Understanding and improving model representation of aerosol optical properties for a Chinese haze event measured during KORUS-AQ

Abstract

Abstract. KORUS-AQ was an international cooperative air quality field study in South Korea that measured local and remote sources of air pollution affecting the Korean Peninsula during May–June 2016. Some of the largest aerosol mass concentrations were measured during a Chinese haze transport event (24 May). Air quality forecasts using the WRF-Chem model with aerosol optical depth (AOD) data assimilation captured AOD during this pollution episode but overpredicted surface particulate matter concentrations in South Korea, especially PM2.5, often by a factor of 2 or larger. Analysis revealed multiple sources of model deficiency related to the calculation of optical properties from aerosol mass that explain these discrepancies. Using in situ observations of aerosol size and composition as inputs to the optical properties calculations showed that using a low-resolution size bin representation (four bins) underestimates the efficiency with which aerosols scatter and absorb light (mass extinction efficiency). Besides using finer-resolution size bins (8–16 bins), it was also necessary to increase the refractive indices and hygroscopicity of select aerosol species within the range of values reported in the literature to achieve better consistency with measured values of the mass extinction efficiency (6.7 m2 g−1 observed average) and light-scattering enhancement factor (f(RH)) due to aerosol hygroscopic growth (2.2 observed average). Furthermore, an evaluation of the optical properties obtained using modeled aerosol properties revealed the inability of sectional and modal aerosol representations in WRF-Chem to properly reproduce the observed size distribution, with the models displaying a much wider accumulation mode. Other model deficiencies included an underestimate of organic aerosol density (1.0 g cm−3 in the model vs. observed average of 1.5 g cm−3) and an overprediction of the fractional contribution of submicron inorganic aerosols other than sulfate, ammonium, nitrate, chloride, and sodium corresponding to mostly dust (17 %–28 % modeled vs. 12 % estimated from observations). These results illustrate the complexity of achieving an accurate model representation of optical properties and provide potential solutions that are relevant to multiple disciplines and applications such as air quality forecasts, health impact assessments, climate projections, solar power forecasts, and aerosol data assimilation.

Improving the Sectional MOSAIC Aerosol models of WRF-Chem with the revised Gridpoint Statistical Interpolation System and multi-wavelength aerosol optical measurements: DAO-K experiment 2019 at Kashi, near the Taklamakan Desert, northwestern China

“…The DA system improved the AOD at the price of deteriorating the data quality of surface particle concentrations, opposite to the result when assimilating PMx. Surface particle overestimations have been reported in previous studies (Liu et al, 2011;Ma et al, 2020;Saide et al, 2020). Ma et al (2020) assimilated ground-based lidars and PM2.5 simultaneously in eastern China using the WRF-Chem/DART (Data Assimilation Research Testbed).…”

“…They assimilated multi-source AOD data with the MOSAIC aerosols over continental United States and found that incorporating multiwavelength fine-mode AOD redistributed the aerosols' particulate mass concentration sizes. The revised GSI system assimilated Korean ground-based and geostationary satellite AOD datasets to improve local aerosol simulations (Saide et al, 2014(Saide et al, , 2020. Pang et al (2020) developed the official GSI to work with the Modal Aerosol Dynamics Model for Europe with the Secondary Organic Aerosol Model (MADE/SORGAM) aerosols in WRF-Chem.…”

Improving the sectional Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) aerosols of the Weather Research and Forecasting-Chemistry (WRF-Chem) model with the revised Gridpoint Statistical Interpolation system and multi-wavelength aerosol optical measurements: the dust aerosol observation campaign at Kashi, near the Taklimakan Desert, northwestern China

“…The revised GSI DA system is based on the official GSI (https://dtcenter.org/community-code/ gridpoint-statistical-interpolation-gsi, last access: March 2021, Wu et al, 2002;Liu et al, 2011;Schwartz et al, 2012;Pagowski et al, 2014) version 3.7. The 3D-Var DA minimizes the cost function:…”

“…The tangent linear operator and adjoint operator for AOD were determined using the Community Radiative Transfer Model (CRTM). The official GSI version incorporated the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD in East Asia (Liu et al, 2011) and revealed the simultaneous DA effects of PM 2.5 and AOD in the continental United States (Schwartz et al, 2012). This GSI identified DA effects that weakened during the succeeding model's running as the model error grew (Jiang et al, 2013) and assessed the radiative forcing of the aerosols released by wildfires (Chen et al, 2014).…”

Sensitivity of aerosol optical properties to the aerosol size distribution over central Europe and the Mediterranean Basin using the WRF-Chem v.3.9.1.1 coupled model

“…Therefore, the droplet number will affect both the droplet mean radius and the cloud optical depth calculated by the model. The aerosol size distribution represents the number (N), mass (M), or volume (V ) of particles as a function of diameter (d; Seinfeld and Pandis, 2006). Commonly, the aerosol size distribution is represented as a function of the logarithm of the diameter.…”