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The area of interest ranges over South Korea from the Yellow Sea to the South Sea. We examined the effects of profiler data on predicted wind distribution in coastal areas (Fig. 1). The southwestern coastal area is expected to have a complicated wind distribution due to its complex coastline. This area was selected as the area of interest to determine the effects of data assimilation on the atmospheric model for a complex area.

Fig. 1. 
Map depicting the two horizontal WRF domains.

Two continuous days that showed a contrast in synoptic pressure were selected for the numerical simulation (Fig. 2). The weather on June 4 and 5, 2007 was typical for early summer before the arrival of the seasonal northward-propagating rain front. Both days were sunny but showed different distributions of synoptic pressure. The high pressure center was located in South Korea under the effect of the North Pacific high. Because of these synoptic conditions, the wide pressure contour caused the sky to be clear and generated local wind-like land-sea breezes or mountain-valley winds. On June 5, however, the dominant high pressure of June 4 was pushed in the southeastern direction by low pressure located north of the Korean Peninsula. Consequently, the pressure contour line narrowed and a strong synoptic wind developed as a result of the intensified pressure gradient force. The different wind features over the two days were described by Lee et al. (2009b) through enhanced surface and vertical observations.

Fig. 2. 
Surface weather chart on June 4 and June 5, 2007.

The numerical model used in this study was the Weather Research and Forecasting (WRF) model v3.0 (Skamarock, 2008), which employs Arakawa-C grid staggering and is comprised of fully compressible and non-hydrostatic equations. The domain area is shown in Fig. 1. The first domain is Northeast Asia centered on the Korean Peninsula, which has a grid resolution of 9 km. The initial and boundary data of Domain 2 were obtained through one-way nesting of Domain 1. The horizontal resolution and grid dimensions of Domain 2 were 3 km and 250 by 250, respectively, and both domains consisted of 35 vertical layers. The NCEP Final Analysis (FNL) data, taken at 6 hour intervals and a resolution of 1°×1°, were used for the initial and boundary conditions of Domain 1. Table 1 outlines the detailed model configuration.

We used the 3DVAR data assimilation technique, which considers the dynamic balance between variables, employs error statistics of the model and observations, and is more elaborate than interpolation of observations, nudging, and optimal interpolation (OI). The basic goal of any variational data assimilation system is to produce an optimal estimate of true atmospheric state at the time of analysis through the iterative solution of a prescribed cost function (Ide et al., 1997). This method can utilize radar echo and satellite surface radiative data in addition to directly-observed meteorological data (University Corporation for Atmospheric Research, https://www.meted.ucar.edu; Mike, 2001). The advantage of using the profiler is that the dynamic equation and error covariance between vertical layers can be examined vertically. The 3DVAR method for the WRF model follows the procedure explained by Barker et al. (2004).

The observation data were obtained from the profiler, radio sonde (hereafter called sonde), and AWS. The profiler utilizes a type of Doppler radar that can continuously observe vertical wind distribution with high temporal and spatial resolution. Therefore, these data are useful for detecting severe weather that occurs on a small horizontal scale over short time periods. The profiler can also be observed continuously at low cost because of its capability for remote detection. The Gunsan, Haenam, and Masan profiler sites near the South and Yellow Seas were selected to investigate wind variation over the southwestern coast of Korea (Fig. 3). Fig. 3 shows the sonde sites that were used for data assimilation and for comparisons with experimental results. The sonde data from Gwangyang is not typically reported to the World Meteorological Organization (WMO), and is a special observation (Lee et al., 2009b). The last data set includes AWS data from 33 meteorological observatories around the assimilated profiler and sonde, which were used to confirm the effects of data assimilation.

Fig. 3. 
Location of wind profiler (▲) and radio sonde (×) sites used in data assimilation. Black dot (●) indicates radio sonde sites used for comparison with model results.

An experiment to assess the effects of profiler data on the existing system of sonde data assimilation, namely OSE, was conducted. This experiment was called OBS-EXP, and included four cases: no data assimilation, profiler assimilation, sonde assimilation, and the assimilation of both sets (NO_DA, PF_DA, RS_DA, and AL_DA). The WRF-Var cycling mode (National Center for Atmospheric Research, 2008) was applied to OBS-EXP every six hours. The second experiment was a data assimilation interval experiment, INT-EXP, to evaluate the enhancement to model performance that occurs when using a high temporal resolution of 10 minutes. In addition to 6 hour intervals (6h_DA), 1 hour, 3 hour, and 12 hour interval experiments were carried out, called 1h_DA, 3h_DA, and 12h_DA. In INT-EXP, the sonde and profiler data was used and 6h_DA was the same as AL_DA in OBS-EXP. Table 2 lists all configurations.

