Improvement of PM2.5 forecast over China by the joint adjustment of initial conditions and emissions with the NLS-4DVar method

Shan, Zhiguang; Tian, Xiangjun; Xiao, Han; Zhang, Meigen; Zhang, Hongqin; Mao, Huiqin

doi:10.1016/j.atmosenv.2021.118896

Cited by 5 publications

(1 citation statement)

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“…For instance, various studies have examined the direct impact of meteorological factor forecasts on the prediction of atmospheric pollutants [ 20 ], the utilization of micro-pulse lidar observations [ 21 ], the incorporation of Global Positioning System Zenith Total Delay data [ 22 ], and the implementation of an urban canopy parameterization scheme [ 23 ] to enhance the accuracy of fine particle forecasting using the CMAQ model. Researchers have also used machine learning [ 24 ], four-dimensional variational assimilation [ 25 ], and Kalman filter [ 26 ] methods to optimize fine particle forecasts by the CMAQ model. Also, the CMAQ model has been used to analyze the impact of changes in meteorological conditions on the regional transmission of fine particles [ 27 , 28 ], thereby facilitating policy-making related to the control of regional fine particulate emissions [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

Forecast of Fine Particles in Chengdu under Autumn–Winter Synoptic Conditions

Yang,

Wang,

Zhang

2023

Toxics

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We conducted an evaluation of the impact of meteorological factor forecasts on the prediction of fine particles in Chengdu, China, during autumn and winter, utilizing the European Cooperation in Science and Technology (COST)733 objective weather classification software and the Community Multiscale Air Quality model. This analysis was performed under four prevailing weather patterns. Fine particle pollution tended to occur under high-pressure rear, homogeneous-pressure, and low-pressure conditions; by contrast, fine particle concentrations were lower under high-pressure bottom conditions. The forecasts of fine particle concentrations were more accurate under high-pressure bottom conditions than under high-pressure rear and homogeneous-pressure conditions. Moreover, under all conditions, the 24 h forecast of fine particle concentrations were more accurate than the 48 and 72 h forecasts. Regarding meteorological factors, forecasts of 2 m relative humidity and 10 m wind speed were more accurate under high-pressure bottom conditions than high-pressure rear and homogeneous-pressure conditions. Moreover, 2 m relative humidity and 10 m wind speed were important factors for forecasting fine particles, whereas 2 m air temperature was not. Finally, the 24 h forecasts of meteorological factors were more accurate than the 48 and 72 h forecasts, consistent with the forecasting of fine particles.

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