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Wind farm wakes have a significant impact on momentum and turbulence fluxes within the atmospheric boundary layer， thereby influencing the local climate and environment.Mesoscale numerical models incorporating wind farm parameterizations are powerful tools for studying the climate and environmental impacts of wind farms.In this study， the wind speed and turbulence kinetic energy profiles of the Fitch wind farm parameterization scheme in the WRF mesoscale model are evaluated in the turbine and wake regions using high-resolution Large Eddy Simulations （LES） as “true values” and a method based on the relation derived from classical momentum theory is proposed to correct the grid-inflow wind speed.The method takes into account the blocking effect caused by the grid equivalent thrust and the corrected wind speed is closer to the free-stream wind speed.Results show that the difference between the grid-inflow wind speed from the original Fitch parameterization scheme and the LES is significant and sensitive to the model horizontal resolution.The Fitch-new parameterization with corrected grid-inflow wind speed reduces the relative error in absolute value between grid-inflow and free-stream wind speed to less than 1% across different horizontal resolutions （1000 m， 500 m， and 250 m）.The spatially averaged thrust and output power are consistent with LES results.The Fitch-new parameterization improves the simulated wind speed deficit in the wake zone of wind turbine， especially in the grid with turbine under high resolution.Although the simulated increase in turbulent kinetic energy and its vertical distribution in the wake zone is improved compared to the original Fitch scheme， there are certain issues that require further investigation.

A dynamic southern branch trough （SBT） and a dynamic northern branch ridge （NBR）， two flows around the Tibetan Plateau （FQXP） generated by the mid-latitude westerlies in the winter half-year （October-April）， have an important influence on the weather and climate of their vicinity and downstream.In this paper， differences between the mean vorticity and the multi-year mean vorticity of two key regions-where the SBT and the NBR are located in 600 hPa are used to represent the intensity of the SBT and the NBR separately.Then the difference between the strength of SBT and NBR is defined visually as the intensity index of FQXP.The relationship between the precipitation and temperature in China and the evolution of FQXP's average in winter half-year and that in autumn， winter and spring is discussed， using correlation analysis and composite analysis.The results show that the FQXP is constant in the winter half-year （October-April）， with significant interannual variations， and the strongest in winter.In the context of global warming， variations in the intensity of FQXP at each period is closely related to the precipitation and temperature in Northwest， Southwest and South China， especially in winter.FQXP has a significant relationship with the temperature changes in northwest， southwest and northeast China， and its best precipitation correlations are found in southwest and south China.Analysis of anomalous large-scale atmospheric circulation and physical quantity fields， such as water vapor flux dispersion and vertical velocity， at each layer in each time period leads to the conclusion that the bottom-up positive pressure characteristic is significant in most of Asia， which well explains the relationship between FQXP anomalies and precipitation and temperature in China at each time period.FQXP anomalies may also be one cause for high-impact weather in China during winter half-year.

Extensive use of wind energy resources in China not only brings huge economic benefits， but also reduces environmental pollution caused by various fossil fuels.However， some studies have shown that the rotation of the rotor blades will increase surface roughness and then enhance turbulence in the rotor wakes by changing the stability of the atmospheric boundary layer （ABL）.If these changes are spatially large enough， they may affect the local and even regional weather and climate.With increasing installed capacity of wind farms， a correct understanding of the possible impacts of wind power on climate is needed， especially its impact on temperature.It is of great significance for the scientific use of wind energy resources and ensuring the sustainable development of energy in China.Satellite remote sensing technology can conduct large-scale， long-term， and regular observations of the land surface， and data acquisition is relatively easy.The MODIS satellite data has been used in studies of the impact of wind farms on the land surface temperature.Based on MODIS satellite data from 2003 to 2019， this paper explores the impact of two large wind farms in Shangyi and Zhangbei in northern Zhangjiakou on land surface temperature.Combined with wind direction， vegetation index， surface albedo， and land use data， the results show that： （1）During spring， autumn， and winter， the land surface temperature downwind of the wind farm is higher than that upwind during nighttime， the opposite is true during the day.The land surface temperature in the downwind area of the wind farm is significantly affected by the wind turbine wake.（2）Compared with the surrounding areas not affected by wind farms， the land surface temperature within wind farms increased significantly in spring and summer， especially during spring night with an increaing trend of 0.379 ℃·（10a）-1， followed by summer night and autumn night with increaing trends of 0.206 ℃·（10a）-1 and 0.211 ℃·（10a）-1， respectively.The warming trends` spatial pattern couples well with the geographic distribution of wind turbines.（3）Although wind farms in Shangyi and Zhangbei have impacts on LST， compared with other studies the increasing trends caused by wind farms in this study area is weak and much smaller than the interannual LST increasing trend in the Bashang area with increaing trends of 0.817 ℃·（10a）-1， the land surface temperature increases caused by the operation of wind turbines is negligible compared with which caused by fossil fuels.Changes in land surface temperature within wind farms are not only affected by the operation of wind farms but also a combination of various influencing factors， such as climate change， vegetation， and land-use changes.

The influence of wind farms on atmospheric boundary layer structures， especially turbulence characteristics， is an important topic in the field of wind energy meteorology.Taking Qilinshan Wind Farm in Shangyi County， Hebei Province as the research object， this paper explores the effect of wind farm operation on turbulence intensity using long-term （2006 -2019） wind data from 70 m and 50 m of a 70 m-wind tower in the wind farm.May of each year was selected for analysis considering the consistency and complement of the data.A comparative analysis of turbulence intensities before （2006 -2009） and after （2010 -2019） the wind farm construction is conducted.The results show that the turbulence intensity shifted to higher values， i.e.， the turbulence increased， after the construction of the wind farm， with the turbulence intensity increased more during night than that during the day.Turbulence intensity decreased with the increase of wind speed.The operation of wind farm increased the turbulence intensity significantly when wind speed was in 4~15 m·s-1 for 70 m， and 3~12 m·s-1 for 50 m.Turbulence intensity had obvious diurnal variation.The operation of wind farm increased the turbulence intensity both during the day and night.The increase of turbulence intensity at 50 m was slightly greater than that at 70 m during the day， but was lower than that at 70 m during night， which made the turbulence intensity difference between the two layers and between day and night decreased after the wind farm construction.

The relative importance of topography and soil moisture on the initiation of an afternoon deep convection under weak synoptic-scale forcing was investigated using the weather research and forecasting (WRF) model with high resolution (1.33 km). The convection occurred on 29 June 2017, over the Liupan Mountains, west of the Loess Plateau. The timing and location of the convective initiation (CI) simulated by the WRF model compared well with the radar observations. It showed that the warm and humid southerly airflow under 700 hPa was divided into east and west flows due to the blockage of the Liupan Mountains. The warm and humid air on the west side was forced to climb along the slope and enhanced the humidity near the ridge. The accumulation of unstable energy in the middle and north of the ridge led to a strong vertical convergence and triggered the convection. Sensitivity experiments showed that terrain played a dominant role in triggering the convection, while the spatial heterogeneity of soil moisture played an indirect role by affecting the local circulation and the partition of surface energy.