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1. Small-Scale Wind Fluctuations within Melting Layers of Winter Storms: Results from WINTRE-MIX

   https://doi.org/10.1175/JAS-D-24-0087.1  

   Zaremba, Troy J; Minder, Justin R; Friedrich, Katja (March 2025, Journal of the Atmospheric Sciences)

   Abstract Investigations into the melting layer (ML) of winter storms have revealed small-scale fluctuations in the horizontal wind that could significantly affect the surface precipitation type (p-type) and the evolution of the ML. Despite previous evidence of such fluctuations, essential questions remain concerning their characteristics and the forces driving them. Therefore, this study characterizes small-scale horizontal wind fluctuations (<1 km in length with perturbation magnitudes < 3 m s⁻¹) and their environments within the ML of winter storms. This analysis uses data from a scanning X-band Doppler radar collected during the Winter Precipitation Type Research Multiscale Experiment (WINTRE-MIX), conducted during February and March 2022. We present three case studies where small-scale horizontal wind fluctuations are identified using along-radial and along-azimuthal radial velocity perturbations. These cases cover the range of environmental conditions observed during WINTRE-MIX, including (i) a descending ML with change in surface p-type from snow to rain, (ii) a steady ML with a surface p-type transition from freezing rain to rain due to surface cold air erosion, and (iii) a steady ML with a surface p-type transition from freezing rain to ice pellets due to surface cold air advection. Forcing mechanisms for small-scale wind fluctuations during each case are attributed to static instability, vertically trapped gravity waves, and/or shear instability inferred from rawinsonde data, HRRR analysis, and radar data. Our findings suggest that static instability, gravity waves, and shear instability drive the ML’s small-scale wind fluctuations and may influence surface precipitation-type transitions. Significance StatementThis research aims to enhance our understanding of horizontal motions (<1 km in length) within melting layers (MLs) of winter storms and their underlying causes. This study uses radar data to detect differences in horizontal motion within the ML of three different winter storms. Weather balloon observations and output from computer weather forecasts are then used to distinguish between horizontal motions generated by convection, vertically trapped gravity waves, or shear. Our findings reveal that horizontal motions within the ML are generated by different forcing mechanisms within different storms and that horizontal motions may influence the surface p-type. 

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2. Four-dimensional mesospheric and lower thermospheric wind fields using Gaussian process regression on multistatic specular meteor radar observations

   https://doi.org/10.5194/amt-14-7199-2021  

   Volz, Ryan; Chau, Jorge L.; Erickson, Philip J.; Vierinen, Juha P.; Urco, J. Miguel; Clahsen, Matthias (January 2021, Atmospheric Measurement Techniques)

   Abstract. Mesoscale dynamics in the mesosphere and lower thermosphere (MLT) region have been difficult to study from either ground- or satellite-based observations. For understanding of atmospheric coupling processes, important spatial scales at these altitudes range between tens and hundreds of kilometers in the horizontal plane. To date, this scale size is challenging observationally, so structures are usually parameterized in global circulation models. The advent of multistatic specular meteor radar networks allows exploration of MLT mesoscale dynamics on these scales using an increased number of detections and a diversity of viewing angles inherent to multistatic networks. In this work, we introduce a four-dimensional wind field inversion method that makes use of Gaussian process regression (GPR), which is a nonparametric and Bayesian approach. The method takes measured projected wind velocities and prior distributions of the wind velocity as a function of space and time, specified by the user or estimated from the data, and produces posterior distributions for the wind velocity. Computation of the predictive posterior distribution is performed on sampled points of interest and is not necessarily regularly sampled. The main benefits of the GPR method include this non-gridded sampling, the built-in statistical uncertainty estimates, and the ability to horizontally resolve winds on relatively small scales. The performance of the GPR implementation has been evaluated on Monte Carlo simulations with known distributions using the same spatial and temporal sampling as 1 d of real meteor measurements. Based on the simulation results we find that the GPR implementation is robust, providing wind fields that are statistically unbiased with statistical variances that depend on the geometry and are proportional to the prior velocity variances. A conservative and fast approach can be straightforwardly implemented by employing overestimated prior variances and distances, while a more robust but computationally intensive approach can be implemented by employing training and fitting of model hyperparameters. The latter GPR approach has been applied to a 24 h dataset and shown to compare well to previously used homogeneous and gradient methods. Small-scale features have reasonably low statistical uncertainties, implying geophysical wind field horizontal structures as low as 20–50 km. We suggest that this GPR approach forms a suitable method for MLT regional and weather studies. 

