3.1. Vegetation Effect From the Fg Parameterizations
 Figure 3 shows Fg images of the study area derived from the methods discussed in section 2.3. As expected, the Fg in the BS case does not properly represent the spatial distribution of vegetation for the 1 km resolution. On the other hand, Fg derived from MODIS data seem to have much more detailed information about the vegetation spatial variability. Differences in contrast of the Fg distribution from west to east is observed in between the VEG1 and VEG2 cases. The Fg shows the west–east spatial distribution from about 0.35 to 0.75 in the BS case, about from 0.15 to 0.85 in the VEG1 case, and about 0.05 from 0.9 to in the VEG2 case. After the Fg parameterization, the Fg values in the western ISFF station sties (0.2 in VEG1 and 0.09 in VEG2 on average) were lower than the BS case (0.37 on average) while those in the eastern region were raised (0.71 in BS, 0.78 in VEG1, and 0.79 in VEG2 on average).
Figure 3. Fg images for BS, VEG1, and VEG2 cases in 1 km spatial resolution: (a) DY1 and WT of the BS case, (b) DY2 of the BS case, (c and e) DY1 of the VEG1 and VEG2 cases, respectively, and (d and f) for WT and DY2, respectively, of the VEG1 and VEG2 cases.
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 Figure 4 shows the temporal variations of simulated land surface variables and Table 3 provides their statistical comparisons to the ISFF observations with correlation coefficients (R square values from regression analyses) and Root Mean Square Errors (RMSE). Relatively low correlations were observed in GH and LH in the eastern area while the other areas showed good correlation with the observations.
Figure 4. Temporal variations of land surface variables simulated by the WRF/Noah model and their comparisons to the ISFF observations for the BS, VEG1, and VEG2 cases. Each point was averaged for the three ISFF sites in each track.
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Table 3. Correlation Coefficients and RMSE of Simulated Land Surface Variables to ISFF Observationsa
 As mentioned above, in order to test only the vegetation effect by Fg parameterization, the nine-point soil moisture initialization was conducted simply by replacing the soil moisture values of the nine-point grid cells in model initial inputs. The target domain configured in the model has 1 km × 1 km spatial resolution, and heterogeneity in spatial distribution, for instance, of soil moisture and vegetation is often observed in such a small area unit or even smaller ones. With this data replacement for the soil moisture initial condition, low atmospheric variation in each simulation period resulted in reasonable soil moisture simulations in the coupled WRF/Noah model. It should be noted that there was no observation of any substantial rainfall during each period, except from 5 to 20 mm of precipitation at the end of DY1. After the soil moisture initialization, the soil moisture initial conditions were lowered in the western and central areas and raised in the eastern area from the original ETA model data.
 The effects of the Fg parameter on TS simulation were observed mainly during DY2 in the eastern area, showing about a 5 K decrease of the diurnal peaks, while those of the other regions showed slight or no improvement. RMSE of TS simulations, however, did not show any significant difference among the cases. The average RMSE of TS simulations were about 3 K. The TS underestimation in the eastern area is mostly found in locations where soil moisture was around 0.38 m3 m−3. This does not indicate the soil moisture effect from the data replacement, but rather the vegetation effect from the Fg parameterizations. Increase in the Fg parameter caused lower TS. This is also supported by the HRLDAS test as described in section 3.2.
 While TS underestimations were observed, SH simulations showed significant improvement during WT and DY2 in the eastern area. The SH simulations agree very closely with the observations with about a 200 Wm−2 decrease during DY2 in that region. RMSE of the SH also supports this improvement, which was observed in both of the Fg parameterizations. In the eastern area, the RMSE of SH improved from about 90 Wm−2 to 40 Wm−2. SH values of the diurnal peaks in that region decreased by 100 Wm−2 during DY1, 120 Wm−2 during WT, and 200 Wm−2 during DY2, which are very close values to the SH observations. No substantial difference was observed between VEG1 and VEG2 because the Fg parameters were very similar in the eastern stations. On the other hand, SH during DY2 in the central track showed overestimations and increased by 100 Wm−2 from that of the BS case. RMSEs also increased by 30 Wm−2 in the VEG2 case. The other periods (DY1 and WT) in the central area showed no significant changes in SH simulations. Meanwhile, in the western area, the new Fg parameters were less influential and SH overestimation during DY1 of the VEG1 and VEG2 cases was interpreted as having a high sensitivity of the model to soil moisture variation in that region. This will be discussed further in section 3.2.
