scholarly journals Impact of modified turbulent diffusion of PM<sub>2.5</sub> aerosol in WRF-Chem simulations in eastern China

2021 ◽  
Vol 21 (22) ◽  
pp. 16827-16841
Author(s):  
Wenxing Jia ◽  
Xiaoye Zhang
Keyword(s):  
Boundary Layer ◽  
Diffusion Process ◽  
Turbulent Mixing ◽  
Turbulent Diffusion ◽  
Eastern China ◽  
Pollutant Concentration ◽  
Mixing Process ◽  
Mountainous Areas ◽  
Turbulent Diffusion Coefficient ◽  
Pm2.5 Concentration

Abstract. Correct description of the boundary layer mixing process of particle is an important prerequisite for understanding the formation mechanism of pollutants, especially during heavy pollution episodes. Turbulent vertical mixing determines the distribution of momentum, heat, water vapor and pollutants within the planetary boundary layer (PBL). However, what is questionable is that the turbulent mixing process of particles is usually denoted by turbulent diffusion of heat in the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). With mixing-length theory, the turbulent diffusion relationship of particle is established, embedded into the WRF-Chem and verified based on long-term simulations from 2013 to 2017. The new turbulent diffusion coefficient is used to represent the turbulent mixing process of pollutants separately, without deteriorating the simulation results of meteorological parameters. The new turbulent diffusion improves the simulation of pollutant concentration to varying degrees, and the simulated results of PM2.5 concentration are improved by 8.3 % (2013), 17 % (2014), 11 % (2015) and 11.7 % (2017) in eastern China, respectively. Furthermore, the pollutant concentration is expected to increase due to the reduction of turbulent diffusion in mountainous areas, but the pollutant concentration did not change as expected. Therefore, under the influence of complex topography, the turbulent diffusion process is insensitive to the simulation of the pollutant concentration. For mountainous areas, the evolution of pollutants is more susceptible to advection transport because of the simulation of obvious wind speed gradient and pollutant concentration gradient. In addition to the PM2.5 concentration, the concentration of CO as a primary pollutant has also been improved, which shows that the turbulent diffusion process is extremely critical for variation of the various aerosol pollutants. Additional joint research on other processes (e.g., dry deposition, chemical and emission processes) may be necessary to promote the development of the model in the future.

scholarly journals Impact of modified turbulent diffusion of PM<sub>2.5</sub> aerosol in WRF-Chem simulations in Eastern China

2021 ◽  
Author(s):  
Wenxing Jia ◽  
Xiaoye Zhang
Keyword(s):  
Diffusion Process ◽  
Turbulent Diffusion ◽  
Turbulent Transport ◽  
Eastern China ◽  
Mixing Process ◽  
Heavy Pollution ◽  
Mixing Length Theory ◽  
Relationship Of ◽  
Pm2.5 Concentration

Abstract. Correct description of the boundary layer mixing process of particle is an important prerequisite to understanding the mechanism of heavy pollution episodes. Turbulent mixing process of particles is usually denoted by the turbulent diffusion relationship of heat, meaning that the turbulent transport of particles and heat are similar. This similarity has, however, never been verified. Here we investigate the dissimilarity between particles and heat, indicating that the unified treatment of all scalars in the model is questionable. Using mixing-length theory, the turbulent diffusion relationship of particle is established, embedded in the model and verified on a long-term scale. Simulated results of PM2.5 concentration were improved by 8.3 % (2013), 17 % (2014), 11 % (2015) and 11.7 % (2017) in Eastern China, respectively. However, under the influence of complex topography, the turbulent diffusion process is insensitive to the simulation of the pollutant concentration. In addition to the PM2.5 concentration, the simulation of the CO concentration has also been improved, which shows that the turbulent diffusion process is extremely critical to the change in the concentration of pollutants.

scholarly journals Contributions to the explosive growth of PM<sub>2.5</sub> mass due to aerosol–radiation feedback and decrease in turbulent diffusion during a red alert heavy haze in Beijing–Tianjin–Hebei, China