Fig. 4 presents the results for OBS-EXP while inspecting changes in horizontal wind at 10 m. A sea breeze developed around all coastal areas of the Korean Peninsula because of the weak synoptic force at 1200 LST on June 4. Due to the sea breeze, the wind speed was greater along the coastal line than inland. At 1500 LST, June 4 (not shown), the sea breeze intensified and penetrated inland. In each case, there was a slight change in wind direction and a considerable change in wind speed. Remarkable differences were observed in the Yellow Sea and the Southern Sea, which showed weak (0-2 ms^-1) and strong (4-6 ms^-1) wind speeds, respectively. The atmosphere stabilized at night and weakened the wind speed, but a mountain wind occurred in the mountainous area. The wind speed over the southern sea was decreased by the data assimilations, and the distribution for PF_DA was similar to that for AL_DA. This explains why the results from AL_DA were primarily affected by the profilers at Haenam and Masan adjoining the Southern Sea. During the day of June 5, when the synoptic force was strong, a clockwise wind field appeared at the edge of the North Pacific high pressure region and prevailed over the entire domain. An approaching low pressure region at the northeast of the Korean Peninsula reinforced the pressure gradient force, so that the wind speed was stronger than on June 4 (4-7ms^-1). Moreover, the differences in wind speed distribution over the sea were much greater than they were inland because the profiler and sonde are located along the seashore.

Fig. 4. 
Horizontal wind distribution for OBS-EXP results ((a) NO_DA, (b) PF_DA, (c) RS_DA, and (d) AL_DA).

Fig. 5 shows the results from INT-EXP, which was designed to apply the time resolution of the profiler. Although the difference in wind direction caused by the synoptic effect was similar to the OBS-EXP results, the wind speed distributions were distinct for all cases of INT-EXP. The 1h_DA shows that the wind speed intensified over the southwest region of the Korean Peninsula with enhanced penetration of the sea breeze. The distribution of wind speed over the southern area in NO_DA (Fig. 5) was 4-6ms^-1, which weakened to 3-5ms^-1 for 1h_DA. These results show that the greatest change in wind speed is explained by the frequent use of profiler data. The differences in each case on June 5 were not significant because predominant synoptic forcing had little effect on data assimilation over time.

Fig. 5. 
Same as Fig. 4. but for INT-EXP results ((a) 1h_DA, (b) 3h_DA, (c) 6h_DA, and (d) 12h_DA).

Fig. 6 shows a comparison between the OBS-EXP results and the observations at Haenam (a) and Masan (b), which were the profiler observation points for data assimilation, and at Gwangyang (c) where data assimilation was not carried out. Because the observed values at the profiler sites were provided excellent vertical resolution, the changes in wind direction were more complex than the distribution of the wind velocity observed by the sonde. The results of the model did not simulate this rapidly changing wind speed, but the overall pattern was simulated accurately. In most results, AL_DA and PF_DA were similar to the observation profile. In lower level Haenam, wind speeds of<2ms^-1 were observed at 09 LST on June 4, but the NO_DA wind speed was considerably overestimated at 6 ms^-1. AL_DA estimated approximately 3 ms^-1, and when PF_DA was compared to RS_DA, PF_DA showed better results. In lower level Masan, the strong wind at 4ms^-1 was accurately simulated in PF_DA at 15 LST, June 5, and its effectiveness had a significant influence on AL_DA. In the region of Gwangyang, which is located in the middle of Masan and Haenam (Fig. 3), results were greatly affected by profilers in the lower level. This is because the profiler concentration in the lower level meant that the results of AL_DA did not reflect those of RS_DA. Therefore, the profilers had a significant impact on the results for wind distribution in the lower level. This study did not compare the potential temperature and specific humidity with observations. PF_DA shows better results in the simulation of specific humidity than NO_DA, even though the profiler has no data associated with water vapor. This suggests that water vapor advection is affected by the change in the lower level wind, and the degree of simulated specific humidity agrees with the observations.

