High frequency turbulent data from sonic anemometers and other ancillary information collected at multiple levels on several meteorological flux towers located at two coastal sites are used to study velocity and scalar (temperature and humidity) structure–function parameters over heterogeneous surfaces in an area of complex coastal terrains. Understanding of such parameters has important applications for studies on wave propagation in a turbulent atmosphere. One observational site was located on the Outer Banks near the town of Duck, North Carolina, with data from the CASPER-East Program (October–November 2015). The second site was located near the town of Ferryland, Newfoundland, Canada, with data from the C-FOG (Coastal-Fog) field campaign (September–October 2018). These measurements allowed studies of structure parameters and other statistics for different footprints, including relatively smooth sea surface conditions and aerodynamically rough dry inland areas for both stable and unstable stratifications. The coastal land cover discontinuity for onshore and offshore winds leads to a thin internal boundary layer (IBL) that could often be resolved by the instrumented towers. The drag coefficient, Bowen ratio, and diurnal variation of sensible heat flux were found to be coastal IBL markers. Our study shows that lower values of the dimensional structure parameters are associated with higher altitudes, smooth surfaces (i.e., over water, outside onshore IBL) and stable stratification (e.g., nocturnal boundary layer), ceteris paribus. In particular, observations showed that the structure parameters over land footprint areas (inside the onshore IBL) can be an order of magnitude larger than over the sea surface due to the change of aerodynamic and thermal properties of the surface. This study also discusses the applicability of Monin–Obukhov similarity theory (MOST) for the velocity and scalar structure parameters in the coastal environment for both stable and unstable conditions. While surface inhomogeneities and the complexity of the coastal landforms nominally violate assumptions underlying MOST, our observations show that the nondimensional structure parameters obey MOST reasonably well for all measurement levels, stability condition, and wind direction. In addition, we suggest a new variance-based hybrid scaling for the structure parameters that overcomes some of the shortcomings of the traditional approach (e.g., self-correlation and ambiguity of the dimensionless temperature structure parameter for near-neutral conditions).

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).

An interesting fog dissipation event was observed during the Fog and Turbulence Interactions in the Marine Atmosphere (FATIMA) Grand Banks field campaign, where a fog-free region appeared immediately downstream of Sable Island as fog advected past it. This fog-free region was predicted a priori by a high-resolution numerical model that guided intensive operational periods of the FATIMA campaign, and its presence was adumbrated by GOES satellite observations. A comprehensive set of field observations shows that this fog-free layer was due to the development of a (daytime) thermal internal boundary layer (IBL) that grew with distance from the leading shore line. The net incoming radiation following sunrise led to an increased air temperature and decreased relative humidity close to the ground, thus dissipating fog over the island. The height of the thermal IBL, as identified by the thickness of the superadiabatic layer, was found to be consistent with several available theoretical IBL formulae.

The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) was a yearlong expedition supported by the icebreaker R/V Polarstern, following the Transpolar Drift from October 2019 to October 2020. The campaign documented an annual cycle of physical, biological, and chemical processes impacting the atmosphere-ice-ocean system. Of central importance were measurements of the thermodynamic and dynamic evolution of the sea ice. A multi-agency international team led by the University of Colorado/CIRES and NOAA-PSL observed meteorology and surface-atmosphere energy exchanges, including radiation; turbulent momentum flux; turbulent latent and sensible heat flux; and snow conductive flux. There were four stations on the ice, a 10 m micrometeorological tower paired with a 23/30 m mast and radiation station and three autonomous Atmospheric Surface Flux Stations. Collectively, the four stations acquired ~928 days of data. This manuscript documents the acquisition and post-processing of those measurements and provides a guide for researchers to access and use the data products.

In this note, we celebrate the professional career of former Boundary-Layer Meteorology (BOUN) Editor-in-Chief, Evgeni Fedorovich (Fig. 1), highlighting his scientific achievements, career milestones, and impacts on those working on problems in boundary-layer meteorology and related disciplines.Evgeni Fedorovich served as the Editor-in-Chief of BOUN from 2014 to 2022.As a member of the editorial board and Editor-in-Chief of the journal, Evgeni knew almost everyone who works in this field, from students to professors.The opposite is also true-apparently everyone also knew Evgeni!Apart from his editorship of BOUN, Evgeni Fedorovich is best known for his research on entrainment in sheared and unsheared convective boundary layers, contaminant dispersal in urban settings, dynamics of nocturnal low-level jets, and turbulence and oscillations in slope flows (katabatic and anabatic flows).Evgeni is likely one of the few people in the field with a broad understanding of most of the topics of boundary-layer meteorology and atmospheric turbulence.Evgeni's research was characterized by creative use of wind tunnels, early adoption of the tools of large-eddy simulation and direct numerical simulation and, notably, its frequently collaborative nature with international participation by research scientists, engineers, faculty, and students.At the time of his retirement from the University of Oklahoma in 2021, Evgeni had

