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Wind power, as one of the primary clean energies, is an important way to achieve the goals of carbon peak and carbon neutrality. Therefore, high-resolution measurement and accurate forecasting of wind speed are very important in the organization and dispatching of the wind farm. In this study, several methodologies, including the mesoscale WRF (Weather Research and Forecasting(WRF) model, mathematical statistics algorithms, and machine learning algorithms, were adopted to systematically explore the predictability and optimization of wind speed of a Gobi grassland wind farm located in western Inner Mongolia. Results show that the rear-row turbines were significantly affected by upwind turbine wakes. The output power of upwind-group turbines was 591 KW with an average wind speed of 7.66 m/s, followed by 532 KW and 7.02 m/s in the middle group and 519 KW and 6.92 m/s in the downwind group. The higher the wind speed was, the more significantly the wake effect was presented. Intercomparison between observations and WRF simulations showed an average deviation of 3.73 m/s. Two postprocessing methods of bilinear interpolation and nearest replacement could effectively reduce the errors by 34.85% and 36.19%, respectively, with average deviations of 2.43 m/s and 2.38 m/s. A cycle correction algorithm named Average Variance–Trend (AVT) can further optimize the errors to 2.14 m/s and 2.13 m/s. In another aspect, the categorical boosting (CatBoost) artificial intelligence algorithm also showed a great performance in improving the accuracy of WRF outputs, and the four-day average deviation of 26–29 September decreased from 3.21 m/s to around 2.50 m/s. However, because of the influence of large-scale circulations, there still exist large errors in the results of various correction algorithms. It is therefore suggested through the investigation that data assimilation of the northwest and Mongolian plateau, boundary layer parameterization scheme optimization, and embedding of high-resolution topographic data could have great potential for obtaining more accurate forecasting products. [ABSTRACT FROM AUTHOR]

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The processes of dust emission, the development, and dissipation of sand and dust storms all exhibit dynamic and rapidly changing characteristics, posing significant challenges for us to accurately represent their complex behaviors in numerical simulations. In the numerical weather prediction model, the selection and optimization of planetary boundary layer (PBL) parameterization schemes have a direct impact on the simulation results of PBL wind fields, primarily due to the significant uncertainty in the fine-grained simulation of PBL wind field structure. To gain a deeper understanding of the sources of uncertainty in simulating sand-dust storms through vertical wind field structure simulations of the PBL, this study utilizes high-resolution 3D Doppler wind lidar (DWL) data. Focusing on sand-dust storm wind fields with distinctive regional characteristics in Inner Mongolia, China, it examines the turbulent momentum equation and assumptions of the PBL. A comprehensive and systematic evaluation was conducted on the characteristics of horizontal and vertical wind fields, wind shear, as well as the dynamic and thermodynamic differences that arise when simulating typical sand-dust storm wind fields using the Weather Research and Forecasting (WRF) Model with various PBL schemes. This evaluation focused on simulating the typical wind field of sand-dust storms during various stages, including dust generation, occurrence, development, and dissipation. The findings indicate that non-adiabatic heating plays a significant role in the sustainability and evolution of sand-dust storms. The wind shear index holds particular importance as it embodies the dynamic factor in the model. The PBL schemes such as Mellor-Yamada Nakanishi and Niino Level 2.5 (MYNN2), A new version of asymmetric convective model (ACM2), and Grenier-Bretherton-McCaa (GBM), which factor in buoyancy effects, demonstrate more accurate wind shear simulations in sand-dust storms. Furthermore, the ACM2 scheme effectively replicates the fundamental variation characteristics of meteorological elements throughout the sand-dust storm lifecycle. [Display omitted] • 3D Doppler wind LiDAR were compared with the sand storm simulation results of 5 boundary layer parameterization schemes. • Evaluation of the applicability of PBLS for sandstorm weather in Inner Mongolia for the first time. • The schemes of MYNN2, ACM2, GBM factor in buoyancy effects, shown more accurate wind shear simulations in dust storms. • The dynamic and thermodynamic differences were considered on simulating the typical wind field of dust storms across stages. [ABSTRACT FROM AUTHOR]

In January 2020, severe and persistent haze events occurred in the plateau city of Hohhot, which was one of the regions with the worst air quality in China. The monthly average concentration of PM 1 , PM 2.5 and PM 10 came to 74 μg·m−3, 106 μg·m−3 and 130 μg·m−3, respectively. Coal burning for heating, pollutant transport, and stable atmosphere led to frequent haze events. Local temperature inversion and regional neutral atmospheric stability provided good meteorological conditions for the accumulation of particulate matter. The southerly and southeasterly winds carried in primary and secondary pollutants, as well as large amounts of water vapor, which was an important reason for the haze events this month. The large increase of fine particles was mainly caused by the massive generation of sulfate and nitrate aerosols, with monthly average concentrations of 26 μg·m−3 and 20 μg·m−3, respectively. During the day, SO 2 was rapidly converted to SO 4 2− through photochemical and heterogeneous reactions under the effect of sufficient NO 2 and high humidity. Meanwhile, the conversion of NO x to NO 3 − was also enhanced. During the night, suspended fine particles generated during the day accumulated, and the increased humidity strengthened the oxidation of S(IV) to SO 4 2− again and NO 2 and N 2 O 5 to NO 3 − under the effect of NO 2 and HONO. The sulfur oxidation rate (SOR) and nitrogen oxidation rate (NOR) rose from ~0.20 and ~0.10 in the cleaning period to ~0.60 and ~0.30 in the pollution period, respectively. In addition, the setting off of fireworks, firecrackers and the traditional bonfires caused a steep rise in PM 2.5 to ~1120 μg·m−3 at midnight on the Chinese Spring Festival. A strong dry north wind broke the static and neutral structure of the atmosphere and completely remove the pollutants from the region. [Display omitted] • Persistently severe haze events occurred in the large city of the Mongolia plateau, in winter. • Coal burning for heating, pollutant transport and neutral or stable atmosphere led to frequent haze events. • Sufficient NO 2 catalytically accelerated heterogeneous reactions of SO 2 to SO 4 2− in high humid conditions. • The fireworks and firecrackers sharply increased PM 2.5 in a short period of time during the Spring Festival. [ABSTRACT FROM AUTHOR]