & eacute;gions o & ugrave; les mod & egrave;les de pr & eacute;vision num & eacute;rique du temps (PNT) rencontrent des difficult & eacute;s. La pr & eacute;vision du brouillard est l'un de ces domaines. La visibilit & eacute; r & eacute;duite due au brouillard pose de s & eacute;rieux probl & egrave;mes pour les transports et la s & eacute;curit & eacute; publique. Une repr & eacute;sentation pr & eacute;cise des processus microphysiques, du rayonnement, de la turbulence de la couche limite et de l'interaction air-surface est essentielle pour pr & eacute;voir le brouillard. Les mod & egrave;les traditionnels de PNT font face & agrave; des limites dans la pr & eacute;vision pr & eacute;cise du brouillard en raison de ces processus complexes. Dans cette & eacute;tude, nous proposons une approche de post-traitement qui combine les pr & eacute;visions du mod & egrave;le de recherche et de pr & eacute;vision m & eacute;t & eacute;orologique (mod & egrave;le WRF) avec un classificateur d'apprentissage automatique afin d'am & eacute;liorer la pr & eacute;vision du brouillard en distinguant les conditions de brouillard des conditions sans brouillard. En utilisant douze ann & eacute;es de donn & eacute;es (2012-2023) pour St John's (Terre-Neuve-et-Labrador) et Yarmouth (Nouvelle-& Eacute;cosse), au Canada, l'approche a & eacute;t & eacute; mise & agrave; l'essai avec des observations ind & eacute;pendantes de 2024. Comparativement aux pr & eacute;visions bas & eacute;es uniquement sur la teneur en eau liquide du mod & egrave;le WRF, le mod & egrave;le AM a obtenu de meilleurs r & eacute;sultats, avec des am & eacute;liorations du score F1 de 13% & agrave; St John's et de 18% & agrave; Yarmouth. D'apr & egrave;s les r & eacute;sultats obtenus, cette approche montre le potentiel des techniques AM pour am & eacute;liorer les capacit & eacute;s op & eacute;rationnelles de pr & eacute;vision du brouillard.

Marine fog plays an important role in ship and aircraft operations as well as in marine ecosystems and climate change. Fog is affected by boundary layer processes occurring at varying time and space scales. Its monitoring and forecasting can be challenging in marine environments because of the limited number of observation sites, such as buoys and ships. Therefore, the Fog and Turbulence in Marine Atmosphere (FATIMA) field campaign was designed to study the marine fog life cycle with high fog occurrence. FATIMA-YS was established over the Yellow Sea region of the Republic of Korea from 20 June to 9 July 2023. Observations were collected using the South Korean research vessel (R/V) Onnuri, the Atmospheric Research Aircraft (NARA), three Korean Ocean Research Stations supported by R/V Gisang-1, meteorological stations including buoys, and the Korean Meteorological Administration weather stations. In-situ instruments on the R/V and aircraft provided extensive observations of various microphysical and dynamical parameters, aerosols and gases, and radiation parameters. Satellite platforms (Himawari and Geostationary Ocean Color Image) together with ship-based millimeter cloud radar, lidar, and microwave radiometer have also been used for fog monitoring and simulation validation. During the campaign, eight Intensive Observational Periods (IOPs) were performed, and visibility changed from tens of meters up to tens of kilometers, and liquid water content reached up to 0.5 g & centerdot;m-3 during heavy fog conditions. Marine fog usually occurs due to advection processes, but fog formation was found to be highly related to the mixing of air masses, turbulence, and ocean cold-water upwelling, whereas its maturity and dissipation were strongly correlated with mixing processes. Preliminary results, extensive new technologies, and the project campaign description with IOP summaries are provided. Finally, the future work and challenges are discussed.

