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In spring, when the mountainous snow cover becomes patchy, the strong multi-scale surface heterogeneity influences atmospheric heat exchange processes. At valley scale, thermally driven winds advect warm air towards snow-covered regions at higher elevations. Additionally, the difference in albedo between bare ground and adjacent snow patches causes strong surface temperature differences on a (sub-)meter scale. Consequently, the structure of the near-surface boundary layer is spatio-temporally highly variable. During spring 2021, we recorded data in a comprehensive field campaign in the Dischma valley close to Davos (CH). The dataset includes multiple eddy-covariance measurements at different measurement heights. The topographic setting allows to group the measurements into up valley and down valley flow systems. Using a multi-resolution spectral decomposition of turbulent flux variables, we investigate the structure and dynamics of near-surface turbulence. During up valley flows, the advection of warm air induces stable internal boundary layers (SIBL) adjacent to snow patches with near-neutral static stability above. The strong stability withing the SIBL eventually leads to decoupling of the near-surface turbulence from the submeso-scale motions aloft. In contrast, during down valley flows, stability dampens turbulence similarly at all measurement heights. In concert with those findings, measurements utilizing the IR-screen setup during the same campaign yield high spatio-temporally resolved air temperature profiles during phases of different stability. Furthermore, the screen measurements visualize the near-surface boundary layer dynamics.In the next step, we will use the gained process understanding to test new physical parameterizations especially for warm air advection in hectometer scale coupled snow-atmosphere models.&nbsp, The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

We present the first direct comparison of turbulence conditions measured simultaneously over exposed ice and a 0.08 m thick supraglacial debris cover on Suldenferner, a small glacier in the Italian Alps. Surface roughness, sensible heat fluxes (~20–50 W m−2), latent heat fluxes (~2–10 W m−2), topology and scale of turbulence are similar over both glacier surface types during katabatic and synoptically disturbed conditions. Exceptions are sunny days when buoyant convection becomes significant over debris-covered ice (sensible heat flux ~ −100 W m−2; latent heat flux ~ −30 W m−2) and prevailing katabatic conditions are rapidly broken down even over this thin debris cover. The similarity in turbulent properties implies that both surface types can be treated the same in terms of boundary layer similarity theory. The differences in turbulence between the two surface types on this glacier are dominated by the radiative and thermal contrasts, thus during sunny days debris cover alters both the local surface turbulent energy fluxes and the glacier component of valley circulation. These variations under different flow conditions should be accounted for when distributing temperature fields for modeling applications over partially debris-covered glaciers.

The development of a unified similarity scaling has so far failed over complex surfaces, as scaling studies show large deviations from the empirical formulations developed over flat and horizontally homogeneous terrain as well as large deviations between the different complex terrain data sets. However, a recent study of turbulence anisotropy for flat and horizontally homogeneous terrain has shown that separating the data according to the limiting states of anisotropy (isotropic, two‐component axisymmetric and one‐component turbulence) improves near‐surface scaling. In this paper we explore whether this finding can be extended to turbulence over inclined and horizontally heterogeneous surfaces by examining near‐surface scaling for 12 different data sets obtained over terrain ranging from flat to mountainous. Although these data sets show large deviations in scaling when all anisotropy types are examined together, the separation according to the limiting states of anisotropy significantly improves the collapse of data onto common scaling relations, indicating the possibility of a unified framework for turbulence scaling. A measure of turbulence complexity is developed, and the causes for the breakdown of scaling and the physical mechanisms behind the turbulence complexity encountered over complex terrain are identified and shown to be related to the distance to the isotropic state, prevalence of directional shear with height in mountainous terrain, and the deviations from isotropy in the inertial subrange., Key Points A pathway toward a unified theory of near‐surface atmospheric turbulence is providedThe separation according to the limiting states of anisotropy significantly improves the collapse of data onto common scaling relationsPhysical processes responsible for turbulence complexity are identified