Fig. 6. 
Calculated (OBS-EXP; solid line) and observed (dashed line) vertical distribution of wind speed ((a) Haenam, (b) Masan, and (c) Gwangyang).

The results for INT-EXP were compared with vertical observation data (Fig. 7). Comparing the results at profiler sites, better results were found when profiler data were used frequently. Most of the results for all times and all sites agree with the observations of 1h_DA except for the overestimation of wind speed that was observed at the upper level in Masan at 15 LST, June 5. INT-EXP increased the wind speed at the level where it was underestimated, and reduced it where it was overestimated. The differences in each case were remarkable below the 1 km level because the profiler produced detailed wind information in the lower layer. The effect of the time interval is not observed when simulating the strong wind speed at the lower levels on June 5. A comparison of the INT-EXP results with the sonde in Gwangyang, which was not used in data assimilation (Fig. 7(c)), revealed the results to be affected by two profiler sites, Haenam and Masan. A slow wind speed due to a weak synoptic effect at 09 LST, June 4 was accurately simulated in 1h_DA, and a high wind speed of 8ms^-1 on the night of June 5 with a strong synoptic effect also agreed with observations. 1h_DA showed the best results not only for wind speed but also for specific humidity (not shown).

Fig. 7. 
Same as Fig. 6. but for INT-EXP.

The diurnal variations in wind speed between model results and site observations where data assimilation was conducted on June 4 and 5 were compared (Fig. 8). The diurnal variations, which were strong winds during the day and weak winds at night, and the wind speeds on June 5 were stronger than those on June 4. Masan (a) and Gwangju (b) in Fig. 8, which are the results from OBS-EXP, show that the surface wind speed decreased at night because the atmosphere became stable. The overestimation of wind speed at dawn by NO_DA at (a) Masan decreased significantly with data assimilation. The maximum wind speed of 4.6 ms^-1 at Gwangju on June 5 was overestimated at approximately 6 ms^-1 by NO_DA, but improved to 5.0 ms^-1 by AL_DA. In addition, the wind speed variation results of AL_DA were most similar to the observations. The trend in wind speed changes seen in the AL_DA results was similar to that of PF_DA because the effect of the profiler data was strongly reflected in the AL_DA results, particularly in comparison with the AWS ground data.

Fig. 8. 
Calculated and observed (shaded area) time variation of wind speed ((a) OBS-EXP at Masan, (b) OBS-EXP at Gwangju, (c) INT-EXP at Haenam, and (d) INT-EXP at Gwangju).

The INT-EXP results (Fig. 8(c), (d)) were significantly affected by the impact of synoptic forcing. The sensitivity of data assimilation with the time interval was much greater on June 4 when synoptic forcing was weak. The daytime wind speed in Haenam was similar to an observation near the 01h_DA, and the wind changes were consistent with observations. There was little difference in 03h_DA and 06h_DA for the slow wind speed at Haenam observed in the early morning, but the wind speed decreased greatly for 01h_DA. In addition, the wind speed variation during the day was accurately simulated by 01h_DA. Frequent data assimilation improved the wind speed results during the day and night in Masan. In particular, the sensitivity to the time interval was much better in Gwangju. On the other hand, the results of experiments on June 5 show that the differences in wind speed and diurnal variation with each time interval were reduced relative to those on June 4. The sensitivities on June 4 and 5 for INT-EXP were distinct because the synoptic effect was different. On June 4, when synoptic forcing was weak, local flow occurred at each observation point, and the wind direction and wind speed changes were observed as functions of time. This atmospheric phenomenon can be observed using a profiler with high time resolution, and the results of 03h_DA and 01h_DA, which reflected these benefits, were greatly improved and showed large differences for each case of INT-EXP. On June 5, which had a strong synoptic effect, there were no significant changes in the profiler observations because the winds exhibited little diurnal variation. Therefore, the sensitivity dependence on the time interval was insignificant.

