Abstract
Dust storms are one of the most frequent meteorological disasters in China, endangering agricultural production, transportation, air quality, and the safety of people’s lives and property. Against the backdrop of climate change, Mongolia’s contribution to China’s dust cannot be ignored in recent years. In this study, we used the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), along with dynamic dust sources and the HYSPLIT model, to analyze the contributions of different dust sources to dust concentrations in northern China in March and April 2023. The results show that the frequency of dust storms in 2023 was the highest observed in the past decade. Mongolia and the Taklimakan Desert were identified as two main dust sources contributing to northern China. Specifically, Mongolia contributed more than 42% of dust, while the Taklimakan Desert accounted for 26%. A cold high-pressure center, a cold front, and a Mongolian cyclone resulted in the transport of dust aerosols from Mongolia and the Taklimakan Desert to northern China, where they affected most parts of the region. Moreover, two machine learning methods [the XGBoost algorithm and the Synthetic Minority Oversampling Technique (SMOTE)] were used to forecast the dust storms in March 2023, based on ground observations and WRF-Chem simulations over East Asia. XGBoost-SMOTE performed well in predicting hourly PM10 concentrations in China in March 2023, with a mean absolute error of 33.8 µg m−3 and RMSE of 54.2 µg m−3.
摘 要
沙尘暴是中国最为常见的气象灾害之一,严重危害了农业生产、交通运输、空气质量和人民生命财产安全。在气候变化的大背景下,蒙古国对我国近年来的沙尘贡献不可忽视。2023年是近10年来我国沙尘事件发生最为频繁的一年。利用耦合了动态沙源的WRF-Chem数值模式,同时结合动态沙源以及HYSPLIT模型,揭示了2023年3月和4月不同沙源对我国北方沙尘浓度的贡献。研究表明,蒙古对我国北方沙尘浓度贡献超过42%,而塔克拉玛干沙漠的贡献约为26%。此外,基于地面观测、卫星遥感等观测资料,还研究了利用机器学习方法对WRF-Chem数值模式预报结果的订正方法,提高了数值模式对沙尘天气中PM10等关键指标的预报准确率。
Article PDF
Avoid common mistakes on your manuscript.
References
Bao, T. N., T. Gao, B. Nandintsetseg, M. Yong, and E. Jin, 2021: Variations in frequency and intensity of dust events crossing the Mongolia-China border. SOLA, 17, 145–150, https://doi.org/10.2151/sola.2021-026.
Chen, T., and C. Guestrin, 2016: Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794, https://doi.org/10.1145/2939672.2939785.
Grell, G. A., S. E. Peckham, R. Schmitz, S. A. McKeen, G. Frost, W. C. Skamarock, and B. Eder, 2005: Fully coupled “online” chemistry within the WRF model. Atmos. Environ., 39, 6957–6975, https://doi.org/10.1016/j.atmosenv.2005.04.027.
Kaufman, Y. J., A. E. Wald, L. A. Remer, B. C. Gao, R. R. Li, and L. Flynn, 1997: The MODIS 2.1-µm channel-correlation with visible reflectance for use in remote sensing of aerosol. IEEE Trans. Geosci. Remote Sens., 35, 1286–1298, https://doi.org/10.1109/36.628795.
Ministry of Environment and Tourism of Mongolia (METM), 2018: Third National Communication of Mongolia, under the United Nations Framework Convention on Climate Change. [Available online from https://www.developmentaid.org/donors/view/143596/ministry-of-environment-and-tourism-of-mongolia]
Qian, W. H., X. Tang, and L. S. Quan, 2004: Regional characteristics of dust storms in China. Atmos. Environ., 38, 4895–4907, https://doi.org/10.1016/j.atmosenv.2004.05.038.
Salomonson, V. V., W. L. Barnes, P. W. Maymon, H. E. Montgomery, and H. Ostrow, 1989: MODIS: Advanced facility instrument for studies of the Earth as a system. IEEE Trans. Geosci. Remote Sens., 27, 145–153, https://doi.org/10.1109/36.20292.
Stein, A. F., R. Draxler, G. Rolph, B. Stunder, M D. Cohen, and F. Ngan, 2015: NOAA’s HYSPLIT atmospheric transport and dispersion modeling system. Bull. Amer. Meteor. Soc., 96(12), 2059–2077, https://doi.org/10.1175/BAMS-D-14-00110.1.
Vova, O., M. Kappas, T. Renchin, and J. Degener, 2015: Land degradation assessment in Gobi-Altai province. Proc. of the Trans-Disciplinary Research Conference: Building Resilience of Mongolian Rangelands, Ulaanbaatar, Mongolia, 54–59, https://doi.org/10.25675/10217/181731.
Xiong, X. X., B. N. Wenny, and W. D. Barnes, 2009: Overview of NASA Earth Observing Systems Terra and Aqua moderate resolution imaging spectroradiometer instrument calibration algorithms and on-orbit performance. Journal of Applied Remote Sensing, 3, 032501, https://doi.org/10.1117/1.3180864.
Yin, Z. C., Y. Wan, Y. J. Zhang, and H. J. Wang, 2022: Why super sandstorm 2021 in North China. National Science Review, 9, nwab165, https://doi.org/10.1093/nsr/nwab165.
Zhang, K., and H. W. Gao, 2007: The characteristics of Asian-dust storms during 2000–2002: From the source to the sea. Atmos. Environ., 41, 9136–9145, https://doi.org/10.1016/j.atmosenv.2007.08.007.
Zhang, Z. H., and D. Huisingh, 2018: Combating desertification in China: Monitoring, control, management and revegetation. Journal of Cleaner Production, 182, 765–775, https://doi.org/10.1016/j.jclepro.2018.01.233.
Acknowledgements
This work was jointly supported by a project supported by the Joint Fund of the National Natural Science Foundation of China and the China Meteorological Administration (Grant No. U2242209), and the National Natural Science Foundation of China (Grant No. 42175106).
Author information
Authors and Affiliations
Corresponding author
Additional information
Article Highlights
• Cold fronts and a Mongolian cyclone caused Mongolian dust to be transported to northern China.
• During dust events in March and April, Mongolia was responsible for 42% of dust storms experienced by China.
• XGBoost combined with SMOTE technology performed well in predicting hourly PM10 concentrations in China.
Rights and permissions
About this article
Cite this article
Chen, S., Zhao, D., Huang, J. et al. Mongolia Contributed More than 42% of the Dust Concentrations in Northern China in March and April 2023. Adv. Atmos. Sci. 40, 1549–1557 (2023). https://doi.org/10.1007/s00376-023-3062-1
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00376-023-3062-1