The WRF model provided meteorological data with 15 km × 15 km horizontal grid spacing and 26 vertical layers extending up to 50 hPa. To obtain highly resolved terrestrial input data, the topography height from the NASA Shuttle Radar Topography Mission (SRTM) 3 arcsec database (http://dds.cr.usgs.gov/srtm/version2_1/SRTM3) and the land use information provided by Environmental Geographic Information Service (EGIS; http://egis.me.go.kr) were used. Initial and boundary meteorological conditions for the WRF simulation were provided by the National Centers for Environmental Protection (NCEP) final operational global tropospheric analyses (http://rda.ucar.edu/datasets/ds083.2). To improve 3-D temperature, winds and water vapor mixing, objective analysis was employed by incorporating the NCEP ADP Global surface and upper air observation data. The meteorological fields were provided with 1 h temporal resolution and were then converted into the input fields for the CMAQ model simulations by the Meteorology–Chemistry Interface Processor (MCIP; v4.1) (Otte and Pleim, 2010).

The CMAQ model is a chemistry-transport model that simulates the chemical fates and transport of gaseous and particulate pollutants. In this study, the CMAQ modeling covered Northeast Asia, from 92 to 149∘ E and 17 to 48∘ N, using 15 km × 15 km horizontal grid spacing (Fig. 2) with 14 terrain following σ coordinates, from 1000 to 94 hPa. The configurations of the WRF model and CMAQ simulation used in this study are described in Table 1.

Anthropogenic emission inputs were processed by Sparse Matrix Operator Kernel Emissions in Asia (SMOKE-Asia; v1.2.1), which has been developed for processing anthropogenic emissions for Asia. Details of SMOKE-Asia were described in Woo et al. (2012). Biogenic emissions were prepared using the Model of Emission of Gases and Aerosol from Nature (MEGAN; v2.0.4) (Guenther et al., 2006) with the MODIS-derived leaf area index (Myneni et al., 2002), MODIS land-cover data sets (Friedl et al., 2002), and the meteorological input data described above. For the consideration of biomass burning emissions, daily fire estimates provided by Fire Inventory from NCAR (FINN) were used (Wiedinmyer et al., 2011). Asian mineral dust emissions were not considered in this study. Thus, the periods for model evaluation were selected during periods when mineral dust events did not take place.

To take full advantage of the AOD data sets intensively measured during the Distributed Regional Aerosol Gridded Observation Network in Asia (DRAGON-Asia) campaign, modeling episodes were chosen for the campaign period from 1 March to 31 May 2012. First, background CMAQ model simulations were conducted for the 3-month DRAGON period with 10-day spin-up modeling. After this, initial conditions were prepared using the STK method, observation operators and CVs via the combination of GOCI AODs with the background modeling AOD. Analysis was carried out for 12 h from 12:00 in local time (LT) on 10 selected high PM pollution days. The hindcast hours are referred to as H + 0 to H + 12. In this study we paid more attention to the performance of the first 12 h PM10 hindcast results; the analysis of the hindcast results after 13 h is also discussed briefly in Sect. 3.3.

In the hindcast analysis, different hindcast runs with 12 combinations of different observation operators and CVs were conducted, as discussed in Sect. 2.4 and 2.5. We selected one episode from March (28 March), five episodes from April (8, 9, 14, 17, and 23 April), and four episodes from May (6, 13, 15, and 16 May), all in 2012, for the analysis associated with three criteria of (i) on the selected days the average PM10 from 12:00 to 18:00 LT was above 70 µg m-3 over SMA; (ii) on the selected days, the daily coverage of the GOCI AOD data was at least 20 % over the GOCI domain; and (iii) on the selected days, dust events were not recorded over South Korea according to the Korea Meteorological Administration (KMA). Additional hindcast runs were also conducted from 7 March 12:00 to 19 March 11:00 LT for evaluating the performances of the hindcast runs for less-polluted episodes. In this study, we focused on SMA because we were particularly interested in this area. However, the system introduced here can be applied to other areas inside the GOCI domain where surface PM observation data are available.