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3. Vertical Energy Fluxes Driven by the Interaction between Wave Groups and Langmuir Turbulence

   https://doi.org/10.1175/JPO-D-23-0193.1  

   Scully, Malcolm E; Zippel, Seth F (July 2024, Journal of Physical Oceanography)

   Abstract Data from an air–sea interaction tower are used to close the turbulent kinetic energy (TKE) budget in the wave-affected surface layer of the upper ocean. Under energetic wind forcing with active wave breaking, the dominant balance is between the dissipation rate of TKE and the downward convergence in vertical energy flux. The downward energy flux is driven by pressure work, and the TKE transport is upward, opposite to the downgradient assumption in most turbulence closure models. The sign and the relative magnitude of these energy fluxes are hypothesized to be driven by an interaction between the vertical velocity of Langmuir circulation (LC) and the kinetic energy and pressure of wave groups, which is the result of small-scale wave–current interaction. Consistent with previous modeling studies, the data suggest that the horizontal velocity anomaly associated with LC refracts wave energy away from downwelling regions and into upwelling regions, resulting in negative covariance between the vertical velocity of LC and the pressure anomaly associated with the wave groups. The asymmetry between downward pressure work and upward TKE flux is explained by the Bernoulli response of the sea surface, which results in groups of waves having a larger pressure anomaly than the corresponding kinetic energy anomaly, consistent with group-bound long-wave theory. 

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4. Wind, Waves, and Surface Currents: Interactions at Mesoscales and Submesoscales

   https://doi.org/10.1146/annurev-marine-040323-034908  

   Gille, Sarah T; Ardhuin, Fabrice; Bôas, AB Villas (January 2026, Annual Review of Marine Science)

   Air–sea exchanges of momentum and kinetic energy involve interactions between wind and ocean surface currents, mediated by the effects of surface waves. The wind stress that transfers momentum into the ocean depends on the velocity difference between winds and currents. Wind stress is also hypothesized to depend on the height and steepness of wind-generated waves. Waves are advected by ocean currents and also refracted as they pass through spatially varying currents. In turn, the impacts of waves and currents feed back on the wind, altering the structure of the atmospheric boundary layer. Together, wind–wave–current interactions shape ocean circulation, weather, and climate. However, gaps remain in our understanding, particularly in quantifying feedbacks between the components. Advances in our understanding will be facilitated by simultaneous measurements of key variables, via in situ observation or future satellite systems capable of obtaining global-scale observations. 

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5. Surface gravity wave effects on submesoscale currents in the open ocean

   https://doi.org/10.1175/JPO-D-20-0306.1  

   Hypolite, Delphine; Romero, Leonel; McWilliams, James C.; Dauhajre, Daniel P. (September 2021, Journal of Physical Oceanography)

   Abstract A set of realistic coastal simulations in California allows for the exploration of surface gravity wave effects on currents (WEC) in an active submesoscale current regime. We use a new method that takes into account the full surface gravity wave spectrum and produces larger Stokes drift than the monochromatic peak-wave approximation. We investigate two high wave events lasting several days — one from a remotely generated swell and another associated with local wind-generated waves — and perform a systematic comparison between solutions with and without WEC at two submesoscale-resolving horizontal grid resolutions ( dx = 270 m and 100 m). WEC results in the enhancement of open-ocean surface density and velocity gradients when the averaged significant wave height H S is relatively large (> 4.2m). For smaller waves, WEC is a minor effect overall. For the remote swell (strong waves and weak winds), WEC maintains submesoscale structures and accentuates the cyclonic vorticity and horizontal convergence skewness of submesoscale fronts and filaments. The vertical enstrophy ζ 2 budget in cyclonic regions ( ζ/f > 2) reveals enhanced vertical shear and enstrophy production via vortex tilting and stretching. Wind-forced waves also enhance surface gradients, up to the point where they generate a small-submesoscale roll-cell pattern with high vorticity and divergence that extends vertically through the entire mixed layer. The emergence of these roll-cells results in a buoyancy gradient sink near the surface that causes a modest reduction in the typically large submesoscale density gradients. 

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