Boundary-Layer Meteorology最新文献

We examine measurements in the very stable boundary layer using tower data and a network of flux stations in the Shallow Cold Pool experiment. Submeso motions in the very stable boundary layer significantly modulate the turbulent heat fluxes and also directly contribute to the submeso vertical heat flux. Time series include well-defined submeso structures such as microfronts, wave-like motions, and meandering but also include complex structures that are difficult to isolate. These structures significantly influence the time and height variation of the turbulent heat flux. From the 19 flux stations distributed across the shallow valley, we find that the surface heat flux with low wind speeds varies significantly on a horizontal scale of 100 m, or less, related partly to the modest topography. For this dataset, the turbulent surface heat fluxes for low wind speeds are closely related to submeso variations of the wind speed but not significantly related to variations of the stratification.

Abstract

Turbulent exchanges between sea ice and the atmosphere are known to influence the melting rate of sea ice, the development of atmospheric circulation anomalies and, potentially, teleconnections between polar and non-polar regions. Large model errors remain in the parametrization of turbulent heat fluxes over sea ice in climate models, resulting in significant uncertainties in projections of future climate. Fluxes are typically calculated using bulk formulae, based on Monin-Obukhov similarity theory, which have shown particular limitations in polar regions. Parametrizations developed specifically for polar conditions (e.g. representing form drag from ridges or melt ponds on sea ice) rely on sparse observations and thus may not be universally applicable. In this study, new data-driven parametrizations have been developed for surface turbulent fluxes of momentum, sensible heat and latent heat in the Arctic. Machine learning has already been used outside the polar regions to provide accurate and computationally inexpensive estimates of surface turbulent fluxes. To investigate the feasibility of this approach in the Arctic, we have fitted neural-network models to a reference dataset (SHEBA). Predictive performance has been tested using data from other observational campaigns. For momentum and sensible heat, performance of the neural networks is found to be comparable to, and in some cases substantially better than, that of a state-of-the-art bulk formulation. These results offer an efficient alternative to the traditional bulk approach in cases where the latter fails, and can serve to inform further physically based developments.

Abstract

Large-eddy simulations (LES) above forests and cities typically constrain the simulation domain to the first 10–20% of the Atmospheric Boundary Layer (ABL), aiming to represent the finer details of the roughness elements and sublayer. These simulations are also commonly driven by a constant pressure gradient term in the streamwise direction and zero stress at the top, resulting in an unrealistic fast decay of the total stress profile. In this study, we investigate five LES setups, including pressure and/or top-shear driven flows with and without the Coriolis force, with the aim of identifying which option best represents turbulence profiles in the atmospheric surface layer (ASL). We show that flows driven solely by pressure not only result in a fast-decaying stress profile, but also in lower velocity variances and higher velocity skewnesses. Top-shear driven flows, on the other hand, better replicate ASL statistics. Overall, we recommend, and provide setup guidance for, simulation designs that include both a large scale pressure forcing and a non-zero stress and scalar flux at the top of the domain, and that also represent the Coriolis force. Such setups retain all the forces used in typical full ABL cases and result in the best match of the profiles of various statistical moments.

Surface turbulent exchanges play a key role on sea ice dynamics, on ocean and sea ice heat budgets and on the polar atmosphere. Uncertainties in parameterizations of surface turbulent fluxes are mostly held by the transfer coefficients and estimates of those transfer coefficients from field data are required for parameterization development. Measurement errors propagate through the computation of transfer coefficients and contribute to its total error together with the uncertainties in the empirical stability functions used to correct for stability effects. Here we propose a methodology to assess their contributions individually to each coefficient estimate as well as the total drag coefficient uncertainty and we apply this methodology on the example of the SHEBA campaign. We conclude that for most common drag coefficient values (between (1.0times 10^{-3}) and (2.5times 10^{-3})), the relative total uncertainty ranges from 25 and 50(%). For stable or unstable conditions with a stability parameter (|zeta |>1) on average, the total uncertainty in the neutral drag coefficient exceeds the neutral drag coefficient value itself, while for (|zeta |<1) the total uncertainty is around 25(%) of the drag coefficient. For closer-to-neutral conditions, this uncertainty is dominated by measurement uncertainties in surface turbulent momentum fluxes which should therefore be the target of efforts in uncertainty reduction. We also propose an objective data-screening procedure for field data, which consists of retaining data for which the relative error on neutral drag coefficient does not exceed a given threshold. This method, in addition to the commonly used flux quality control procedure, allows for a reduction of the drag coefficient dispersion compared to other data-screening methods, which we take as an indication of better dataset quality.

The Monin–Obukhov Similarity Theory (MOST) is a cornerstone of boundary layer meteorology and the basis of most parameterizations of the atmospheric surface layer. Due to its significance for observations and modelling, we generalize the dimensional analysis of MOST by considering the bulk gradient directly, enabling the study of any sublayer of the atmospheric surface layer. This results in a family of similarity relations describing all gradients from the local gradient to the full-layer bulk gradient. By applying the profiles derived from the law-of-the-wall and MOST, we are able to derive analytic expressions for this family of similarity relations. Under stable conditions, we discover that the log-linear profile of Businger–Dyer generalizes from the local to the bulk shear where the slope is dependent on the choice of the layer. The simplicity of the general log-linear relation allows for estimating the influence of stability on the non-dimensional gradients. It is shown that bulk gradients are less sensitive to stability than the local gradient. By correctly filtering cases where the full-layer bulk gradient is influenced by stability, we demonstrate that MOST is compatible with the Hockey-Stick Transition. For unstable conditions, the Kader and Yaglom (J Fluid Mech 212(151):637-662, 1990) model represents the local gradient well but was not successful in representing the bulk gradient, demonstrating the need for further analysis of scaling relations for the unstable atmospheric surface layer.