Fog constitutes a thick, opaque blanket of air hugging Earth's surface, laden with small water droplets or ice crystals. Fog disrupts transportation, poses security threats, disorients human perception, and impacts communications and ecosystems. Collusion of atmospheric, terrestrial, and hydrologic processes produces fog droplets that pullulate over hygroscopic aerosols that act as condensation nuclei. Marine fog is particularly complex, since underlying dynamic, thermodynamic, and (bio)physicochemical processes span fifteen decades of spatial scales, from megameter-sized synoptic weather systems to nanometer-scale bioaerosols. This paper overviews the first international field campaign [Fog and Turbulence Interactions in the Marine Atmosphere-Grand Banks campaign (Fatima-GB)] of the project dubbed Fatima conducted during 1-31 July 2022 in the Grand Banks region of the North Atlantic. Therein, weather systems and commingling cold and warm oceanic waters provide entr & eacute;e for fog genesis. Measurement platforms included an islet southwest of Nova Scotia (Sable Island), a research vessel (Atlantic Condor), an offshore oil platform, and autonomous surface vehicles. The instrument array comprised of extant remote and in situ sensors augmented by novel sensing systems prototyped and deployed in marine fog to penetrate the smallest scales of turbulence, examine aerosols, and quantify radiation budget. The comprehensive dataset so gathered, together with satellite and reanalysis products, mesoscale model, and large-eddy simulations, demonstrated that the long-held hypotheses of marine fog formation by warm air advection over colder water and in areas of enhanced (shelf) turbulence need to be revisited. The study also elicited new phenomena, for example, the fog shadow (clearings of fog downstream of islands).

This study investigates the phenomenon of fog clearing in the lee of Sable Island, Nova Scotia, termed a "fog shadow", using observations from the 2022 Fog And Turbulence Interactions in the Marine Atmosphere field experiment and Coupled Ocean-Atmosphere Mesoscale Prediction System numerical weather prediction model. The fog shadow occurs when a shallow layer of marine fog dissipates downstream of this low-elevation island due to surface heating and turbulent mixing. Analysis focuses on two cases: July 24, 2022, when a fog shadow was both predicted and observed through multiple platforms, including satellite imagery, and July 26, 2022, when the model erroneously forecast a fog shadow. Though the model successfully predicted some fog shadow events, it consistently overestimated surface heat fluxes over the island, leading to excessive fog dissipation in forecasts. The study reveals that fog shadows can form when sufficient surface heating combines with turbulent mixing to erode shallow fog layers, though the precise mechanisms and conditions required remain to be fully understood. These findings highlight the challenges in accurately modeling air-sea interactions and fog evolution around small islands, suggesting several areas for model improvement, including enhanced resolution, better surface flux parameterizations, and more sophisticated turbulence schemes.

A relatively simple 1D RANS model of the time evolution of the planetary boundary layer is extended to include water vapor and cloud droplets plus transfers between them. Radiative fluxes and flux divergence are also included. An underlying ocean surface is treated as a source of water vapor and as a sink for cloud or fog droplets. With a constant sea surface temperature and a steady wind, initially dry or relatively dry air will moisten, starting at the surface. Turbulent boundary layer mixing will then lead towards a layer with a well-mixed potential temperature (and so temperature decreasing with height) and well-mixed water vapor mixing ratio. As a result, the air will, sooner or later, become saturated at some level, and a stratus cloud will form.

Weather radar research has produced numerous radar-based rainfall estimators based on climate, rainfall intensity, a variety of ground-truthing instruments and sensors (e.g., rain gauges, disdrometers), and techniques. Although each research direction gives improvement, their collective application in an operational sense still yields uncertainty in rainfall estimation at times. This study aims to explore the concept of implementing Machine Learning (ML) models in optimizing the radar-based rainfall estimations at the bin level from a group of estimator. The Canadian King City C-Band radar was used with a GEONOR T-200B rain gauge (a total of 263 sample points) to establish a group of polarimetric-based rainfall estimators (R(Z), R(Z, Z(DR)), R(KDP)). The estimators were used to train three ML models, namely Decision Tree, Random Forest, and Gradient Boost, to choose the optimal rainfall estimators based on radar variables (Z, Z(DR), KDP). Data from the Canadian Exeter C-Band radar and a Texas Electronics TE525 tipping bucket gauge at a different location were used to verify the ML models and compare their results to the most commonly used Z-R relations. The verification process shows promising results for the ML models, specifically the Gradient Boost model. These encouraging results need to be further explored with more sample points to further refine the ML models.