This paper reports analysis of eddy-covariance data collected during the WFIP2 field campaign in the complex-terrain of the US Pacific Northwest. A 31-day period representative of the region's dry season was used to address the following questions: (1) To what extent does the Constant-Flux Layer (CFL) assumption hold? (2) What is the spatial variability of turbulent and momentum fluxes over km scales? and (3) How skilful are the surface-layer parameterizations of mesoscale models? These questions are directly relevant to subgrid parameterization studies of mesoscale models. Results show that the efficacy of the CFL concept and the spatial variability of turbulent and momentum fluxes are dependent on: (i) the turbulent parameter being analysed, (ii) the measurement's location, (iii) the atmospheric stability regime (determined by the flow and vertical stratification), and (iv) the magnitude of the flux. Finally, the skill of the physics formulation of an often-used surface-layer parameterization scheme available in the Weather Research and Forecasting (WRF) model was also evaluated. Meteorological conditions associated with the highest and the lowest errors were identified. A metric to quantify (time-dependent) flow heterogeneity is proposed, which appears to be a good candidate to predict the skill of idealized surface-layer parameterization schemes in complex terrain.

The universal velocity log law first proposed by von K\'arm\'an in the near-wall region of turbulent shear flows is one of the cornerstones of turbulence theory. When buoyancy effects are important, the universal velocity log law is typically believed to break down according to Monin-Obukhov similarity theory (MOST), which has been used in almost all global weather and climate models to describe the dependence of the mean velocity profiles on buoyancy in the atmospheric boundary layer. In contrast to MOST, we propose new logarithmic profiles of near-wall mean velocity in the stably stratified atmospheric boundary layers based on direct numerical simulations and field observations across a wide range of buoyancy effects. We find that buoyancy does not change the logarithmic nature of velocity profiles but instead modifies the slope of the log law in stably stratified conditions.

Measurements made in the Columbia River basin (Oregon) in an area of irregular terrain during the second Wind Forecast Improvement Project (WFIP2) field campaign are used to develop an optimized hybrid bulk algorithm to predict the surface turbulent fluxes from readily measured or modeled quantities over dry and wet bare or lightly vegetated soil surfaces. The hybrid (synthetic) algorithm combines (i) an aerodynamic method for turbulent flow, which is based on the transfer coefficients (drag coefficient and Stanton number), roughness lengths, and Monin-Obukhov similarity; and (ii) a modified Priestley-Taylor (P-T) algorithm with physically based ecophysiological constraints, which is essentially based on the surface energy budget (SEB) equation. Soil heat flux in the latter case was estimated from measurements of soil temperature and soil moisture. In the framework of the hybrid algorithm, bulk estimates of the momentum flux and the sensible heat flux are derived from a traditional aerodynamic approach, whereas the latent heat flux (or moisture flux) is evaluated from a modified P-T model. Direct measurements of the surface fluxes (turbulent and radiative) and other ancillary atmospheric/soil parameters made during WFIP2 for different soil conditions (dry and wet) are used to optimize and tune the hybrid bulk algorithm. The bulk flux estimates are validated against the measured eddy-covariance fluxes. We also discuss the SEB closure over dry and wet surfaces at various time scales based on the modeled and measured fluxes. Although this bulk flux algorithm is optimized for the data collected during the WFIP2, a hybrid approach can be used for similar flux-tower sites and field campaigns.

Despite the importance of high-latitude surface energy budgets (SEBs) for land-climate interactions in the rapidly changing Arctic, uncertainties in their prediction persist. Here, we harmonize SEB observations across a network of vegetated and glaciated sites at circumpolar scale (1994–2021). Our variance-partitioning analysis identifies vegetation type as an important predictor for SEB-components during Arctic summer (June-August), compared to other SEB-drivers including climate, latitude and permafrost characteristics. Differences among vegetation types can be of similar magnitude as between vegetation and glacier surfaces and are especially high for summer sensible and latent heat fluxes. The timing of SEB-flux summer-regimes (when daily mean values exceed 0 Wm −2 ) relative to snow-free and -onset dates varies substantially depending on vegetation type, implying vegetation controls on snow-cover and SEB-flux seasonality. Our results indicate complex shifts in surface energy fluxes with land-cover transitions and a lengthening summer season, and highlight the potential for improving future Earth system models via a refined representation of Arctic vegetation types.

