Abstract
Abstract. The authors developed a three-dimensional variational (3-DVAR) aerosol extinction coefficient (AEC) and aerosol mass concentration (AMC) data assimilation (DA) system for aerosol variables in the Weather Research and Forecasting–Chemistry (WRF–Chem) model with the WRF–Chem using the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) scheme. They establish an AEC observation operator and its corresponding adjoint based on the Interagency Monitoring of Protected Visual Environments (IMPROVE) equation and investigate the use of lidar AEC and surface AMC DA to forecast mass concentration (MC) profiles of PM2.5 (particulate matter with an aerodynamic diameter of less than 2.5 µm) across China. Two sets of data were assimilated: AEC profiles captured by five conventional Mie scattering lidars (positioned in Beijing, Shijiazhuang, Taiyuan, Xuzhou, and Wuhu) and PM2.5 and PM10 MC data obtained from over 1500 ground environmental monitoring stations across China. Three DA experiments (i.e., a PM2.5 (PM10) DA experiment, a lidar AEC DA experiment, and a simultaneous PM2.5 (PM10) and lidar AEC DA experiment) with a 12 h assimilation period and a 24 h forecast period were conducted. The PM2.5 (PM10) DA reduced the root mean square error (RMSE) of the surface PM2.5 MC in the initial field of the model by 38.6 µg m−3 (64.8 %). When lidar AEC data were assimilated, this reduction was 10.5 µg m−3 (17.6 %), and a 38.4 µg m−3 (64.4 %) reduction occurred when the two data sets were assimilated simultaneously, although only five lidars were available within the simulation region (approximately 2.33 million km2 in size). The RMSEs of the forecasted surface PM2.5 MC 24 h after the DA period in the three DA experiments were reduced by 6.1 µg m−3 (11.8 %), 1.5 µg m−3 (2.9 %), and 6.5 µg m−3 (12.6 %), respectively, indicating that the assimilation and hence the optimization of the initial field have a positive effect on the PM2.5 MC forecast performance over a period of 24 h after the DA period.
Highlights
Aerosol data assimilation (DA) generates a threedimensional (3D) gridded analysis field capable of describing the spatial distribution of aerosols by integrating numerical forecasts produced by an air quality model (AQM) and measured aerosol data
The root mean square error (RMSE) of the forecasted surface PM2.5 mass concentration (MC) 24 h after the DA period in the three DA experiments were reduced by 6.1 μg m−3 (11.8 %), 1.5 μg m−3 (2.9 %), and 6.5 μg m−3 (12.6 %), respectively, indicating that the assimilation and the optimization of the initial field have a positive effect on the PM2.5 MC forecast performance over a period of 24 h after the DA period
This study presents an observation operator and corresponding adjoint module developed for lidar aerosol extinction coefficient (AEC) DA based on the Interagency Monitoring of Protected Visual Environments (IMPROVE) equation, which was introduced into the DA system by Li et al (2013) and Zang et al (2016) for the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) aerosol scheme oriented to the WRF– Chem model
Summary
Aerosol data assimilation (DA) generates a threedimensional (3D) gridded analysis field capable of describing the spatial distribution of aerosols by integrating numerical forecasts produced by an air quality model (AQM) and measured aerosol data. With integrated information from various sources, this analysis field can more accurately describe the 3D distribution pattern of aerosols The analysis field generated by DA can be used to effectively study atmospheric aerosol transmission patterns through an analysis of the products of a certain time series and, on this basis, further examine the effects of aerosols on human health, the environment, the weather, and the climate (Baraskar et al, 2016; Haywood and Boucher, 2020). The analysis field can be used to determine the initial chemical conditions for an AQM. Improving the accuracy of the initial chemical conditions and enhancing the forecasting performance of the AQM for aerosols (Wu et al, 2015)
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