A "Katata like" approach with module_bl_mynn and module_sf_fogdes: A quick fix to increase deposition of Qc to a water surface with module_bl_mynn could be to modify the WRF implementation of the Katata scheme in module_sf_fogdes to allow an extra land use category "water" with a more appropriate estimate of vdfg, the deposition velocity (m/s) of fog mixing ratio (Qc, kg/kg).There is however an additional complication in that module_bl_fogdes deals with gravitational settling of fog droplets through the air column as well as to the surface.However, as explained in a note posted at https://repository.library.noaa.gov/view/noaa/19837,that process is also dealt with in the microphysics module_mp_thompson.It should not be double counted.The Thompson microphysics code removes cloud droplets from the lowest level with its settling velocity sed_n(k) and relationships like nc(k) = MAX(10., nc(k) + (sed_n(k+1)-sed_n(k)) *odzq*DT).

Turbulent boundary layer concepts of constant flux layers and surface roughness lengths are extended to include aerosols and the effects of gravitational settling. Interactions between aerosols and the Earth's surface are represented via a roughness length for aerosol which will generally be different from the roughness lengths for momentum, heat or water vapour. Gravitational settling will impact vertical profiles and the surface deposition of aerosols, including fog droplets. Simple profile solutions are possible in neutral and stably stratified atmospheric surface boundary layers. These profiles can be used to predict deposition velocities and to illustrate the dependence of deposition velocity on reference height, friction velocity and gravitational settling velocity.

Liang, Sheng-Ru; Cai, Jing; Yang, Yang; Zhang, Lei; Taylor, Peter; Ming, Jie; Yu, Xin-Wen; Hu, Ruo-Fan; Zhou, Jie; Da-Yan, Colin M.; Ji, Qiu-HeEditor(s): Guo, Li-Shao Author Information

There have been many studies of marine fog, some using Weather Research and Forecasting (WRF) and other models. Several model studies report overpredictions of near-surface liquid water content (Qc), leading to visibility estimates that are too low. This study has found the same. One possible cause of this overestimation could be the treatment of a surface deposition rate of fog droplets at the underlying water surface. Most models, including the Advanced Research Weather Research and Forecasting (WRF-ARW) Model, available from the National Center for Atmospheric Research (NCAR), take account of gravitational settling of cloud droplets throughout the domain and at the surface. However, there should be an additional deposition as turbulence causes fog droplets to collide and coalesce with the water surface. A water surface, or any wet surface, can then be an effective sink for fog water droplets. This process can be parameterized as an additional deposition velocity with a model that could be based on a roughness length for water droplets, z0c, that may be significantly larger than the roughness length for water vapour, z0q. This can be implemented in WRF either as a variant of the Katata scheme for deposition to vegetation or via direct modifications in boundary-layer modules.