Results of weather forecast, present-day climate simulations and future climate projections depend among other factors on the interaction between the atmosphere and the underlying sea-ice, the land and the ocean. In numerical weather prediction and climate models some of these interactions are accounted for by transport coefficients describing turbulent exchange of momentum, heat and moisture. Currently used transfer coefficients have, however, large uncertainties in flow regimes being typical for cold nights and seasons, but especially in the polar regions. Furthermore, their determination is numerically complex. It is obvious that progress could be achieved when the transfer coefficients would be given by simple mathematical formulae in frames of an economic computational scheme. Such a new universal, so-called non-iterative parametrization scheme is derived for a package of transfer coefficients. The derivation is based on the Monin-Obukhov similarity theory, which is over the years well accepted in the scientific community. The newly derived non-iterative scheme provides a basis for a cheap systematic study of the impact of near-surface turbulence and of the related transports of momentum, heat and moisture in NWP and climate models. We show that often used transfer coefficients like those of Louis et al. (1982) or of Cheng and Brutsaert (2005) can be applied at large stability only with some caution, keeping in mind that at large stability they significantly overestimate the transfer coefficient compared with most comprehensive measurements. The latter are best reproduced by Gryanik et al. (2020) functions, which are part of the package. We show that the new scheme is flexible, thus, new stability functions can be added to the package, if required. Gryanik, V.M., Lüpkes, C., Grachev, A., Sidorenko, D. (2020) New Modified and Extended Stability Functions for the Stable Boundary Layer based on SHEBA and Parametrizations of Bulk Transfer Coefficients for Climate Models, J. Atmos. Sci., 77, 2687-2716

Abstract Weather prediction and climate simulations need reliable parameterizations of turbulent fluxes in the stable surface layer. Especially in these conditions, the uncertainties of such parametrizations are still large. Most of them rely on the Monin‐Obukhov similarity theory (MOST), for which universal stability functions (SFs) represent important ingredients. The SFs are nonlinear, if so, a numerical iteration of the MOST equations is required. Moreover, presently available SFs are significantly different at large stability. To simplify the calculations, a non‐iterative parametrization of fluxes is derived and corresponding bulk transfer coefficients for momentum and heat for a package of five pairs of state‐of‐the‐art SFs are proposed. For the first time, a parametrization of the related transfer coefficients is derived in a universal framework for all package members. The new parametrizations provide a basis for a cheap systematic study of the impact of surface layer turbulent fluxes in weather prediction and climate models.

C-FOG is a comprehensive bi-national project dealing with the formation, persistence, and dissipation (life cycle) of fog in coastal areas (coastal fog) controlled by land, marine, and atmospheric processes. Given its inherent complexity, coastal-fog literature has mainly focused on case studies, and there is a continuing need for research that integrates across processes (e.g., air-sea-land interactions, environmental flow, aerosol transport, and chemistry), dynamics (two-phase flow and turbulence), microphysics (nucleation, droplet characterization), and thermodynamics (heat transfer and phase changes) through field observations and modeling. Central to C-FOG was a field campaign in eastern Canada from 1 September to 8 October 2018, covering four land sites in Newfoundland and Nova Scotia and an adjacent coastal strip transected by the Research Vessel Hugh R. Sharp. An array of in situ, path-integrating, and remote sensing instruments gathered data across a swath of space-time scales relevant to fog life cycle. Satellite and reanalysis products, routine meteorological observations, numerical weather prediction model (WRF and COAMPS) outputs, large-eddy simulations, and phenomenological modeling underpin the interpretation of field observations in a multiscale and multiplatform framework that helps identify and remedy numerical model deficiencies. An overview of the C-FOG field campaign and some preliminary analysis/findings are presented in this paper.

Climate models still have deficits in reproducing the surface energy and momentum budgets in Arctic regions. One of the reasons is that currently used transfer coefficients occurring in parameterizations of the turbulent fluxes are based on stability functions derived from measurements over land and not over sea ice. An improved parameterization is developed using the Monin–Obukhov similarity theory (MOST) and corresponding stability functions that reproduce measurements over sea ice obtained during the Surface Heat Budget of the Arctic Ocean (SHEBA) campaign. The new stability functions for the stable surface layer represent a modification of earlier ones also based on SHEBA measurements. It is shown that the new functions are superior to the former ones with respect to the representation of the measured relationship between the MOST stability parameter and the bulk Richardson number. Nevertheless, the functions fulfill the same criteria of applicability as the earlier functions and contain, as an extension, a dependence on the neutral-limit turbulent Prandtl number. Applying the new functions we develop an efficient noniterative parameterization of the near-surface turbulent fluxes of momentum and heat with transfer coefficients as a function of the bulk Richardson number (Rib) and roughness parameters. A hierarchy of transfer coefficients is recommended for weather and climate models. They agree better with SHEBA data for strong stability (Rib > 0.1) than previous parameterizations and they agree well with those based on the Businger–Dyer functions in the range Rib ≤ 0.1.