A comprehensive analysis of the turbulence structure of relatively deep midlatitude katabatic flows (with jet maxima between 20 and 50 m) developing over a gentle (1°) mesoscale slope with a long fetch upstream of the Meteor Crater in Arizona is presented. The turbulence structure of flow below the katabatic jet maximum shows many similarities with the turbulence structure of shallower katabatic flows, with decreasing turbulence fluxes with height and almost constant turbulent Prandtl number. Still stark differences occur above the jet maximum where turbulence is suppressed by strong stability, is anisotropic and there is a large sub-mesoscale contribution to the flux. Detecting the stable boundary-layer top depends on the method used (flux- vs. anisotropy-profiles) but both methods are highly correlated. The top of the stable boundary layer, however, mostly deviates from the jet maximum height or the top of the near-surface inversion. The flat-terrain formulations for the boundary-layer height correlate well with the detected top of the stable boundary layer if the near-surface and not the background stratification is used in their formulations; however, they mostly largely overestimate this boundary-layer height. The difference from flat-terrain boundary layers is also shown through the dependence of size of the dominant eddy with height. In katabatic flows the eddy size is semi-constant with height throughout the stable boundary-layer depth, whereas in flat terrain, eddy size varies significantly with height. Flux-gradient and flux-variance relationships show that turbulence data from different stable boundary-layer scaling regimes collapse on top of each other showing that the dominant dependence is not on the scaling regime but on the local stability.

Pattern correlation techniques are commonly used in Particle Image Velocimetry studies to calculate tracer particle velocity in fluids or gases. The techniques can be further improved and used to track landscape scale moving thermal patterns in time sequenced infrared images to estimate a spatial advection velocity field.This study presents a validation and parametric sensitivity test of commonly used image correlation techniques using data produced from a numerical weather model and field observations from a longwave infrared camera. The numerical simulations aimed to create three different wind speed pattern cases to analyze the performance of four different image velocimetry techniques and compare the estimated velocity field to the model's velocity field. A sensitivity test was carried out with various combinations of required user input to find the best performing image velocimetry techniques. These were then applied to a dataset of surface temperature of an artificial hockey turf field taken with an infrared (IR) camera to test the algorithms in real world conditions and to directly compare the estimated velocity to in-situ measured wind velocity.The velocimetry algorithms are especially accurate when there is low complexity in ow structure (root mean square error: 0.4: Structural Similarity: 0.81). The comparison of the IR image velocimetry with the in-situ measurements on the hockey turf field shows accurate representation of the 10 minute mean wind direction and the mean wind speed with a maximal absolute error of 0.8 for the wind direction. If the comparison is done on a one minute average basis the accuracy of the image velocimetry decreases when the wind speed drops.

Pattern correlation techniques are commonly used in Particle Image Velocimetry studies to calculate tracer particle velocity in fluids or gases. The techniques can be further improved and used to track landscape scale moving thermal patterns in time sequenced infrared images to estimate a spatial advection velocity field.This study presents a validation and parametric sensitivity test of commonly used image correlation techniques using data produced from a numerical weather model and field observations from a longwave infrared camera. The numerical simulations aimed to create three different wind speed pattern cases to analyze the performance of four different image velocimetry techniques and compare the estimated velocity field to the model's velocity field. A sensitivity test was carried out with various combinations of required user input to find the best performing image velocimetry techniques. These were then applied to a dataset of surface temperature of an artificial hockey turf field taken with an infrared (IR) camera to test the algorithms in real world conditions and to directly compare the estimated velocity to in-situ measured wind velocity.The velocimetry algorithms are especially accurate when there is low complexity in ow structure (root mean square error: 0.4: Structural Similarity: 0.81). The comparison of the IR image velocimetry with the in-situ measurements on the hockey turf field shows accurate representation of the 10 minute mean wind direction and the mean wind speed with a maximal absolute error of 0.8 for the wind direction. If the comparison is done on a one minute average basis the accuracy of the image velocimetry decreases when the wind speed drops.