Tropospheric and lower‐stratospheric motions at mesoscales and larger are a mixture of waves and two‐dimensional (2‐D) turbulence. Determining their relative importance is necessary, since waves are capable of coordinated systematic momentum transport accompanying the wave propagation, and associated wind forcing, in ways that 2‐D turbulence is not. This can impact weather forecasting. Using a network of ten windprofiler radars in eastern Ontario and western Quebec in Canada, plus an additional one in the Arctic, the relative roles of internal gravity (buoyancy) waves and two‐dimensional turbulence are examined at temporal scales from about 3–4 hrs to several tens of hours (horizontal spatial scales of typically one or two hundred kilometres to a few thousand kilometres), with the purpose of investigating the respective roles of these two distinct characteristic fluid motions as functions of location, season and year. The emphasis is on studies of spectral slope variability, rather than absolute spectral magnitudes, giving a perspective not previously substantially presented. In particular, we have found a frequency band in which gravity‐wave Doppler shifting produces distinctly different spectral slopes than those predicted for 2‐D turbulence, and these differences are employed to distinguish the flow fields. The network used (excluding the Arctic site) covers an area of ∼106 km2 and includes a variety of different terrains. Radial velocities have been recorded on time scales of minutes for data lengths covering durations of up to 12 years. Altitude coverage is from 1 km to typically 14 km, at 500 m resolution. Results suggest a region from ∼2 to ∼5 km altitude (deeper for some radars) where waves are weaker and 2‐D turbulence appears to be generally more significant, but where occasional bursts of gravity‐wave activity can occur, while above typically 6–8 km, gravity waves increase in significance. There are distinct site‐to‐site variations.

Power generation is a leading cause of air pollution and major source of global warming emissions. Renewable energy resources, like wind and solar power, generate electricity with little to no global warming emissions and are reliable, affordable and beneficial for health, economy and climate. There are two sectors in wind energy: offshore and onshore. Offshore wind speeds are faster and steadier and coastal areas often exhibit a high energy demand. Offshore wind farms are coming to the Great Lakes. We therefore investigate the potential impact of wind farms on Lake Erie's dynamic and thermal structure using the COHERENS (a Coupled Hydrodynamical-Ecological model for Regional and Shelf Seas) and simulate a large wind farm with 432 offshore turbines located in the shallow southern waters of the central basin. The simulation is run twice to compare physical parameters such as temperature and circulation pattern and velocity results in the absence and presence of a large wind farm. In the case of no wind farm, model results are validated with data from buoys located in Lake Erie, while with the wind farm, the results show that the central basin is impacted by the wind turbines. This occurs because the reduced wind speed and stress leads to less mixing, lower current speeds and higher surface water temperature. There is no significant impact, however, in the eastern and western basins. This research examines Lake Erie, since this lake has a high potential for offshore wind turbine installation due to its proximity to population centers and its shallow depth.

We review developments in the field of boundary-layer flow over complex topography, focussing on the period from 1970 to the present day. The review follows two parallel strands: the impact of hills on flow in the atmospheric boundary layer and gravity-driven flows on hill slopes initiated by heating or cooling of the surface. For each strand we consider the understanding that has resulted from analytic theory before moving to more realistic numerical computation, initially using turbulence closure models and, more recently, eddy-resolving schemes. Next we review the field experiments and the physical models that have contributed to present understanding in both strands. For the period 1970–2000 with hindsight we can link major advances in theory and modelling to the key papers that announced them, but for the last two decades we have cast the net wider to ensure that we have not missed steps that eventually will be seen as critical. Two important new themes are given prominence in the 2000–2020 period. The first is flow over hills covered with tall plant canopies. The presence of a canopy changes the flow in important ways both when the flow is nearly neutral and also when it is stably stratified, forming a link between our two main strands. The second is the use of eddy-resolving models as vehicles to bring together hill flows and gravity-driven flows in a unified description of complex terrain meteorology.

Lake Erie has a significant wind energy resource potential, with extensive areas of moderate water depth and proximity to major cities and industries with substantial electricity demands. The presence of significant numbers of large turbines in wind farms will lead to reduced wind speeds and wind stresses in the wakes within and downwind of the farms. This in turn will affect surface fluxes, currents, and mixing in lake waters, generally allowing increased surface temperatures and reduced summer time mixed-layer depths. The potential magnitude of these impacts is investigated with a one dimensional application of the Coupled Hydrodynamical-Ecological Model for Regional and Shelf Seas model for three different water depths using observed meteorological data as input. Plain Language Summary Once a wind farm is installed there will be modifications to the wind field caused by the wakes of the turbines. This has been well studied for wind farms such as Horns Rev in the North Sea. For Lakes Erie and Ontario the areas with water depths suitable for wind farms are limited and we envisage multiple farms relatively close together. Wake effects will then cause significant reductions in the overall wind field of the area and have potentially significant impacts on mixing and air-water gas transfers. The research goal is to study airflow over selected Great Lakes with and without wind farms and to investigate the potential impact of wind farm development on circulation, mixing, and water quality issues.