Kriging is a geostatistical interpolation method to estimate unmeasured variables and their uncertainties, using correlation structure of measured variables. An atmospheric application study of the kriging method to estimating PM10 exceedance days over Europe reported that ST kriging showed comparable performances to those of the EnKF (ensemble Kalman filter) approach (Denby et al., 2008).

In this study, the STK method was used to fill out the missing pixels (Fig. 3d) with the spatial and temporal GOCI AOD data. The AOD fields produced by ST kriging can be prepared with a horizontal resolution of 15 km × 15 km from 10:00 to 16:00 LT over the GOCI domain. In this study, the AOD data at 12:00 LT (H + 0) during the selected episode days were used for preparing the initial conditions. The details and general application of the STK method are presented in Appendix A. One advantage of using ST kriging in this study framework is to use large numbers of observational data (GOCI AODs), compared with other methods. In fact, the GOCI AOD data are densely available temporally (with 1 h intervals) and spatially (compared with MODIS AODs; see Fig. 3a and b). This was the primary reason for using the STK method in this study. For example, when initial AOD fields were prepared at a certain time (e.g., at noon, 12:00 LT: H + 0), the STK method used not only GOCI AOD data at 11:30 LT or 12:30 LT but also GOCI AOD data at 09:30, 10:30, and 13:30 LT, unlike other methods. In the case of 4 April 2012 (a high PM pollution episode during the DRAGON-Asia campaign), other interpolation methods (e.g., Cressman, bilinear, and nearest-neighbor methods) could use only the GOCI AOD data of ∼ 88 000 for the preparation of the initial AOD field at 12:00 LT, whereas the STK method used the GOCI AOD data of ∼ 280 000 (3 times more AOD data). Sequential data assimilation (DA) methods such as OI (optimal interpolation) and 3DVAR (three-dimensional variational data assimilation) can use the same number of observations as the STK method. However, they required four data assimilation steps (i.e., 4 h time window for DA) (Tang et al., 2015) to include observations from 09:30 to 13:30, thus greatly increasing the computational cost for daily assimilation.

If the observation data are densely available and the differences between the observations and model-simulated data are large (i.e., the model simulations include relatively large errors and biases), there is less “practical need” to use the CTM-simulated data in the process of data assimilation. That is, it would be more desirable if the values of the unobserved (missing) pixels could be filled in based on “more reliable” observation data (here, GOCI AODs). This would be particularly true, when the CTM-predicted AODs are systematically underestimated compared with GOCI or AERONET AODs (as will be shown in Fig. 5a). Additionally, computation costs of the STK method are so low that the STK AOD can be calculated rapidly. For example, the 1-day process for preparing the AOD fields over the GOCI domain takes only ∼ 20 min with two 3.47 GHz Xeon X5690 six-core processors and 32 GB of memory in the current application of the STK method. Thus, it can be applied directly to the daily CWF due to the relatively cheap computation cost. Again, computation time (rapid calculation) is a central issue in daily (short-term) chemical weather forecasts. The calculation of daily three-dimensional semivariograms takes most of the computation time (regarding the details of calculation of the daily three-dimensional semivariogram, refer to Appendix A and Fig. A1).

Connected with these discussions, in the application of the STK method to the GOCI AODs, the “optimal number” of observation data is necessary to balance the accuracy of the data and the computational speed. From many sensitivity tests (not shown here), the optimal number of observations for most missing (white) pixels is approximately 100. That is, the use of more observation data above this optimum number does not meaningfully enhance the accuracy of AODs of the missing pixels but simply takes more computation time. This number of observation data is usually available for most of the missing (white) pixels of the GOCI scenes from nearby grids both/either at the concurrent scene spatially within ∼ 100 km and/or at the temporally close snapshots within 3 h. Based on these reasons, the STK method was chosen for this study.

