Development of Three‐Dimensional Variational Data Assimilation Method of Aerosol for the CMAQ Model: An Application for PM 2.5 and PM 10 Forecasts in the Sichuan Basin

• Published in: Earth and space science (Hoboken, N.J.) Vol. 8; no. 5
• Main Authors: Zhang, Zhendong, Zang, Zengliang, Cheng, Xinghong, Lu, Chunsong, Huang, Shunxiang, Hu, Yiwen, Liang, Yanfei, Jin, Lubin, Ye, Lei
• Format: Journal Article
• Language: English
• Published: Hoboken John Wiley & Sons, Inc 01.05.2021
• Subjects: Aerosols; Air pollution; Air quality; Bias; Correlation coefficient; Data assimilation; Data collection; Dust; Forecasting; Methods; Outdoor air quality; Particulate matter; Pollutants; Variables; Sichuan Basin; United States > US; China
• ISSN: 2333-5084; 2333-5084
• DOI: 10.1029/2020EA001614

Abstract A three‐dimensional variational (3DVAR) data assimilation method for the aerosol variables of the community multiscale air quality (CMAQ) model was developed. This 3DVAR system uses PM 2.5 and PM 2.5‐10 (the difference between PM 10 and PM 2.5 ) as control variables and used the AERO6 aerosol chemical mechanism in the CMAQ model. Two parallel experiments (one with and one without data assimilation [DA]) were performed to evaluate the assimilating effects of surface PM 2.5 and PM 10 during a heavy haze episode from January 13 to 16, 2018 in the Sichuan Basin (SCB) region. The results show that simulations without DA clearly underestimated PM 2.5 and PM 10 concentrations, and the analysis field with aerosol DA is skillful at fitting the observations and effectively improving subsequent forecasts of PM 2.5 and PM 10 . For the analysis fields of PM 2.5 and PM 10 after DA comparing with those without DA, the correlation coefficient (CORR) of PM 2.5 and PM 10 increased by 0.59 and 0.65, the bias (BIAS) increased by 82.29 and 125.41 μg/m 3 , and the root mean square error (RMSE) declined by 73.69 and 116.30 μg/m 3 , respectively. Improvement of subsequent 24‐h forecasts of PM 2.5 and PM 10 with DA is also significant. Statistical results of forecasting improvement with DA indicated that the CORR, BIAS, and RMSE for PM 2.5 and PM 10 at 78% and 89% of stations in the SCB region are improved, respectively. From the perspective of assimilation duration time, the improvement of PM 2.5 and PM 10 can be maintained for ∼24 h. Key Points A three‐dimensional variation data assimilation is developed for the aerosol variables to improve PM 2.5 and PM 10 forecasts in the community multiscale air quality (CMAQ) model The simulation and prediction of PM 2.5 and PM 10 in Sichuan Basin were improved significantly in 24 h by using the assimilation system The rapid deposition and strong localness of PM 2.5‐10 leads to a more limited spread of PM 10 assimilation improvement than that of PM 2.5