According to wind energy forecasting and an assessment using improved wind fields in the boundary layer, this study focused on the results of wind speed simulations because wind direction does not seriously affect wind power. The simulated results for wind direction were compared with ground observations to determine whether the changes in wind direction due to local circulation were reflected accurately. Fig. 9 compares the results of each experiment and the wind directions determined through ground-based observatories on June 4 and 5. The direction of the land-sea breeze circulation on June 4 was slightly different due to the different shape of the coastline at each point, but apparent local circulation and changes in wind direction occurred. Variation in wind direction, which was affected by the impact of synoptic wind on the night of June 4, did not occur. The wind direction at Masan changed from north and northeast before sunrise to a southeast sea breeze during the day, and a southwest sea breeze occurred in Gwang-ju. In Haenam, the night easterly wind also changed to a westerly breeze during the day in the southwest coast of Haenam, and the land-sea breeze cycle was observed by the wind direction changes. The wind direction results of each experiment differed depending on the regions and experiments. In the results of Masan for NO_DA in OBS-EXP, significant improvements were not observed because the results were similar to the observations showing the local circulation on June 4 and the southwesterly synoptic wind on June 5. In Gwangju, the results of the OBS-EXP southwest winds that occurred in NO_DA during the day of June 4 exhibited an easterly wind. However, data assimilation was carried out for southwest winds and the simulated results, which changed over time, were-simulated accurately in the southwesterly direction. In the case of the numerical simulation with a 3 km horizontal resolution, the representations of the terrain and coastline were presented smoothly, which make it easy to represent the complex flow of the actual terrain without data assimilation. The simulated results under low resolution, however, increased when assimilating the observation data, which thereby detected the complex atmospheric flow. In particular, the differences in experimental results were greater for June 5 than for June 4 because of the synoptic effect. The results of AL_DA were similar to those of PF_DA because the addition of profiler data had a significant effect on ground outcomes. The reception rates of the profiler data in Haenam at low levels were higher than in other regions. Therefore, the results of 01h_DA in INT-EXP showed very good correlations with observations, and land-sea breeze circulations on June 4 were simulated accurately.

Fig. 9. 
Same as Fig. 8. but for wind direction.

The results in previous chapter were qualitatively analyzed by comparing the results of OBS-EXP and INT-EXP with vertical observation data and ground AWS data. This chapter quantitatively analyzes the results of each experiment using the statistical indices listed in Table 3.

The statistics for wind speed for observations at the 33 AWS points and the results of data assimilation were calculated. Table 4 summarizes the results of OBS-EXP. In all cases, OBS-EXP overestimated the mean wind speed and demonstrated a positive MB. In the case of AL_DA, the results of the wind simulation were improved for all statistics. In particular, the normalized mean error (NME) normalized by the average observed wind speed was 71% of the average wind speed in NO_DA but 61.04% in AL_DA, showing an approximate 10% decrease in error. The error reduction in AL_DA with the addition of profiler data was greater in PF_DA (61.37%) than in RS_DA (64.39%). In all other figures, the results of PF_DA were better than RS_DA because the wind information of the profiler was detailed in the low levels. This advantage was confirmed by a comparison with the ground AWS.

As the sensitivities on June 4 and 5 were different, the INT-EXP results were calculated statistically by dividing the weak and strong synoptic days. Table 5 shows the results of June 4 and 5 on the left and right, respectively. The results of 01h_DA were the best on the weak synoptic days. They still showed a positive bias, but in the 01h_DA results the mean wind speed decreased from 2.56 ms^-1 to 1.99 ms^-1, which is similar to observed values. Overall, the normalized mean error (NME) of the average wind speed during the day showed the greatest decrease of 28%. Therefore, the more frequently the profiler data were used, the more the simulation results were enhanced under weak synoptic conditions. The statistics on the strong synoptic days (Table 5, right) showed that the simulations provided better results at all time intervals than NO_DA, but did not show a significant effect on the results of 03h_DA or 01h_DA. The 06h_DA showed low or similar errors in all statistics except for the IOA and CORR. The differences between 06h_DA and 12h_DA led to a definite effect, but the use of data assimilation for intervals shorter than six hours had little or no effect. As explained earlier in the time series analysis, the profiler data did not affect the dominant wind direction and wind speed under strong synoptic conditions. The diurnal variations of the statistics averaged by space differed significantly according to the time intervals of data assimilation due to the synoptic effect (Fig. 9). The errors for 01h_DA during the day of June 4 were the lowest and highest in RMSE and IOA, respectively. However, the sensitivity of the time interval was reduced for the night of June 4, and the results from June 5 were not different between experiments.