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The study presents the first results from the multi-platform observational campaign carried out at the Mukhrino peatland in June 2022. The focus of the study is the quantification of spatial contrasts of the surface heat budget terms and methane emissions across the peatland, which arise due to the presence of microlandscape heterogeneities. It is found that surface temperature contrasts across the peatland exceeded 10 °C for clear-sky conditions both during day and night. Diurnal variation of surface temperature was strongest over ridges and drier hollows and was smallest over the waterlogged hollows and shallow lakes. This resulted in strong spatial variations of sensible heat flux ( H ) and Bowen ratio, while the latent heat varied much less. During the clear-sky days, H over ryam exceeded the one over the waterlogged hollow by more than a factor of two. The Bowen ratio amounted to about unity over ryam, which is similar to values over forests. Methane emissions estimated using the static-chamber method also strongly varied between various microlandscapes, being largest at a hollow within a ridge-hollow complex and smallest at a ridge. A strong nocturnal increase in methane mixing ratio was observed and was used in the framework of the atmospheric boundary layer budget method to estimate nocturnal methane emissions, which were found to be in the same order of magnitude as daytime emissions. Finally, the directions for further research are outlined, including the verification of flux-aggregation techniques, parameterizations of surface roughness and turbulent exchange, and land-surface model evaluation and development. [ABSTRACT FROM AUTHOR]

This study considers the problem of approximating the temporal dynamics of the urban-rural temperature difference (ΔT) in Moscow megacity using machine learning (ML) models and predictors characterizing large-scale weather conditions. We compare several ML models, including random forests, gradient boosting, support vectors, and multi-layer perceptrons. These models, trained on a 21-year (2001–2021) dataset, successfully capture the diurnal, synoptic-scale, and seasonal variations of the observed ΔT based on predictors derived from rural weather observations or ERA5 reanalysis. Evaluation scores are further improved when using both sources of predictors simultaneously and involving additional features characterizing their temporal dynamics (tendencies and moving averages). Boosting models and support vectors demonstrate the best quality, with RMSE of 0.7 K and R2 > 0.8 on average over 21 years. For three selected summer and winter months, the best ML models forced only by reanalysis outperform the comprehensive hydrodynamic mesoscale model COSMO, supplied by an urban canopy scheme with detailed city-descriptive parameters and forced by the same reanalysis. However, for a longer period (1977–2023), the ML models are not able to fully reproduce the observed trend of ΔT increase, confirming that this trend is largely (by 60–70%) driven by megacity growth. Feature importance assessment indicates the atmospheric boundary layer height as the most important control factor for the ΔT and highlights the relevance of temperature tendencies as additional predictors. [ABSTRACT FROM AUTHOR]

We present results from the multi-level mast eddy-covariance measurements conducted over flat grassland and over complex urban terrain composed of trees and small buildings. Relationships between the stability parameter and turbulent statistical properties were analysed. Over flat grassland, non-dimensional turbulent moments up to the third-order obey Monin–Obukhov similarity theory. Third-order moments increase for increasing instability, indicating nonlocal turbulent transport of heat flux due to convective motions, and in this case, the relationship between the third- and second-order moments obtained using the framework of the mass-flux approach might be applicable. In stable conditions, the turbulent transport of heat flux is positive and does not demonstrate an explicit dependence on stability but might be associated with large eddies and sweep motions transporting downwards the heat flux originating near the top of a stable boundary layer. Such a downward transport of negative heat flux by large eddies makes applicable the mass-flux approach even in stable boundary layers at higher levels. Over the urban landscape, for all wind directions the third moment corresponds well with the theoretical curve in cases of stable and unstable stratification, but there is a wide spread of data in near-neutral conditions. Skewness of the vertical velocity component is negative over the urban landscape and its value depends on wind direction. At lower levels, the standard deviation of the vertical velocity component is increasing with stability faster than expected from the theoretical curve. Over the urban landscape, large roughness elements do not affect the performance of the mass-flux approach in the convective boundary layer. Furthermore, there is a link between the second and third moments even in stable boundary layers over heterogeneous landscape. [ABSTRACT FROM AUTHOR]

