## 3 Results
### Validation of AOD Estimation Methods
#### 3.2.1 Springtime  
Figure 4 displays the horizontal distribution of the observed and estimated AOD at 01:00 UTC on 31 May 2016, during the KORUS-AQ period. As shown in Figure 2, because the M1 method of Malm [11] with maximum RH values of 96% and 98% tends to overestimate AOD, we only present the results with a maximum RH value of 93%. In Figure 4a, the GOCI satellite image shows large missing areas in most cloud regions where synoptic lows have collapsed; however, there is high-AOD from eastern China to the mid-western coastal region of the Korean Peninsula, crossing the Yellow Sea. Although the estimated algorithms for AOD use the same model results, their diagnosed AOD patterns are very different, especially in the cloud region, regardless of the grid resolution. In the D01 analysis, M1 (Figure 4b) shows high AOD along the missing regions from a satellite image; in contrast, reduced values of AOD were output by the M2 and M3 methods (Figure 4c,d, respectively). Since most values from clouds are missing in satellite observations, no  
Figure 3: Time series of observed PM\({}_{2.5}\) in (**a**) the Seoul and (**b**) the Busan metropolitan areas and forecasted daily PM\({}_{2.5}\) from the 27 km resolution (red) and 9 km resolution (blue) operational forecasts of March 2016–February 2017.  
quantitative validation can be performed (Figure 4b-d). Thus, other validations using ground observations are discussed later using detailed statistics. From a 9 km resolution model around the Korean Peninsula (Figure 4e,f), more detailed distributions were simulated and compared with the coarse grid results. High AOD values are apparent in the Seoul metropolitan area, and a strong AOD gradient is evident in the northern area of the western inflow (Figure 4e). However, as shown in Figure 4f,g, the AOD is reduced in the central Korean Peninsula, and the northern edge of the strong gradient is also weaker than that of the M1 method. Statistical robustness was determined using long-term analysis of the time series and quantitative comparisons were carried out using ground observations (Figure 5). Figure 5 shows observed and estimated AOD values during March-May 2016. There are 11 AERONET stations in the Seoul metropolitan area; we selected the Yonsei-YSU AERONET station (marked as a large red dot in Figure 1b) because it has the fewest missing value during our analysis period. However, we also performed a comparative statistical analysis using multiple stations for the entire numerical domain (see Section 3.2.3). To the best of our knowledge, the model's ability to estimate AOD is usually investigated in large domains, and past studies have tested the method on a regional scale. The estimated AOD values from the WRF-CMAQ model were interpolated to the location of the observation station using Cressman interpolation. Both the observation and model results were averaged daily; Figure 5a,b represent the results from D01 and D02, respectively. From the 27 km grid  
Figure 4: (**a**) Spatial distribution of aerosol optical depth (AOD) obtained from the geostationary ocean color imager (GOCI) satellite at 01:00 UTC on 31 May 2016. Estimated AOD from 27 km resolution model results using (**b**) M1 with maximum relative humidity (RH) = 93%, (**c**) M2, and (**d**) M3. (**e**–**g**) Estimated AODs from the 9 km resolution model using the (**e**) M1, (**f**) M2, and (**g**) M3 method. (Figure 5a), all estimated AODs show similar temporal variation to the observed AOD, but their difference is significant when high peaks of AOD are simulated. The estimated AOD using the M1 method with a maximum RH of 96% was almost twice that for a maximum RH of 93%, offering evidence of uncertainty in this method. The finer-grid AOD also suffered from these overestimations (Figure 5b), but the M2 and M3 methods were more accurate than the M1 method in both D01 and D02, with the M3 estimation showing the smallest AOD among the algorithms.