An observation operator (or forward operator) describes the relation between observation data and model parameters. For example, the observation operator in this study converts the aerosol composition into AODs (and vice versa). Based on the aerosol composition and the relative humidity (RH) from the model simulations, simulated AODs at a wavelength of 550 nm (τCMAQ) were calculated with the following observation operator: τCMAQ=∑s=1N∑l=1Mαs,dryfs(RHl)[C]s,lHl, where N and M denote the number of aerosol species (s) and model layer (l), respectively, αs,dry the mass extinction efficiency (MEE) of the species (s) at 550 nm under the dry condition, fs(RHl) the hygroscopic enhancement factor for the species (s) as a function of RH at the layer of l, [C]s,l the mass concentration of the species (s) at the layer of l, and Hl the height of layer l. Here, [C]s,l is selected as the control variable (refer to Sect. 2.5).

In this study, three observation operators were used for calculating AODs and updating initial PM composition for the hindcast studies. The differences in the observation operators are caused mainly by the differences in αs,dry and fs(RHl) of Eq. (1). The first observation operator was selected from the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model (Chin et al., 2002; hereafter GOCART operator). Hygroscopic growth rates for SO42-, OC, BC, and sea-salt aerosols were considered separately in this operator. The second observation operator was from the GEOS-Chem model (the GEOS-Chem operator). The detailed aerosol speciation and MEE values were described in Martin et al. (2003). The final observation operator is based on the study of Malm and Hand (2007) (the IMPROVE operator). This observation operator was based on the reconstruction method with the MEEs and hygroscopic enhancement factors at 550 nm for different types of aerosol species. Table 2 summarizes the characteristics of the three observation operators chosen in this study. To consistently consider the characteristics of the three observation operators, aerosol types (s in Eq. 1) were classified into seven groups: SO42-, NO3-, NH4+, OAs, BC, sea salt, and others, which mainly consist of PM2.5 trace elements (Reff et al., 2009). In the classification, internal mixing states of SO42-, NO3-, and NH4+ were assumed. It should also be noted that the consideration of NO3- is important to correctly estimate AOD and aerosol mass loading in East Asia (R. S. Park et al., 2011, 2014; Song et al., 2008). Figure 4 shows the wet MEE values (αs,wet; product of αs,dry and fs(RHl) in Eq. 1) calculated for SO42-, NO3-, and NH4+, OAs, BC and sea salt at a wavelength of 550 nm as a function of RH, indicating that the three different operators can create large differences in the wet MEE values.