We present experimental results of turbulent heat exchange between a small frozen lake surrounded by forest and the atmospheric boundary layer. Heat fluxes are measured at three levels using the eddy-covariance method and estimated by Monin–Obukhov similarity theory (MOST). In addition, we estimate the heat flux due to non-local turbulent transport of heat by coherent structures originating at the forest/lake transition, given the measured skewness of w ′ and w ′ w ′ T ′ ¯ , using a bimodal bottom-up–top-down model or the mass-flux models appropriate to the convective boundary layer. Two heat-flux-formation regimes in the surface layer are clearly distinguished. When the flow is from the vast forest, wind shear at the tree height leads to increased turbulent kinetic energy above the centre of the lake. Under conditions of simultaneous horizontal advection of warm air in the boundary layer above the canopy, a downward turbulent diffusion of negative heat flux leads to increased negative sensible heat flux in the surface layer, accompanied by an increasing third moment w ′ w ′ T ′ ¯ . Since MOST does not account for this mechanism, MOST-based fluxes poorly correspond to the eddy-covariance data in this case. At the same time the contribution of coherent structures increases. In contrast, when the flow is from the gap connecting the lake with the wide clearing, the effects of landscape inhomogeneity significantly reduce. In this case the turbulent transport of the heat flux from the upper part of the boundary layer vanishes, w ′ w ′ T ′ ¯ is negligible, and the heat flux is now primarily determined by the wind speed and temperature differences between the surface and near-surface atmosphere. This is a surface-flow regime for which MOST has been developed, and MOST-based fluxes correlate well with eddy-covariance data. [ABSTRACT FROM AUTHOR]

Large-eddy simulation (LES) and Lagrangian stochastic modeling of passive particle dispersion were applied to the scalar flux footprint determination in the stable atmospheric boundary layer. The sensitivity of the LES results to the spatial resolution and to the parameterizations of small-scale turbulence was investigated. It was shown that the resolved and partially resolved ("subfilter-scale") eddies are mainly responsible for particle dispersion in LES, implying that substantial improvement may be achieved by using recovering of small-scale velocity fluctuations. In LES with the explicit filtering, this recovering consists of the application of the known inverse filter operator. The footprint functions obtained in LES were compared with the functions calculated with the use of first-order single-particle Lagrangian stochastic models (LSMs) and zeroth-order Lagrangian stochastic models - the random displacement models (RDMs). According to the presented LES, the source area and footprints in the stable boundary layer can be substantially more extended than those predicted by the modern LSMs. [ABSTRACT FROM AUTHOR]

Experimental field campaigns are an essential part of atmospheric research, as well as of university education in the field of atmospheric physics and meteorology. Experimental field observations are needed to improve the understanding of the surface-atmosphere interaction and atmospheric boundary layer (ABL) physics and develop corresponding model parameterizations. Information on the ABL wind profiles is essential for the interpretation of other observations. However, wind profile measurements above the surface layer remain challenging and expensive, especially for the field campaigns performed in remote places and harsh conditions. In this study, we consider the experience of using two low-cost methods for the wind profiling, which may be easily applied in the field studies with modest demands on logistical opportunities, available infrastructure, and budget. The first one is a classical and well-known method of pilot balloon sounding, i.e., when balloon is treated as a Lagrangian particle and tracked by theodolite observations of angular coordinates. Second one is based on a vertical sounding with a popular and relatively cheap mass-market quadcopter DJI Phantom 4 Pro and utilizes its built-in opportunity to restore the wind vector from quadcopter tilt angles. Both methods demonstrated reasonable agreement and applicability even in harsh weather conditions and complex terrain. Advantages and shortcomings of these methods, as well as practical recommendations for their use are discussed. For the drone-based wind estimation, the importance of calibration by comparison to high-quality wind observations is shown. [ABSTRACT FROM AUTHOR]