Predicting air quality using a quantitative forecasting model of PM2.5 micro-scale variation nested with wave spectrum analysis - Wang, Deying; Wang, Jizhi; Yang, Yuanqin; Jia, Wenxing; Zhong, Junting; Wang, Yaqiang; Zhang, Xiaoye (Frontiers in Environmental Science, 2023)
This study has focused on weather and environmental numerical prediction and public demand. It has expanded the concept and technology growth points in new fields in terms of new tasks for major prediction services for "large-scale public events". This is required for more advanced prediction and to improve the resolution, fineness, and accuracy of the prediction. This study has explored the prediction theory and technical application of transient atmospheric aerosol pollution within an accuracy of an hour. The novelty of this study is:①Based on high-quality big data, covering the Northern Hemisphere, with high temporal resolution with an accuracy of one hour, a quantitative theory of "natural weather cycle" spectral analysis algorithm was developed. This study has presented a quantitative forecast model that nests the "spectral analysis of atmospheric wave-like disturbance" in the westerly belt with the "transient characteristics" of micro-scale aerosols (PM 2.5 concentration) in Beijing and North China. ② According to the nested model of this study, the wave-like oscillation (H') of 500 hPa was positively correlated with the PLAM index, PLAM index and PM 2.5 mass concentration during nested multi-"natural weather cycles". The significance level exceeded 0.001. This study has demonstrated the prediction abilities of early quantitative fine prediction theory and implementation in the context of air quality. The forecast service on October 1, 2022, and for the opening of the CCP 20th National Congress (16 October) and during the conference was successfully presented in real time. The results of this study on hourly resolution high-precision air quality forecasting service have shown that rolling forecasts can be continuously released 1month in advance and 7-10 d in advance, and the nesting effect can constantly be updated. Forecasts were found to be consistent with reality.③The nested mode method for atmospheric spectrum analysis and micro-scale aerosol (PM 2.5 ) distribution provides quantitative analysis and a decision-making basis for business-oriented operations to address technical difficulties.