 The simulated GH flux did not show any vegetation effect in the highly vegetated areas. The low diurnal variations of the simulated GH in the eastern area were not improved either by the Fg parameterizations or soil moisture initialization. GH RMSEs in this region showed over 50 Wm−2, which is larger by about 20 Wm−2 compared with the other regions. Considering the range of the GH diurnal cycle (from about 80 Wm−2 to 200 Wm−2), this error is quite significant. The greatest difference between the observations and the simulations were up to 150 Wm−2. On the other hand, some simulation improvements especially in the VEG1 case were observed during WT and DY2 in the region with less vegetation (the western area).
 LH simulations were very sensitive to Fg in highly vegetated areas. Table 3 shows good correlations with the observations (the LH_budget case) in that area. Apart from the LH measurement error mentioned in the section 2.1, noticeable LH overestimations in that area were observed and the differences from the LH_budget observation was as much as 200 Wm−2. This LH overestimation has also been reported in previous studies [Chen et al., 2007; Hong et al., submitted manuscript, 2009] in which the studies used the Noah LSM implemented into HRLDAS and into the WRF model, respectively. Any substantial difference between the VEG1 and VEG2 cases was not observed in the eastern region, but the central (in all periods) and western areas (during DY2) show improved simulations in the VEG2 case. The error statistics through RMSEs of the LH also demonstrates this LH overestimation results. RMSE were observed to be better in the VEG1 (about 6 Wm−2 and 3.5 Wm−2 improvements in the western and central area, respectively) and VEG2 (about 7.5 Wm−2 and 6 Wm−2 improvements in the western and central area, respectively) cases in relatively low vegetated areas but worse in the eastern area (about 39 Wm−2 and 52 Wm−2 worsened in VEG1 and VEG2 cases, respectively) as verified with LH_budget observations. When compared with LH observations, the central area shows better results with a lower RMSE (about 39 Wm−2 improvements in the VEG2 case). The analyses of the temporal variations of the ET components, EDIR (direct evaporation from bare ground) and ETT (vegetation transpiration), were performed in order to understand the LH overestimations in the eastern area. EC generally occurs in a very short time, taking a very small portion of the total ET after precipitation. As seen from section 2.2, the model was configured to avoid any precipitation during the spin-up periods. It should be noted that we omitted analyzing EC in this result section. EC was a very small portion of our model simulations (less than 10 Wm−2 in average) and can be ignored for LH analyses. According to the result, the LH overestimation is mainly due to the overestimation of vegetation transpiration. LHs were overestimated in both VEG1 and VEG2 cases with about 250 Wm−2 more than those of the BS case. Even though BS is not a good reference for transpiration analyses, the case studies provide enough results to interpret model responses to vegetation parameters. With the HRLDAS case study, we present that the vegetation effect is more responsible for the LH overestimation than soil moisture variation. This issue will be discussed further in section 3.2.
3.2. Soil Moisture Effect From HRLDAS Initialization
 Figure 5 shows the soil moisture comparison between that derived from Eta and HRLDAS. As shown in section 3.1, the soil moisture in the BS case shows much less information about spatial variability than the one from HRLDAS, and the soil moisture spatial contrast from east to west is also obvious. The very low soil moisture area in the west (areas with very red color) corresponds to a sandy soil surface that generally has high hydraulic conductivity. As mentioned in section 2.4, the soil moisture initialization test of HRLDAS was performed in combination with the Fg parameterization (the quadric method).
Figure 5. Soil moisture images of the model input, interpolated for 1 km spatial resolution from (top) Eta model and (bottom) HLRDAS.