2018 ◽  
Vol 18 (23) ◽  
pp. 17717-17733 ◽  
Author(s):  
Hong Wang ◽  
Yue Peng ◽  
Xiaoye Zhang ◽  
Hongli Liu ◽  
Meng Zhang ◽  
...  
Keyword(s):  
Diffusion Coefficient ◽  
Atmospheric Chemistry ◽  
Turbulent Diffusion ◽  
Growth Stage ◽  
Eastern China ◽  
Turbulent Diffusion Coefficient ◽  
Chemical Tracers ◽  
Haze Pollution ◽  
Local Inversion ◽  
Radiation Feedback

Abstract. The explosive growth of PM2.5 mass usually results in extreme PM2.5 levels and severe haze pollution in eastern China, and is generally underestimated by current atmospheric chemistry models. Based on one such model, GRAPES_CUACE, three sensitivity experiments – a “background” experiment (EXP1), an “online aerosol feedback” experiment (EXP2), and an “80 % decrease in the turbulent diffusion coefficient of chemical tracers” experiment, based on EXP2 (EXP3) – were designed to study the contributions of the aerosol–radiation feedback (AF) and the decrease in the turbulent diffusion coefficient to the explosive growth of PM2.5 during a “red alert” heavy haze event in China's Jing–Jin–Ji (Beijing–Tianjin–Hebei) region. The results showed that the turbulent diffusion coefficient calculated by EXP1 was about 60–70 m−2 s−1 on a clear day and 30–35 m−2 s−1 on a haze day. This difference in the diffusion coefficient was not enough to distinguish between the unstable atmosphere on the clear day and the extremely stable atmosphere during the PM2.5 explosive growth stage. Furthermore, the inversion calculated by EXP1 was obviously weaker than the actual inversion from sounding observations on the haze day. This led to a 40 %–51 % underestimation of PM2.5 by EXP1; the AF decreased the diffusion coefficient by about 43 %–57 % during the PM2.5 explosive growth stage, which obviously strengthened the local inversion. In addition, the local inversion indicated by EXP2 was much closer to the sounding observations than that indicated by EXP1. This resulted in a 20 %–25 % reduction of PM2.5 negative errors in the model, with errors as low as −16 % to −11 % in EXP2. However, the inversion produced by EXP2 was still weaker than the actual observations, and the AF alone could not completely explain the PM2.5 underestimation. Based on EXP2, the 80 % decrease in the turbulent diffusion coefficient of chemical tracers in EXP3 resulted in near-zero turbulent diffusion, referred to as a “turbulent intermittence” atmospheric state, which subsequently resulted in a further 14 %–20 % reduction of the PM2.5 underestimation; moreover, the negative PM2.5 errors were reduced to −11 % to 2 %. The combined effects of the AF and the decrease in the turbulent diffusion coefficient explained over 79 % of the underestimation of the explosive growth of PM2.5 in this study. The results show that online calculation of the AF is essential for the prediction of PM2.5 explosive growth and peaks during severe haze in China's Jing–Jin–Ji region. Furthermore, an improvement in the planetary boundary layer scheme with respect to extremely stable atmospheric stratification is essential for a reasonable description of local “turbulent intermittence” and a more accurate prediction of PM2.5 explosive growth during severe haze in this region of China.

scholarly journals Modeling microphysical effects of entrainment in clouds observed during EUCAARI-IMPACT field campaign

2013 ◽  
Vol 13 (16) ◽  
pp. 8489-8503 ◽  
Author(s):  
D. Jarecka ◽  
H. Pawlowska ◽  
W. W. Grabowski ◽  
A. A. Wyszogrodzki
Keyword(s):  
Boundary Layer ◽  
Turbulent Mixing ◽  
Marine Boundary Layer ◽  
Cloud Droplet ◽  
Droplet Radius ◽  
Subgrid Scale ◽  
Microphysics Scheme ◽  
Local Conditions ◽  
Field Campaign ◽  
Les Model