To prepare the distributions of the aerosol composition, the STK AOD fields should be converted into the 3-D aerosol composition. To do this, the differences between the STK AODs and background AODs (often called “observational increments”: ΔAODk = AODSTK,k - AODbg,k; k: grid cell) should be added to the background model-derived aerosol composition at each grid cell in connection with the observation operators (Eq. 1). Which aerosol species is/are selected for allocating ΔAODk? We selected four types of control variables (CVs) of particulate species. First, all particulate species were selected as CVs. In this case, ΔAODk was distributed to all particulate species, with the particulate fractions calculated from the background CMAQ model simulations. The second CV was the selection of SO42- concentration. Despite the large contribution of SO42- to both AOD and PM concentration in East Asia, model-estimated SO42- concentrations have shown large systematic underestimations, compared with observed SO42- concentrations (R. S. Park et al., 2011, 2014). This can be related to either (or both) the uncertainty in SO2 emissions in East Asia or (and) the uncertainty in the parameterizations of SO42- production in the CTM models (Kim et al., 2013; Lu et al., 2010; Smith et al., 2011; R. S. Park et al., 2014). In addition, there is also large uncertainty in the levels of hydroxyl radicals (OH) due to uncertain daytime HONO chemistry, OH reactivation, in-plume processes and others (Archibald et al., 2010; Han et al., 2015; Karamchandani et al., 2000; Kim et al., 2009; Kubistin et al., 2010; Lelieveld et al., 2008; Song et al., 2003, 2010; Sörgel et al., 2011; Stemmler et al., 2006; Zhou et al., 2011). Obviously, these uncertainties can influence the levels of H2SO4 and thus particulate sulfate concentrations in the atmosphere. In this case, aerosol mass concentrations (except for SO42-) were the same as those of the background aerosol concentrations. Third, SO42- and OAs were chosen to be changed. Although OAs are one of the major particulate species, it is well known that OA concentrations are also systematically underestimated due to two reasons: (i) the uncertainty in the parameterizations of the secondary OA formation (Donahue et al., 2006, 2011; Dzepina et al., 2009; Hodzic et al., 2010; Matsui et al., 2014; Slowik et al., 2010), and (ii) the uncertainty in emission inventories for anthropogenic and biogenic OA precursors (Guenther et al., 1999; Han et al., 2013; Sakulyanontvittaya et al., 2008; Tsimpidi et al., 2010; Wyat Appel et al., 2008). In this case, the mass concentration of surface OAs is assumed to be equal to the mass concentration of surface SO42-, based on the ground-based measurement studies over East Asia (Lee et al., 2009; Zhang et al., 2007, 2012). Thus, ΔAODk accounted for the increments of concentrations from OAs and SO42-, which are changed independently from the background concentrations. Finally, SO42-, NO3-, NH4+, and OAs were selected to be changed. In this case, ΔAODk was distributed to the four species selected, with the fractions of SO42-, NO3-, and NH4+ calculated from background simulations. The method to change the OA concentration in the fourth selection of CVs was the same as the method in the third selection of CVs. The fourth selection of CVs was also made to consider the thermodynamic balance among SO42-, NO3-, and NH4+ concentrations (Bassett and Seinfeld, 1983; Saxena et al., 1986; Seinfeld and Pandis, 2012; Song and Carmichael, 1999; Stelson et al., 1984). It should be noted that background modeling-derived vertical profiles and the size distributions of aerosol species were used for converting 2-D AOD to 3-D PM composition in all the STK cases. With the combinations of the three different observation operators and four choices of CVs (Table 3), 12 hindcast runs were made for high PM episodes during the DRAGON-Asia campaign.

Figure 5a–c show scatter plot analyses of background CMAQ-simulated AODs, spatial kriging AODs (i.e., kriging only with the GOCI AODs from one scene) and STK AODs vs. AERONET level 2 AODs over the GOCI domain during the DRAGON-Asia campaign. First, it can be found that the CMAQ-predicted AODs are underestimated significantly compared with the AERONET AODs. As discussed in Sect. 2.3, this was the main reason that we used the STK method in this study. More weight should be given to observations because the CTM modeling produces significant biases. Second, STK AODs show improved correlations, compared with the AODs estimated via the spatial kriging method. Also, the STK AOD data show equivalent levels of errors and biases, compared with GOCI AOD data. If one compares Fig. 3b with Fig. 5c, it can be seen that the ST kriging can effectively produce the AOD fields (also note the increase in N).

Figure 5d and e show the scatter plot analysis of the STK AOD products versus the AERONET AOD data with kriging variances (KVs). It is found that the STK AOD data with KV ≤ 0.04 show a similar scattering pattern and accuracy to those of GOCI AOD. In contrast, some overestimated outliers from the STK AOD data in Fig. 5e (e.g., 1.0–2.0 in the x axis and 2.0–4.0 in the y axis) show different patterns than those from the GOCI AOD data. This may be explained by the relatively large KVs (> 0.04) of such overestimated outliers. The KV generally increases when the observations near a certain prediction point are not available or when nearby observations have relatively large errors. Thus, when the GOCI observations are contaminated by optically thin clouds and they are not removed perfectly, this can increase the local variances due to their high cloud optical depth (COD). These factors can affect the quality of the STK AOD products. In this study, only the STK AOD products having small KVs (less than 0.04) were used for preparing the initial condition of each data processing step. Therefore, the initial PM concentrations did not changed where the STK AOD having large KVs (larger than 0.04; i.e. the right-bottom corner area in Fig. A2f). Collectively, it appears that the STK method is a reasonable tool for obtaining realistic AOD values at locations where the GOCI observations are not available.