During July 2016, an on-road study was conducted in and around the Toronto, Canada region to investigate the spatial variation of vehicle-induced turbulence on highways. The power spectral density of turbulent kinetic energy (TKE) while following on-road vehicles is significantly enhanced for frequencies greater than 0.5Hz. This increase is not present while driving isolated from traffic, demonstrating that TKE is enhanced considerably on highways in the presence of vehicles. The magnitude of normalized TKE is found to decay following a power-law relationship with increasing normalized distance behind on-road vehicles, which is most pronounced behind heavy-duty trucks. The results suggest that the TKE in the vehicle wake is maximized in the upper shear layer near the vehicle top. An extended parametrization is outlined that describes the total on-road TKE enhancement due to a composition of vehicles, which includes a vertical dependence on the magnitude of TKE.

Snow water‐equivalent (SWE) estimation is important for meteorologists and hydrologists, but solid snowfall estimation (snow depth) is essential for on‐duty meteorologists, the snow removal authorities and airports. Such an estimation can help meteorologists better quantify solid snowfall amounts and allow them to issue more accurate alerts to designated agencies, cities, municipalities, airports or the public. These agencies and the public are usually more interested in snow depth. Data from the dual‐polarimetric C‐band King City radar (CWKR) near Toronto in Ontario, Canada, and solid snowfall observations from nearby Oakville were used to establish radar‐based solid‐snowfall algorithms. A nonlinear regression analysis method was used to develop two power‐law algorithms to estimate solid snowfall rates (cm/hr): one used reflectivity and the other both reflectivity and differential reflectivity. These algorithms directly determine snowfall rates (which can be translated to snow accumulation on the ground or snow depth), in contrast to the conventional radar‐based technique of estimating the melted snowfall rate (mm/hr), before applying a constant snow–liquid ratio (SLR) to obtain the solid snowfall rate. Both new algorithms were similar at estimating solid snowfall rates and showed far superior results when compared with the one currently used by Environment Canada which uses a constant SLR of 10:1. Although there is no unique SLR for any geographical area, the solid and SWE ground measurements from Oakville suggests a higher SLR (14:1) as a better representative value than that currently assumed. Further validation of the algorithm requires frequent accurate solid snowfall data.

Although it is well known that jet streams play a vital role in everyday weather and long‐term climate variability, very few regional climatological studies on jet streams exist to date. Using the high‐resolution North American Regional Reanalysis (NARR), this study aims to create a preliminary jet stream, and more specifically a jet core, database for a relatively narrow region in eastern North America covering the period 1979 through 2016 inclusive. We use regional maxima in the smoothed horizontal wind field to locate both the latitudinal and vertical (pressure) location of jet cores along specific meridians. Results show that the median pressure level of all jet cores in the region of interest is 250 hPa and that two is the most likely number of distinct jet cores to occur along a given meridian at any given time. The jet cores are categorized into three bins based on their latitude in an attempt to capture the characteristics of the different tropopause‐level jet types. However, as the vertical and horizontal extents of the jet streams were not analysed, we cannot conclude for certain that each of the geographical bins corresponds to specific jet types. Statistically significant negative trends in the seasonal and overall mean pressure of the jet cores, implying an increased height, supports the findings of previous studies. Our analysis of NARR winds shows that jet core wind speeds increased in the region studied. This may be related to the increased height of jet cores through the thermal wind equation as well as increased baroclinicity across sections of the region studied. Jet core latitude and meridional circulation index (MCI) trends were generally negligible and not statistically significant. An absence of significant increases in |MCI| diverges from expectations and some theories of what to expect with Arctic amplification in a warming world.