Different Turbulent Regimes and Vertical Turbulence Structures of the Urban Nocturnal Stable Boundary Layer

Turbulence in the nocturnal boundary layer(NBL)is still not well characterized,especially over complex underlying surfaces.Herein,gradient tower data and eddy covariance data collected by the Beijing 325-m tower were used to better understand the differentiating characteristics of turbulence regimes and vertical turbulence structure of urban the NBL.As for heights above the urban canopy layer(UCL),the relationship between turbulence velocity scale(VTKE)and wind speed(V)was consistent with the“HOckey-Stick”(HOST)theory proposed for a relatively flat area.Four regimes have been identified according to urban nocturnal stable boundary layer.Regime 1 occurs where local shear plays a leading role for weak turbulence under the constraint that the wind speed V<VT(threshold wind speed).Regime 2 is determined by the existence of strong turbulence that occurs when V>VT and is mainly driven by bulk shear.Regime 3 is identified by the existence of moderate turbulence when upside-down turbulence sporadic bursts occur in the presence of otherwise weak turbulence.Regime 4 is identified as buoyancy turbulence,when V>VT,and the turbulence regime is affected by a combination of local wind shear,bulk shear and buoyancy turbulence.The turbulence activities demonstrated a weak thermal stratification dependency in regime 1,for which within the UCL,the turbulence intensity was strongly affected by local wind shear when V<VT.This study further showed typical examples of different stable boundary layers and the variations between turbulence regimes by analyzing the evolution of wind vectors.Partly because of the influence of large-scale motions,the power spectral density of vertical velocity for upsidedown structure showed an increase at low frequencies.The upside-down structures were also characterized by the highest frequency of the stable stratifications in the higher layer.

二项-二维对数正态分布及其在极端海况预测中的应用

Extreme value analysis is an indispensable method to predict the probability of marine disasters and calculate the design conditions of marine engineering.The rationality of extreme value analysis can be easily affected by the lack of sample data.The peaks over threshold(POT)method and compound extreme value distribution(CEVD)theory are effective methods to expand samples,but they still rely on long-term sea state data.To construct a probabilistic model using shortterm sea state data instead of the traditional annual maximum series(AMS),the binomial-bivariate log-normal CEVD(BBLCED)model is established in this thesis.The model not only considers the frequency of the extreme sea state,but it also reflects the correlation between different sea state elements(wave height and wave period)and reduces the requirement for the length of the data series.The model is applied to the calculation of design wave elements in a certain area of the Yellow Sea.The results indicate that the BBLCED model has good stability and fitting effect,which is close to the probability prediction results obtained from the long-term data,and reasonably reflects the probability distribution characteristics of the extreme sea state.The model can provide a reliable basis for coastal engineering design under the condition of a lack of marine data.Hence,it is suitable for extreme value prediction and calculation in the field of disaster prevention and reduction.

Impacts of Atmospheric Boundary Layer Vertical Structure on Haze Pollution Observed by Tethered Balloon and Lidar

In this paper, the characteristics of the atmospheric boundary layer(ABL) vertical structure over the North China Plain(NCP) during a comprehensive observation experiment conducted during 15–21 December 2018 were investigated. Observational data were obtained with a large tethered balloon, Doppler wind lidar, and ground-level instruments. The maximum concentration of PM_(2.5) exceeded 200 μg m^(-3), and the ratio of PM_(2.5)/PM_(10) was approximately 0.4(its maxi-mum was approximately 0.8) during the whole observation period, indicating the explosive growth of dominant fine-mode aerosols in the winter heating season. Elevated concentrations of pollutants decreased the solar irradiance received by the ground, resulting in lower temperature at ground level. Our results illustrate three distinct types of vertical profiles: Type 1(convective state)—the concentration of PM_(2.5) decreased nearly linearly with increase of the height below approximately 600 m;Type 2(stable state)—the PM_(2.5) concentration sharply decreased from the ground to approximately 200 m;and Type 3(multilayer structure)—some pollutants were suspended aloft in the upper air layer. Diurnal evolution of the vertical profiles of PM_(2.5) and their relationship with the changes in meteorological factors were identified. From daytime to nighttime, the vertical profiles evolved from Type 1 to Type 2 or Type 3. All the 33 vertical PM_(2.5) profiles that we obtained showed a strong relationship with elements of the ABL structure, such as the distributions of winds, the inversion layer, and turbulence activities. A light-wind layer and weak turbulence activity, especially within the inversion layer, contributed greatly to the accumulation of pollutants.Vertical PM_(2.5) concentration patterns were also greatly affected by local ground-level emission sources and regional transport processes.