To investigate the best combination of the observation operators and CVs, the AOD and PM10 hindcast runs and sensitivity analyses with the 12 different configurations (Table 3) were performed. For this, the hindcast AOD and PM10 from 13:00 to 19:00 LT (H + 1 to H + 6) on 10 selected episode days were compared with the ground-measured AOD and surface PM10. The observations from the six AERONET sites and nearest NAMIS PM10 stations within 10 km from the AERONET locations were selected for this comparison study (Fig. 2). The AOD values for the background CMAQ model simulations without the application of the STK method (noSTK) were also calculated with the GEOS-Chem observation operator.

Figure 6 shows the soccer plot analysis of the 13 hindcast AODs (left panel) and PM10 (right panel) during the first 6 h of the short-term PM hindcasting on the 10 selected episode days. In the soccer plot, mean fractional bias (MFB) and mean fractional error (MFE) (described in Appendix B) are plotted on the x and y axes, respectively. Using this plot, the relative discrepancy can be presented by the distances from the origin of the plot and particular characteristics, such as systematic bias, can also be shown as a group of scatter points. Detailed statistical metric values are shown in Table 4. All the AODs and PM10 with the application of the STK method are much better than those from the noSTK simulation, with reduced errors and biases. Percentage decreases in MFE with the STK hindcasts were found to be 60–67 % for AOD and are 50–63 % for PM10. The MFB also decreased by 67–82 % for AOD and by 56–84 % for PM10. The noSTK case showed a strong negative bias (i.e., underprediction) and the 12 STK cases also showed less, yet still negative, biases. These negative biases are considered to be systematic, because of the negative bias of the GOCI AOD data (Fig. 6). Additionally, the negative biases are due to underestimation of CMAQ-simulated SO42- and OA concentrations (Carlton et al., 2008, 2010; R. S. Park et al., 2011, 2014). This issue has been discussed in Sect. 2.5 and is investigated further in Sect. 3.4.

On the other hand, there are relatively small differences in errors and biases among the 12 STK cases (Fig. 6). Several differences among the 12 sensitivity cases were investigated further. First, the error and bias patterns for the AOD values were different from those for the PM10 predictions, being associated with the different observation operators. For example, the STK cases with the IMPROVE observation operator (cases C1, C2, C3, and C4) exhibited a relatively small bias for PM10 predictions, although they did not in the AOD predictions. This was likely caused by small wet MEE values of SO42-, NO3-, and NH4+ in the IMPROVE observation operator (represented by the green line in Fig. 4). In Eq. (1), the concentrations of converted aerosol species are inversely proportional to the MEEs of aerosol species. In the CV cases, the selections of SO42- and OAs (i.e., A3, B3, and C3) and SO42-, NO3-, NH4+, and OAs (i.e., A4, B4, and C4) showed better performances for both the AOD and PM10 predictions.

To show the degree of enhanced performances via the STK GOCI data, we also carried out some hindcast simulations, using the initial conditions prepared with single-frame GOCI data at 11:30 LT. The grids that did not have AOD observations were not filled out in this runs. In Fig. 6, the MFBs and MFEs of the bilinear interpolation method (denoted as BL) were -45.05 and 59.52 for AOD and -46.13 and 53.30 for PM10, respectively. It is shown in Fig. 6 that the use of the single-frame GOCI data without filling any gap cannot sufficiently improve the performance, compared with the cases of the STK simulations.

