Abstract
In this study, the characteristics of turbulence transport and intermittency and the evolutionary mechanisms were studied in different pollution stages of heavy haze weather from December 2016 to January 2017 in the Beijing area using the method developed by Ren et al. (2019) as the automatic identification of atmospheric spectral gaps and the reconstruction of atmospheric turbulence sequences. The results reveal that turbulence intermittency is the strongest in the cumulative stage (CS) of heavy haze weather, followed by in the transport stage (TS), and it is the weakest in the dissipation stage (DS). During the development and accumulation of haze pollution, buoyancy contributes negatively to turbulent kinetic energy (TKE), and horizontal wind speed is low. The classical turbulent motion is often affected by submesoscale motion. As a result, the calculation results of turbulence parameters are affected by submesoscale motion, which causes intensified turbulence intermittency. During the dissipation of pollution, the downward momentum transfer induced by low-level jets provides kinetic energy for turbulent motion in the near surface layer. The turbulent mixing effect is enhanced, and intermittency is weakened. Due to the intermittency of atmospheric turbulence, turbulence parameters calculated from the original fluctuation of meteorological elements may be overestimated. The overestimation of turbulence parameters in the CS is the strongest, followed by the TS, and the DS is the weakest. The overestimation of turbulent fluxes results in an overestimation of atmospheric dissipation capability that may cause an underestimation of pollutant concentrations in the numerical simulations of air quality.
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