Nocturnal Boundary Layer Evolution and Its Impacts on the Vertical Distributions of Pollutant Particulate Matter

To investigate the evolution of the nocturnal boundary layer (NBL) and its impacts on the vertical distributions of pollutant particulates, a combination of in situ observations from a large tethered balloon, remote sensing instruments (aerosol lidar and Doppler wind lidar) and an atmospheric environment-monitoring vehicle were utilized. The observation site was approximately 100 km southwest of Beijing, the capital of China. Results show that a considerable proportion of pollutant particulates were still suspended in the residual layer (RL) (e.g., the nitrate concentration reached 30 μg m−3) after sunset. The NBL height calculated by the aerosol lidar was closer to the top of the RL before midnight because of the pollutants stored aloft in the RL and the shallow surface inversion layer; after midnight, the NBL height was more consistent with the top of the surface inversion layer. As the convective mixing layer gradually became established after sunrise the following day, the pollutants stored in the nocturnal RL of the preceding night were entrained downward into the mixing layer. The early morning PM2.5 concentration near 700 m in the RL on 20 December decreased by 83% compared with the concentration at 13:34 on 20 December at the same height. The nitrate concentration also decreased significantly in the RL, and the mixing down of nitrate from the RL could contribute about 37% to the nitrate in the mixing layer. Turbulence activities still existed in the RL with the bulk Richardson number (Rb) below the threshold value. The corresponding increase in PM2.5 was likely to be correlated with the weak turbulence in the RL in the early morning.

Reciprocal Interaction between Waves and Turbulence within the Nocturnal Boundary-Layer

<p>The presence of waves in the nocturnal boundary layer has proven to generate complex interaction with turbulence. On complex terrain environments, where turbulence is observed in a weak but continuous state of activity, waves can be a vehicle of additional production/loss of turbulence energy. The common approach based on the Reynolds decomposition is unable to disaggregate turbulence and wave motion, thus revealing impaired to explicit the terms of this additional interaction. In the current investigation, we adopt a triple-decomposition approach to separate mean, wave, and turbulence motions within near-surface boundary-layer flows, with the aim of unveiling the role of wave motion as source and/or sink of turbulence kinetic and potential energies in the respective explicit budgets. This investigation reveals that the waves contribute to the kinetic energy budget where the production is not shear-dominated and the budget equation does not reduce to a shear-dissipation balance (e.g., as it occurs close to a surface). Away from the surface, the buoyancy effects associated with the wave motion become a significant factor in generating a three-terms balance (shear-buoyancy-dissipation). Similar effects can be found in the potential energy budget, as the waves affect for instance the production associated with the vertical heat flux. On this basis, we develop a simple interpretation paradigm to distinguish two layers, namely near-ground and far-ground sublayer, estimating where the turbulence kinetic energy can significantly feed or be fed by the wave. To prove this paradigm and evaluate the explicit contributions of the wave motion on the turbulence kinetic and potential energies, we investigate a nocturnal valley flow observed under weak synoptic forcing in the Dugway Valley (Utah) during the MATERHORN Program. From this dataset, the explicit kinetic and potential energy budgets are calculated, relying on a variance-covariance analysis to further comprehend the balance of energy production/loss in each sublayer. With this investigation, we propose a simple interpretation scheme to capture and interpret the extent of the complex interaction between waves and turbulence in nocturnal stable boundary layers.</p>

Nocturnal Boundary Layer Erosion Analysis in the Amazon Using Large-Eddy Simulation during GoAmazon Project 2014/5

This study investigated the erosion of the nocturnal boundary layer (NBL) over the central Amazon using a high-resolution model of large-eddy simulation (LES) named PArallel Les Model (PALM) and observational data from Green Ocean Amazon (GoAmazon) project 2014/5. This data set was collected during four intense observation periods (IOPs) in the dry and rainy seasons in the years 2014 (considered a typical year) and 2015, during which an El Niño–Southern Oscillation (ENSO) event predominated and provoked an intense dry season. The outputs from the PALM simulations represented reasonably well the NBL erosion, and the results showed that it has different characteristics between the seasons. During the rainy season, the IOPs exhibited slow surface heating and less intense convection, which resulted in a longer erosion period, typically about 3 h after sunrise (that occurs at 06:00 local time). In contrast, dry IOPs showed more intensive surface warming with stronger convection, resulting in faster NBL erosion, about 2 h after sunrise. A conceptual model was derived to investigate the complete erosion during sunrise hours when there is a very shallow mixed layer formed close to the surface and a stable layer above. The kinematic heat flux for heating this layer during the erosion period showed that for the rainy season, the energy emitted from the surface and the entrainment was not enough to fully heat the NBL layer and erode it. Approximately 30% of additional energy was used in the system, which could come from the release of energy from biomass. The dry period of 2014 showed stronger heating, but it was also not enough, requiring approximately 6% of additional energy. However, for the 2015 dry period, which was under the influence of the ENSO event, it was shown that the released surface fluxes were sufficient to fully heat the layer. The erosion time of the NBL probably influenced the development of the convective boundary layer (CBL), wherein greater vertical development was observed in the dry season IOPs (~1500 m), while the rainy season IOPs had a shallower layer (~1200 m).

A novel lidar gradient cluster analysis method of nocturnal boundary layer detection during air pollution episodes

The observation of the nocturnal boundary layer height (NBLH) plays an important role in air pollution and monitoring. Through 39 d of heavy pollution observation experiments in Beijing (China), as well as an exhaustive evaluation of the gradient, wavelet covariance transform, and cubic root gradient methods, a novel algorithm based on the cluster analysis of the gradient method (CA-GM) of lidar signals is developed to capture the multilayer structure and achieve night-time stability. The CA-GM highlights its performance compared with radiosonde data, and the best correlation (0.85), weakest root-mean-square error (203 m), and an improved 25 % correlation coefficient are achieved via the GM. Compared with the 39 d experiments using other algorithms, reasonable parameter selection can help in distinguishing between layers with different properties, such as the cloud layer, elevated aerosol layers, and random noise. Consequently, the CA-GM can automatically address the uncertainty with multiple structures and obtain a stable NBLH with a high temporal resolution, which is expected to contribute to air pollution monitoring and climatology, as well as model verification.

Multiscale Shear Forcing of Turbulence in the Nocturnal Boundary Layer: A Statistical Analysis

The lower nocturnal boundary layer is governed by intermittent turbulence which is thought to be triggered by sporadic activity of so-called sub-mesoscale motions in a complex way. We analyze intermittent turbulence based on an assumed relation between the vertical gradients of the sub-mean scales and turbulence kinetic energy. We analyze high-resolution nocturnal eddy-correlation data from 30-m tower collected during the Fluxes over Snow Surfaces II field program. The non-turbulent velocity signal is decomposed using a discrete wavelet transform into three ranges of scales interpreted as the mean, jet and sub-mesoscales. The vertical gradients of the sub-mean scales are estimated using finite differences. The turbulence kinetic energy is modelled as a discrete-time autoregressive process with exogenous variables, where the latter ones are the vertical gradients of the sub-mean scales. The parameters of the discrete model evolve in time depending on the locally-dominant turbulence-production scales. The three regimes with averaged model parameters are estimated using a subspace-clustering algorithm which illustrates a weak bimodal distribution in the energy phase space of turbulence and sub-mesoscale motions for the very stable boundary layer. One mode indicates turbulence modulated by sub-mesoscale motions. Furthermore, intermittent turbulence appears if the sub-mesoscale intensity exceeds $$10 \%$$ 10 % of the mean kinetic energy in strong stratification.