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 Figure 6 shows the temporal variation of the surface variables simulated by the coupled WRF/Noah model and Table 4 provides the statistical analyses of these data. Relatively good correlations with observations were observed for most variables except GH and LH in the eastern area, which is similar to the Fg cases. The low coefficient of LH simulations was improved when it was compared to the LH_budget (from 0.49 to 0.91). Simulated soil moisture variations by HRLDAS were improved in the western and central regions but showed almost no change in the eastern area. This soil moisture improvement did not show any significant effect on TS simulations in the coupled WRF/Noah model. The new Fg in the HRLDAS case, however, showed a very similar pattern to the TS diurnal cycle seen in the VEG2 cases. Moreover, the TS underestimations in the eastern area support the Fg effect as discussed in section 3.1. Meanwhile, the second TS peak values in the western area make possible an interesting interpretation about the model. In the soil moisture variations of the VEG2 and HRLDAS cases, HRLDAS showed higher soil moisture (0.13 m3 m−3) than in VEG2 (0.08 m3 m−3), but the second TS peak value during DY1 was higher in HRDAS (319 K) than that in VEG2 (315 K). This result of the TS increase in spite of soil moisture increase in lowly vegetated areas indicates a greater sensitivity of the model to the Fg parameter but not to soil moisture (Fg was 0.09 in the western station sites of VEG2 in average).
Figure 6. Temporal variations of land surface variables simulated by the WRF/Noah model and their comparisons to the ISFF observations for the BS and HRLDAS and VEG2 cases. Each point was averaged for the three ISFF sites in each track.
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Table 4. Correlation Coefficients and RMSE of Simulated Land Surface Variables to ISFF Observations for the HRLDAS Casea
 Unlike the improved SH simulations in the VEG1 and VEG2 cases for the eastern track, the ones of the HRLDAS case did not closely approach the observed diurnal cycle because soil moisture did not change in that region after the HRLDAS soil moisture initialization. This indicates that the SH simulation is affected not only by vegetation but also by soil moisture variation. While the central area showed similar results as in the VEG2 case, the western area indicated the model sensitivity to soil moisture variation as discussed in section 3.1. During the WT period in the western area, soil moisture did not display any quantifiable variability in all cases in this study. This resulted in little change of SH simulations in that period, indicating low sensitivity to the Fg parameter. On the other hand, while soil moisture was lowered to 0.1 m3 m−3 during DY1 in that region, the SH of the VEG2 case increased by about 100 Wm−2 in the first peak time of that period. A similar result was observed in the HRLDAS case (SH increased by 50 Wm−2), but the difference in the SH peak values between these two cases explains that the SH overestimation is caused by soil moisture variation in lowly vegetated areas. GH in the coupled model is not sensitive to soil moisture variation and demonstrate similar results to the VEG1 and VEG2 cases.
 LH overestimations from ETT overestimation were also observed in the eastern area in the HRLDAS case, indicating the new Fg effects on the model sensitivity. From the HRLDAS case study, however, we found that the soil moisture variation was also influential on the ETT overestimation. Given that there was no soil moisture change in the HRLDAS case, the ETT showed less overestimation (up to 550 Wm−2) than that found in the VEG1 or VEG2 cases (up to 630 Wm−2). Thus, the ETT difference between the HRLDAS and the VEG1 or VEG2 cases implies the effect of soil moisture variation on the model sensitivity. In the western area, both effects of vegetation and soil moisture on ETT simulations were observed. When Fg and soil moisture decreased during DY1, ETT also decreased. EDIR simulations also support the dual effects as demonstrated during the WT period in the western area. EDIR increased more (up to about 200 Wm−2) owing to the effect of the Fg parameter alone than it did when the effects of the Fg parameter and soil moisture decrease (up to about 160 Wm−2) were combined. The RMSE of the LH simulations in the HRLDAS case showed significant improvement in the central area more than other cases. Meanwhile, the LH simulations seem to demonstrate better results in the eastern area for the VEG1 or VEG2 cases (from the statistics with the LH_budget), but this did not lead to any improvement from the BS case (Table 3).