3. Impact of spectral nudging on real-time TC forecast
3.2. Model configuration and experiments
We used the WRF model version 3.4 to verify spectral nudging effects on TC forecasts. Spectral nudging in WRF can be modified by adjusting the nudging factors including the nudging interval, variables, cut-off wavelength, coefficient, and so on. The horizontal resolution and grid numbers of the domain were 12 km and 421 x 371, respectively. The domain contained 28 vertical levels from the surface to the top of the atmosphere at 50 hPa, with a model time step equal to 60 s. All simulations were 120 h forecasts from model initiation.
The center of the domain was approximately 15ΒΊ to the northwest of the observed TC center when the latitude of the TC center was south of 20ΒΊN, and 10ΒΊ to the north when the latitude of TC center was north of 20ΒΊN.
Due to the change in domain center for each forecast, the domain could cover the entire track of the simulated TCs for five days. Real-time global analysis data and forecast data from the Global Forecast System (GFS) of the National Centers for Environmental Prediction (NCEP) were used to provide the initial and boundary conditions for the WRF model with a grid spacing of 0.5o. The GFS real-time forecast is available every 6 hours. This 6-hourly forecast data is used as initial and large-scale forcing data through the WRF pre- processing system (WPS), and the model sea surface temperature is updated with the GFS. TC vortex initialization methods, such as the bogussing technique, were not applied to solely focus on the effects of spectral nudging. The model utilized the Yonsei University planetary boundary layer scheme scheme (Hong et al., 2006), WRF single-moment 6-class microphysics scheme (Hong & Lim, 2006), Kain-Fritsch cumulus parameterization scheme (Kain, 2004), Dudhia short-wave radiation scheme (Dudhia, 1989), and Rapid Radiative Transfer Model long-wave radiation scheme (Mlawer et al., 1997).
To elucidate the impact of spectral nudging on TC forecasting, three experiments were conducted. The first was a case study to examine the effects of spectral nudging for Typhoons Neoguri and Vongfong. Typhoons Neoguri and Vongfong were classified as Category 5 on Saffir-Simpson hurricane wind scale, and their maximum wind speeds were 54 m s-1 and 59 m s-1, respectively. The Typhoon Neoguri and Vongfong experiments consisted of 6 and 10 simulations, initialized every 12 hours from 0000 UTC 4 Jul 2014 to 1200 UTC 6 Jul 2014 and 0000 UTC 4 Oct 2014 to 1200 UTC 8 Oct 2014, respectively. The NOSN run was a basic WRF forecast without spectral nudging. It was forced by the lateral boundary conditions only. In the SN run, the WRF forecast was forced by the large-scale wind field of global forecast data in the domain interior.
Spectral nudging options were as follows. Waves of u-component wind, v-component wind, and potential
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temperature whose wavelengths were longer than 1000 km were nudged with a nudging coefficient of 0.0003 s-1. The humidity was not nudged because its effect is not as significant as other fields. The nudging coefficient of 0.0003 s-1 corresponds to an e-folding damping time of 55.6 min. The variables at the 10 lowest sigma levels (below about 800 hPa) were not nudged to avoid the suppression of TC dynamics at the near-surface, and the nudging coefficient linearly increases from 0 at the 10th sigma level to its maximum value at the top of the model. Spectral nudging was applied for the entire forecast period (120 hours) every 10 minutes.
Second, we designed a sensitivity test on spectral nudging options to optimize the spectral nudging effect on the TC forecast. The cut-off wavelength, nudging coefficient, and nudging interval were controlled to optimize the impact of spectral nudging on TC forecasts. Spectral nudging decomposes the environmental variables using a specific wavelength and nudges only large-scale fields because small-scale features are intrinsically resolved by the regional model. When the small-scale fields of global model data are nudged into the regional model, they can suppress TC intensification and formation. Conversely, a long cut-off wavelength can reduce the effect of spectral nudging by excluding the large-scale fields which determine the TC track.
