This spring, sand and dust weather occurred frequently in China. Recently, the research team of Huang Jianping, academician of the Chinese Academy of Sciences and professor of Lanzhou University, with the help of advanced air pollution models and satellite remote sensing technology, found that in the frequent dust weather events in China this spring, Mongolia’s average contribution to the sand and dust in northern China is about 42%, and the average contribution of the Taklamakan Desert is about 26%. In addition, the study further integrates ground-based observation and satellite remote sensing observations, and uses machine learning methods to revise the model dust forecasting, which effectively improves the effect of sand and dust forecasting. The relevant research results were published as cover articles in Progress in Atmospheric Science.
Cover of Advances in Atmospheric Science, Issue 9, 2023 (Photo courtesy of the editorial office of the journal)
Chen Siyu, the first author of the paper and a professor at Lanzhou University, pointed out that since January this year, 12 sand and dust processes have occurred in northern China, and the number of sand and dust that has occurred since 2023 is the largest in the same period in the past 10 years. Among them, the dust events that occurred from March 19 to 24 and April 9 to 11 have reached the level of sandstorms.
Chen Siyu said that the two weather systems of cold front and Mongolian cyclone dominated these events, causing widespread sand in Mongolia and promoting cross-border transportation of sand and dust, resulting in short-term strong sandstorms in many places in China. As the cold front continues to push south, the sand and dust also spread south, resulting in serious pollution in the Yangtze River basin.
In order to further reveal the influence of different sand sources on sand and dust events in China, the research team determined the source and transport path of sand and dust in northern China, and quantified the contribution of different sand sources to the sand and dust concentration in northern China by using the concentration weight trajectory analysis method. The results of the study show that among the 10 dust events that occurred in northern China this spring, Mongolia’s average contribution to the sand and dust in northern China was about 42%, and the average contribution of the Taklamakan Desert was about 26%.
At the same time, in order to improve the refined forecasting of sand and dust disaster weather events in China, the research team effectively integrated ground-based observation, satellite remote sensing and other observation data, and used machine learning methods to revise the forecast results of WRF-Chem model. Aiming at the problems of uneven data distribution and long-tail distribution in extreme weather data, the research team used the SMOTE resampling algorithm to resample the training data to make the learning data distribution balanced and avoid the preference of the machine learning model to bias the “head data” learning. The comparative experimental results show that the revised model can effectively revise the forecast results of the WRF-Chem model, and improve the prediction accuracy of key indicators such as PM10 in dust weather events.
Huang Jianping, the corresponding author of the paper, said: “We are currently carrying out large-scale field comprehensive observation experiments to develop the parameterization scheme of wind erosion sand on different underlying surfaces to improve the simulation accuracy of sand and dust. Based on machine learning and fusion of multi-source data, a refined forecasting system for sand and dust weather with high temporal and spatial resolution is developed. The implementation of this study will enhance the early identification ability of sand and dust weather, improve the refined sand and dust disaster early warning and forecasting system, effectively improve the level of joint prevention and control of sand and dust weather and its secondary disasters in China, and contribute to China’s disaster weather forecasting. (Source: China Science News Gao Yali)
Related paper information:http://www.iapjournals.ac.cn/aas/en/article/doi/10.1007/s00376-023-3062-1
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