Abstract

This study aims at estimating the inherent variability of microscale boundary-layer flows and its impact on air pollutant dispersion in urban environments. For this purpose, we present a methodology combining high-fidelity large-eddy simulation (LES) and a stationary bootstrap algorithm, to estimate the internal variability of time-averaged quantities over a given analysis period thanks to sub-average samples. A detailed validation of an LES microscale air pollutant dispersion model in the framework of the Mock Urban Setting Test (MUST) field-scale experiment is performed. We show that the LES results are in overall good agreement with the experimental measurements of wind velocity and tracer concentration, especially in terms of fluctuations and peaks of concentrations. We also show that both LES estimates and the MUST experimental measurements are subject to significant internal variability, which is therefore essential to take into account in the model validation. Moreover, we demonstrate that the LES model can accurately reproduce the observed internal variability.

Numerical simulations and in-situ measurements represent two important and synergistic pillars for the study of flow and transport in plant canopies. Due to model limitations and parameter uncertainty, the alignment of model predictions with actual observations is challenging in practice. The present work proposes a Bayesian uncertainty quantification (UQ) framework that estimates the approaching wind angle parameter for large-eddy simulation (LES) of flow in plant canopies by assimilating data from in-situ measurements. The framework is applied to LES of flow within and above realistic plant canopy, with plant area density derived from light detection and ranging measurements. Uncertainty on approaching wind direction is characterized via a Markov chain Monte Carlo procedure, and propagated through Monte Carlo sampling to wind speed and resolved Reynolds stresses. Given the substantial computational cost of LES, a surrogate model based on an exiguous number of LESs is used for flow simulations within the UQ framework. As a result of the analysis, the UQ solution is given by probability density functions of selected flow statistics at different heights. Profiles of mean ± standard deviation for the considered flow statistics exhibit excellent agreement with corresponding observations, proving that the proposed approach is able to calibrate the approaching wind angle parameter, and that the quantified uncertainty captures discrepancies between observations and model results. Overall, the present work highlights the potential of UQ to enhance predictions of exchange processes between vegetation canopy and atmosphere.

The presence of vegetation within urban canyons leads to non-trivial patterns of the concentration of airborne pollutants, as a result of the complex structure of the velocity field. To investigate the relationship between concentration, velocity fields and vegetation density, we have performed wind-tunnel experiments in a reduced-scale street canyon, oriented perpendicular to the external wind flow, within which we placed a steady ground-level line source of a passive tracer. The aerodynamic behavior of vegetation was reproduced by inserting plastic miniatures of trees along the two long sides of the canyon, according to three different densities. The canyon ventilation was investigated by acquiring one-point simultaneous statistics of concentration and velocity over a dense grid of points within the canyon. The results show that the presence of trees hinders the upward mean vertical velocity at the rooftop, causes a reduction of the turbulent kinetic energy inside the canyon, and reduces the energy content of the large scales. The scalar concentration is conversely characterized by an enhanced level of turbulent fluctuations, whose magnitude is not dampened increasing the tree density. Within the canyon, high tree density inhibits turbulent mass fluxes, which are instead enhanced at roof level, where the mean component of the scalar flux is however hindered. A statistical analysis of concentration time series reveals that the lognormal distribution is suitable to model concentration fluctuations and extreme events, in dispersing plumes emitted by a linear source.

The near-surface boundary layer above patchy snow cover in mountainous terrain is characterized by a highly complex interplay of various flows on multiple scales. In this study, we present data from a comprehensive field campaign that cover a period of 21 days of the ablation season in an alpine valley, from continuous snow cover until complete melt out. We recorded near-surface eddy covariance data at different heights and investigated spectral decompositions. The topographic setting led to the categorisation of flows into up and down valley flows, with a down valley Föhn event in the middle of the observation period. Our findings reveal that the snow cover fraction is a major driver for the structure and dynamics of the atmospheric layer adjacent to the snow surface. With bare ground emerging, stable internal boundary layers (SIBL) developed over the snow. As the snow coverage decreased, the depth of the SIBL decreased below 1 m and spectra of air temperature variance showed a transition towards turbulent time scales, which were caused by the intermittent advection of shallow plumes of warm air over the snow surface. The intermittent advection could also be observed visually with high spatio-temporal resolution measurements using a thermal infrared camera. While the shallow advection only affected the lowest measurement level at 0.3 m, the measurements above at 1 m, 2 m, and 3 m indicate that the distribution of eddy size and, thus, the turbulence structure, did not distinctly change with height.

Abstract

The interaction of large- and small-scale velocity fluctuations between a street canyon flow and the overlying boundary layer, under the influence of a local morphological model and a single upstream tall building, is investigated. The experiments are conducted in a wind tunnel, using Stereoscopic Particle Image Velocimetry (S-PIV) and Hot-Wire Anemometry (HWA). The Proper Orthogonal Decomposition-Linear Stochastic Estimation (POD-LSE) method is applied to decompose the velocity fluctuation scales and estimate the large-scale fluctuations at a high frequency. The amplitude modulation mechanism, which was found to exist for both smooth and homogeneous rough wall boundary layers in previous studies, still applies to the more complex morphological model with a single upstream building having a relative low height, but with some modification. When the upstream building is much higher than the surrounding buildings, the large eddies shed from the tall building may predominate the scale interaction.