Therefore, the cut-off wavelength of spectral nudging should be optimized to maximize the positive effects of spectral nudging. In spectral nudging method, the difference between the large-scale fields of the global model and the WRF model is nudged to the model field by multiplying by the nudging coefficient;
ππ
ππ‘ = πΉ(π) + πΌ(ππΊπΏβ ππ πΏ)
where Q is the model prognostic variables, F is the model operator, πΌ is a nudging coefficient, and ππΊπΏ and ππ πΏ are the large-scale components of the global forecast result and the WRF model forecast, respectively. The nudging coefficient can also control the degree of spectral nudging and should be optimized for successful TC forecasting. In this study, the nudging coefficient for zonal wind, meridional wind and temperature are modified to control the strength of spectral nudging. The nudging process was conducted over designated time intervals in spectral nudging method. The time interval of the nudging process can also influence the degree of spectral nudging, and its effects on TC forecasts need to be investigated. Hence, we designed the sensitivity experiment to test three spectral nudging options; therefore, eight experimental sets of spectral nudging options were accomplished (Table 3.1). Experiments were applied to Typhoons Phanfone and Vongfong, and initial times were 0000 UTC 1 October 2014 and 1200 UTC 6 October 2014, respectively.
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Table 3.1 Details of the options employed in each spectral nudging sensitivity test.
Experiment Wave length Coefficient Interval
WV1_C30_INT10 about 1000 km 0.00030 10 min
WV1_C30_INT20 about 1000 km 0.00030 20 min
WV2_C30_INT10 about 2000 km 0.00030 10 min
WV2_C30_INT20 about 2000 km 0.00030 20 min
WV1_C03_INT10 about 1000 km 0.00003 10 min
WV1_C03_INT20 about 1000 km 0.00003 20 min
WV2_C03_INT10 about 2000 km 0.00003 10 min
WV2_C03_INT20 about 2000 km 0.00003 20 min
Lastly, to verify the optimization and to generalize the effects of spectral nudging on TC forecasts, TCs that occurred during 2013 and 2014 over the WNP were simulated using optimized spectral nudging. A total of 51 forecasts for 18 TCs were conducted, and NOSN, SN, and OSN (Optimized SN) runs were compared to investigate the effects of spectral nudging on TC forecasts (Table 3.2). In the OSN runs, spectral nudging options optimized by the sensitivity test were employed in spectral nudging configuration.
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Table 3.2 Forecast Details and Initial Times for the 18 Typhoons which Occurred during 2013 and 2014. The Interval of Forecast Initial Time is 24 h.
TC number TC name Forecast initial time (interval of 24 h) Number of cases
1307 SOULIK 2013/07/08 00 UTC 1
1311 UTOR 2013/08/10 00 UTC 1
1319 USAGI 2013/09/17 00 UTC ~ 2013/09/18 00 UTC 2
1323 FITOW 2013/10/01 00 UTC ~ 2013/10/02 00 UTC 2
1325 NARI 2013/10/10 00 UTC 1
1326 WIPHA 2013/10/11 00 UTC ~ 2013/10/13 00 UTC 3
1327 FRANCISCO 2013/10/17 00 UTC ~ 2013/10/21 00 UTC 5
1329 KROSA 2013/10/30 00 UTC 1
1330 HAIYAN 2013/11/04 00 UTC ~ 2013/11/06 00 UTC 3
1408 NEOGURI 2014/07/04 00 UTC ~ 2014/07/06 00 UTC 3
1409 RAMMASUN 2014/07/13 00 UTC ~ 2014/07/15 00 UTC 3
1410 MATMO 2014/07/18 00 UTC ~ 2014/07/20 00 UTC 3
1411 HALONG 2014/07/30 00 UTC ~ 2014/08/06 00 UTC 8
1416 FUNG-WONG 2014/09/18 00 UTC ~ 2014/09/19 00 UTC 2
1417 KAMMURI 2014/09/25 00 UTC 1
1418 PHANFONE 2014/09/30 00 UTC ~ 2014/10/01 00 UTC 2
1419 VONGFONG 2014/10/04 00 UTC ~ 2014/10/08 00 UTC 5
1422 HAGUPIT 2014/12/02 00 UTC ~ 2014/12/06 00 UTC 5
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