Figure 7 shows the performances of the short-term hindcast system with the 13 different configurations via comparisons between the hourly averaged PM10 observations and model PM10 predictions at the six NAMIS sites, on 9 April and 6 and 16 May 2012, respectively. Only 3-day and six-site results were selected and presented here, and more comprehensive performance evaluations are presented in Sect. 3.3. While noSTK failed to reproduce the high PM pollution, all the STK cases showed significant improvements in the surface PM10 predictions. However, there was a tendency in that the hourly peaks of PM10 were not well captured by the STK cases.

Consequently, it can be concluded that the combination of the GOCART observation operator and CVs of SO42- and OAs (represented by A3) leads to the best results in the current hindcast system (Table 4). The use of the GOCART observation operator and CVs of SO42-, NO3-, NH4+, and OAs (represented by A4) could also provide a comparable performance to A3. However, it appears that the differences among the 12 STK cases were relatively small.

In this section, PM10 observations from the hindcast experiments were compared with the PM10 observations from 58 NAMIS sites to evaluate the overall performance of the current hindcast system in SMA. Table 5 provides the statistical metrics that were calculated separately from the first and the second 6 h hindcast results. The main characteristics of the statistical analysis in Table 5 are similar to those at the six sites discussed in the previous section. First, both errors and biases of PM10 distributions were significantly reduced after the application of the STK method. The MFEs and MFBs in the 12 h STK simulations decreased by ∼ 40 and ∼ 80 %, respectively.

A distinctive difference was also found in the model performances for the first and the second 6 h runs. During the first 6 h, all the hindcast results showed negative biases, with the MFB of ∼ -100 % for the noSTK cases and ∼ -40 % for the STK cases. The performances of the A3 and A4 cases are somewhat better than those of the other STK cases (Table 5). Collectively, the MFEs and MFBs of the STK cases are a factor of 2–4 smaller than those of the noSTK cases during the first 6 h.

Figure 8 shows a comparison between the noSTK case and the A3 case, in terms of the PM10 predictions, during the first and the next 6 h in SMA with the 6 h averaged NAMIS PM10 observations. As shown, the A3 case produced better PM10 predictions during the first and the next 6 h. In addition, the A4 case (not shown) also provided similar results to the A3 case, as discussed in Sect. 3.2. It can be confirmed again that the A3 and A4 cases are able to produce better PM10 predictions against the PM10 observations in SMA.

Hindcast performances from H + 13 to H + 24 were also evaluated with the ground-measured NAMIS PM10 data. In short, the differences between all the STK and noSTK cases became smaller than those during the first 12 h (an approximate difference of 10 % was found at H + 24, i.e., 24 h after the hindcast actually began). Based on this, it appears that the effects of using the initial PM composition on the hindcast performances may effectively last during the first 12 h. After 12 h, the effects started to diminish. This is due to several facts: (i) the regions for applying the initial PM composition in this study were limited only within the GOCI domain (relatively small region); (ii) although the initial PM composition was used, its effects can be offset by uncertainties and errors in emissions as time progressed; and (iii) the large uncertainties associated with the formation of SO42- and OAs in the CTMs can also limit the effects of the initial PM composition. The latter two are the reasons that there is strong necessity for both emissions and CTMs to be improved continuously, even though the initial PM composition is applied in the short-term forecast activities.

In the previous section, PM10 mass concentrations were simply predicted by the short-term hindcast system with 12 different combinations of observation operators and CVs. Although the purpose of this study is to develop a better PM forecast system for accurately predicting PM10 mass concentrations, it is still necessary to more carefully scrutinize the changes in the PM composition in accordance with the different selections of the CVs.

