3. Impact of spectral nudging on real-time TC forecast
3.5. General effect of spectral nudging on TC forecast
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Figure 3.8 Time series of pattern correlation for 850 hPa zonal and meridional wind, and 500 hPa geopotential height for NOSN, OSN, and GFS runs with GFS analysis data for those cases where track forecast is improved.
The correlations of all variables tend to decrease with time. GFS (NOSN) has the highest (lowest) correlation with the GFS analysis for all forecast lead times. It is remarkable that the difference in correlation between NOSN and GFS increases with time, especially after 48 h. This implies that synoptic field errors resolved by the regional model increase more rapidly than those of the global model. Conversely, the correlations of wind
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and geopotential height between SN and the GFS analysis increase, implying a reduction in error for synoptic fields such as the monsoon circulation and subtropical high related to TC movement. Therefore, the improved forecast of synoptic fields by spectral nudging induces a positive effect on the track forecast. Because the GFS run is based on a global forecast model, it seems that GFS more accurately represents the synoptic fields. As mentioned by Waldron et al. (1996), it is difficult for regional models to resolve large-scale waves due to imperfections in the lateral boundary conditions, while they can be reasonably resolved by global models whose domain covers the entire earth.
Similar to results in section 3.1.3, TC intensity tends to be underestimated with not optimized spectral nudging (Figure 3.9). NOSN simulates approximately accurate TC intensity before 66 h; however, it tends to be slightly overestimated after 72 h (<5 m s-1). Conversely, the SN run without optimized nudging options tends to underestimate TC intensity due to the fact that the GFS forecast data simulated weak TC intensity.
The underestimation of TC intensity in SN is prominent during intermediate forecast lead times. The OSN run optimized for the intensity forecast tends to simulate stronger TC intensity as compared to the SN run because the strength of spectral nudging is reduced. Before 90 h, when the intensification processes of most cases are included, OSN has an intensity error which is considerably smaller as compared to SN.
Figure 3.9 Same as Figure 3.6, but for mean maximum surface wind speed error.
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Although it appears that the effect of spectral nudging is not significant until 96 h (Figure 3.7), the effect can vary considerably between cases. Figure 3.10 shows the differences in TPE between OSN and NOSN together with differences between GFS and NOSN. Overall, the presence of many positive and negative TPE differences between OSN and NOSN imply that spectral nudging works differently for each case. Spectral nudging negatively affects the forecast track for 30 of the 51 cases at 24 h. A higher number of positive TPE difference between GFS and NOSN indicates GFS has worse track predictability than NOSN at an early forecast time. Therefore, OSN with spectral nudging of GFS forecast has larger TPE compared with NOSN at an early forecast time. It is notable that the number of improved cases by spectral nudging tends to increase as the forecast lead time increases. In addition, OSN tends to have similar TPEs to GFS forecast as forecast time increases. In particular, at 120 h, spectral nudging decreases the track error by more than 200 km in 16 cases while track error increments above 200 km are observed in 5 cases. The GFS forecast has smaller TPEs than NOSN, and this leads to the improvement of track forecast in OSN. Therefore, spectral nudging tends to improve the track forecast when forecast lead time is longer than 3 days. However, it is difficult to conclude that this is true for all TC cases. To clarify the uncertain effects of spectral nudging it is necessary to determine the cases in which spectral nudging can improve the track forecast.
Figure 3.10 The TPE difference between OSN and NOSN (bar; OSN-NOSN) arranged in ascending order together with the difference between GFS-NOSN (dots; GFS-NOSN). Improved cases are colored in blue, while the others are colored in red.
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The GFS forecast providing initial and boundary data precede the operation of the real-time TC forecast by the WRF model. Thus, the characteristics of simulated track in the GFS forecast can be analyzed prior to real- time operation. If spectral nudging is applied selectively after analyzing the simulated track from GFS, then the track forecast can be further improved. Figure 3.11 shows the simulated tracks of GFS for all cases used in this study. Each track is colored according to whether its 5-day mean track error is decreased by spectral nudging. Most tracks improved with spectral nudging are in the northeast of the active TC region (105~160ºE and 5~40ºN), while those who did not improve are generally in the southwest. The TCs in the northeast of the active TC region are profoundly affected by the subtropical and mid-latitude environmental conditions associated with the WNPSH and large-scale circulations, and tend to follow curved tracks. Spectral nudging has a positive impact on the forecast track for these systems due to the improved synoptic fields. Therefore, the positive effects of spectral nudging can be enhanced by only applying it to TCs with curved tracks.
Figure 3.11 GFS forecast track of improved (blue) and degraded forecasts (red) using the spectral nudging for the 51 TC cases listed in Table 3.2. The box represents the criterion area for SOSN.
To quantify whether spectral nudging is effective at the northeast of the TC active region, we recalculate the track error using a combination of results from OSN and NOSN runs based on the location of TC center. The
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OSN tracks are used for the 32 simulations passing through the northeast area (17 ~ 40oN and 130 ~ 160ºE;
Figure 3.11) at least once in forecast hours, while the NOSN tracks are employed for the remaining 19 simulations. We named these combined sets SOSN (Selective and Optimized SN). These classifications are empirically derived to minimize the 5-day mean track error. The SOSN run in figure 3.7 highlights the results of the recalculation. Of the 19 cases where spectral nudging is not employed, OSN with optimized spectral nudging demonstrates worse performance in 14 cases as compared to NOSN. The 5-day mean track error for SOSN is reduced to 153.5 km as compared to 161.8 km for OSN. In particular, the reduction of the track error is relatively large in the middle phase of the forecast; by 8.7% and 9.0% at 72 h and 96 h, respectively.
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