Abstract. This paper discusses aircraft observations and large-eddy simulation (LES) modeling of 15 May 2008, North Sea boundary-layer clouds from the EUCAARI-IMPACT field campaign. These clouds are advected from the northeast by the prevailing lower-tropospheric winds and featured stratocumulus-over-cumulus cloud formations. An almost-solid stratocumulus deck in the upper part of the relatively deep, weakly decoupled marine boundary layer overlays a field of small cumuli. The two cloud formations have distinct microphysical characteristics that are in general agreement with numerous past observations of strongly diluted shallow cumuli on one hand and solid marine stratocumulus on the other. Based on the available observations, a LES model setup is developed and applied in simulations using a novel LES model. The model features a double-moment warm-rain bulk microphysics scheme combined with a sophisticated subgrid-scale scheme allowing local prediction of the homogeneity of the subgrid-scale turbulent mixing. The homogeneity depends on the characteristic time scales for the droplet evaporation and for the turbulent homogenization. In the model, these scales are derived locally based on the subgrid-scale turbulent kinetic energy, spatial scale of cloudy filaments, mean cloud droplet radius, and humidity of the cloud-free air entrained into a cloud, all predicted by the LES model. The model reproduces contrasting macrophysical and microphysical characteristics of the cumulus and stratocumulus cloud layers. Simulated subgrid-scale turbulent mixing within the cumulus layer and near the stratocumulus top is on average quite inhomogeneous, but varies significantly depending on the local conditions.

Frontogenesis and frontal arrest of a dense filament in the oceanic surface boundary layer

2017 ◽  
Vol 837 ◽  
pp. 341-380 ◽  
Author(s):  
Peter P. Sullivan ◽  
James C. McWilliams
Keyword(s):  
Boundary Layer ◽  
Turbulent Mixing ◽  
Shear Instability ◽  
Upper Ocean ◽  
Surface Boundary ◽  
Horizontal Shear ◽  
Boundary Layer Turbulence ◽  
Filament Axis ◽  
Small Width ◽  
The Cross

The evolution of upper ocean currents involves a set of complex, poorly understood interactions between submesoscale turbulence (e.g. density fronts and filaments and coherent vortices) and smaller-scale boundary-layer turbulence. Here we simulate the lifecycle of a cold (dense) filament undergoing frontogenesis in the presence of turbulence generated by surface stress and/or buoyancy loss. This phenomenon is examined in large-eddy simulations with resolved turbulent motions in large horizontal domains using${\sim}10^{10}$grid points. Steady winds are oriented in directions perpendicular or parallel to the filament axis. Due to turbulent vertical momentum mixing, cold filaments generate a potent two-celled secondary circulation in the cross-filament plane that is frontogenetic, sharpens the cross-filament buoyancy and horizontal velocity gradients and blocks Ekman buoyancy flux across the cold filament core towards the warm filament edge. Within less than a day, the frontogenesis is arrested at a small width,${\approx}100~\text{m}$, primarily by an enhancement of the turbulence through a small submesoscale, horizontal shear instability of the sharpened filament, followed by a subsequent slow decay of the filament by further turbulent mixing. The boundary-layer turbulence is inhomogeneous and non-stationary in relation to the evolving submesoscale currents and density stratification. The occurrence of frontogenesis and arrest are qualitatively similar with varying stress direction or with convective cooling, but the detailed evolution and flow structure differ among the cases. Thus submesoscale filament frontogenesis caused by boundary-layer turbulence, frontal arrest by frontal instability and frontal decay by forward energy cascade, and turbulent mixing are generic processes in the upper ocean.

scholarly journals Mixing process in turbulent diffusion flames.

1989 ◽  
Vol 55 (510) ◽  
pp. 517-522 ◽  
Author(s):  
Kohoichi SUZUKI ◽  
Kinichi TORIKAI ◽  
Kiyoshi SAKUMA
Keyword(s):  
Turbulent Diffusion ◽  
Diffusion Flames ◽  
Mixing Process ◽  
Turbulent Diffusion Flames
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