Sand and dust storms (SDSs) cause major disasters in northern China. They have serious impacts on human health, daily life, and industrial and agricultural production, in addition to threatening the regional ecological environment and social economy. Based on meteorological observational data and the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 dataset for spring 2000–2021, we used the Lamb–Jenkinson circulation classification method to classify the three major areas influencing SDSs in northern China. We also used the k-means clustering method to classify the overall circulation pattern in northern China. Our results show that the circulation types favoring SDSs in the southern basin of Xinjiang are southwesterly winds (SW), cyclones (C), and anticyclones (A). The circulation types favoring SDSs in western Inner Mongolia and southern Mongolia are northwesterly winds (NW), northerly winds (N), cyclones (C), and anticyclones (A). The circulation types favoring SDSs in central Inner Mongolia are northwesterly winds (NW), northerly winds (N), southwesterly winds (SW), and anticyclones (A). The 500 hPa and surface circulation patterns in China can be divided into nine types. Among them, five dominant circulation patterns favor strong SDSs: a cold high-pressure region and cold front (T1), a Mongolian cyclone (T2), a mixed type of Mongolian cyclone and cold front (T3), a thermal depression and cold front (T5), and a cold front (T8). During 2000–2004, the T8 circulation pattern occurred most frequently as the main influencing circulation. From 2005 to 2010, the T3 and T8 circulation patterns dominated. Circulation patterns T1 and T3 dominated during 2011–2015 and 2016–2020, respectively. We analyzed the main circulation patterns for four SDS events occurring in 2021 by combining the Lamb–Jenkinson and k-means methods. The SDS events in 2021 were closest to the T3 circulation pattern and were mainly influenced by Mongolian cyclones and surface cold fronts. The main propagation paths were westerly and northwesterly.

Northern China experienced a severe sand and dust storm (SDS) on 14/15 March 2021. It was difficult to simulate this severe SDS event accurately. This study compared the performances of three dust-emission schemes on simulating PM₁₀ concentration during this SDS event by implementing three vertical dust flux parameterizations in the Comprehensive Air-Quality Model with Extensions (CAMx) model. Additionally, a statistical gusty-wind model was implemented in the dust-emission scheme, and it was used to quantify the gusty-wind contribution to dust emissions and peak PM₁₀ concentration. As a result, the LS scheme (Lu and Shao 1999) produced the minimum errors for peak PM₁₀ concentrations, the MB scheme (Marticorena and Bergametti 1995) underestimated the PM₁₀ concentrations by 70–90%, and the KOK scheme (Kok et al. 2014) overestimated PM₁₀ concentrations by 10–50% in most areas. The gusty-wind model could reasonably reproduce the probability density function of 2-min wind speeds. There were 5–40% more dust-emission flux and 5–40% more peak PM₁₀ concentrations generated by the gusty wind than the hourly wind in the dust-source regions. The increase of peak PM₁₀ concentration caused by gusty wind in the non-dust-source regions was higher than in the dust-source regions, with 10–50%. Implementing the gusty-wind model could help improve the LS scheme’s performance in simulating PM₁₀ concentrations of this severe SDS event. More work is still needed to investigate the reliability of the gusty-wind model and LS scheme on various SDS events.

In this study, based on the National Centers for Environmental Prediction (NCEP) Final Analysis (FNL) data, the reliability and performances of their application on clean days and polluted days (based on the PM_2.5 mass concentrations) in Beijing were assessed. Conventional meteorological factors and diagnostic physical quantities from the NCEP/FNL data were compared with the L-band radar observations in Beijing in the autumns and winters of 2017–2019. The results indicate that the prediction reliability of the temperature was the best compared with those of the relative humidity and wind speed. It is worth noting that the relative humidity was lower and the near-surface wind speed was higher on polluted days from the NCEP/FNL data than from the observations. As far as diagnostic physical quantity is concerned, it was revealed that the temperature inversion intensity depicted by the NCEP/FNL data was significantly lower than that from the observations, especially on polluted days. For example, the difference in the temperature inversion intensity between the NCEP/FNL data and the observation ranged from −0.56 to −0.77 °C on polluted days. In addition, the difference in the wind shears between the NCEP/FNL reanalysis data and the observations increased to 0.40 m/s in the lower boundary layer on polluted days compared with that on clean days. Therefore, it is suggested that the underestimation of the relative humidity and temperature inversion intensity, and the overestimation of the near-surface wind speed should be seriously considered in simulating the air quality in the model, particularly on polluted days, which should be focused on more in future model developments.