###### Abstract  
Currently, significant efforts are being made to enhance the performance of the National Institute of Environmental Research (NIER) operational model. However, the model performance concerning Aerosol Optical Depth (AOD) estimation remains uninvestigated. In this study, three different estimation methods for AOD were implemented using the NIER operational model and validated with satellite and ground observations. In the widely used Interagency Monitoring of Protected Visual Environments (IMPROVE) method, AOD exponentially increases with relative humidity owing to a hygroscopic growth factor. However, alternative methods show better performance, since AOD estimation considers the size dependency of aerosol particles and is not sensitive to high relative humidity, which reduces the high AOD in areas with large cloud fractions. Although some R values are significantly low, especially for a single observational comparison and small numerical domain analysis, one of the alternative estimation methods achieves the best performance for diagnosing AOD in the East Asia region. a revised optical depth (AOD); weather research and forecasting community multi-scale air-quality (WRF-CMAQ); hygroscopic growth factor +
Footnote †: journal: Applied sciences  
0022212556  
from satellite data [5; 6; 7], but there are still limitations in stably identifying concentrations of air pollutants. For example, spatial and temporal resolutions may not be sufficient for operational needs, and many satellite images suffer from a high cloud fraction or high surface reflectance. In terms of high cloud fraction, baroclinic waves are most active in the mid-latitude spring or wintertime when severe pollutant episodes are frequent. Hence, complementary methods using numerical or statistical models to address missing data are essential. The simulation and forecasting of air quality using numerical models are common for obtaining accurate and continuous information on air pollution. Models' performances decrease with forecast or simulated time, and significant efforts are being made to increase the accuracy or extend predictability. These methods can be categorized into two approaches: (1) decreasing the uncertainty of model complexity, such as emissions, boundary conditions, and parameterization for atmospheric processes (e.g., [8; 9; 10]); and (2) increasing the accuracy of initial conditions using data assimilation techniques with ground and satellite observations (e.g., [11; 12; 13; 14]). However, while issues remain, several studies have accurately estimated AOD from air-quality models. This study aimed to analyze the performance of several AOD-estimation methods from the operational air quality model of the NIER. Although significant efforts are being made to enhance the performance of NIER's operational model, in this study, we focused on the ability and performance of AOD estimation based on past forecasting results. If the missing values of satellite observations are related to a high cloud fraction, they should have a significant dependency on the cloud hygroscopic growth factor in that region. Herein, the effects of f(RH) on aerosol mass concentration are discussed and different parametrization methods for f(RH) are compared with conventional methods. The results of this study reveal fundamental problems and provide suggested strategies for improved AOD estimation when using the operational WRF-CMAQ model in Korea. The Interagency Monitoring of Protected Visual Environments (IMPROVE) estimation method (or [PERSON]'s reconstructed method) [15] is widely used in the Weather Research and Forecasting (WRF)-Community Multi-Scale Air-quality (CMAQ) community. One of the main limitations of [PERSON]'s reconstructed method is that it cannot consider the size-dependent function of relative humidity (f(RH)), which is an important aspect of aerosol hygroscopicity [3; 16].