During the DRAGON-Asia campaign, the PM2.5 composition was measured for SO42-, NO3-, and NH4+ with 30 min intervals and for SO42-, NO3-, NH4+, OC and BC with 24 h intervals using the PILS-IC instrument (semicontinuous measurements) and low-air-volume sampler with a Teflon filter (offline measurements), respectively, in Yongin City near SMA (Fig. 2). Thus, in this section, the selection of the CVs is further discussed with the observed PM2.5 composition.

Figure 9 shows the comparison between 1 h averaged SO42-, NO3-, and NH4+ concentrations measured via the PILS-IC instrument and model-predicted concentrations during the selected days at the Yongin observation site. Only the STK cases with the GOCART observation operator (i.e., A1, A2, A3, and A4) were selected here. The STK cases showed significant changes in the PM composition with the selection of CVs. For example, the A2 and A3 cases tended to overestimate the SO42- concentrations but underestimated the NO3- and NH4+ concentrations, whereas the A1 and A4 cases tended to capture relatively well the trend of the concentrations of the three particulate species. This phenomenon was driven by intraparticulate thermodynamics. That is, if larger amounts of SO42- are allocated into particles (like the cases of A2 and A3), then NO3- tends to be evaporated, because SO42- is more strongly associated with NH4+ (Song and Carmichael, 1999). As shown in Fig. 9a and b, when the SO42- concentrations increase (as in case A2), the NO3- concentrations decrease accordingly, because NO3- is evaporated out of the particulate phase as a form of HNO3 (Song and Carmichael, 1999, 2001). Collectively, the “best” results were produced from the case A4, as shown in Fig. 9a–c.

The 24 h averaged PM2.5 compositions measured from the PILS-IC instrument and the low-air-volume sampler with a Teflon filter during the campaign period are also compared in Fig. 9d. Again, the observations of the SO42-, NO3-, and NH4+ concentrations were obtained from both the PILS-IC instrument and the low-volume sampler, whereas the concentrations of OAs (≅ [OC] × 1.5) and EC were only measured via the low-air-volume sampler. As shown in Fig. 9d, the SO42-, NO3-, and NH4+ concentrations from both samplers showed good agreements (see circles and crosses in Fig. 9d). The A4 case (the red bars in Fig. 9d) again showed the best results in the comparison between the observed and predicted particulate composition, particularly in SO42- and OAs. In the previous discussion (see Sect. 3.2 and 3.3), the A3 and A4 cases showed the best performances for predicting PM10 mass concentrations over SMA. This is somewhat consistent with our analysis in this section. However, in the case of A3, it can capture the PM mass behaviors (Sect. 3.3) but does not capture the changes in the PM composition well (this section). Based on this, it is concluded that the A4 case would be the best configuration for accurately predicting the PM composition as well as the PM mass. However, this PM composition analysis was conducted with observations from only one site (Yongin City) in this study. Thus, to reach a firmer conclusion, more intensive analyses with observations from multiple sites are required in future.

To further evaluate the performance of the short-term hindcast runs, 48 h hindcast simulations with the configuration of A4 were carried out from 7 to 19 March. The observations from the six AERONET sites and the nearest NAMIS stations were analyzed in this study.

The time series of the first and the second 24 h averaged PM10 at the six sites on 8, 10, and 11 March 2012 are presented in Fig. 10. Again, reduced errors and biases were shown in the A4 STK simulations, compared with the noSTK simulation for polluted episodes (panels a and b in Fig. 10) and for the less-polluted episode (panel c in Fig. 10). Percent decreases with MFEs of the first 24 h A4 STK hindcast were ∼ 40 % for AOD and ∼ 10 % for PM10, and those with MFBs were ∼ 40 % for AOD and ∼ 100 % for PM10. In addition, slight improvements in the horizontal distributions of AOD and PM10 were also found. This was indicated by the increases of correlation coefficients (refer to Table S1 in the Supplement). The second 24 h STK hindcasts also reduced the errors and biases for AOD and PM10, although the improvements in the spatial distributions were not shown clearly. More detailed statistical metrics are presented in the Supplement (Table S1).