5. Impact of TC-ocean interaction on TC intensity forecast
5.2. Model configuration and experiments
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which is the terrain-following vertical coordinate system. It can consider the bathymetry of the ocean, and it can realistically simulate the ocean dynamics in coastal regions.
We organized the three experiments which are uncoupled-OISST (UC_OISST), uncoupled-HYCOM (UC_HYCOM), and ROMS coupled-HYCOM (COAWST_HYCOM). 12 km single domain is utilized, and GFS analysis data is nudged to exclude the impact of different track forecasts of each experiment. To investigate the effect of the initial ocean state, two ocean data is compared. The Optimum Interpolation Sea Surface Temperature (OISST) is constructed by combining observations from different platforms (satellites, ships, buoys, and Argo floats) with a 0.5o of horizontal resolution. The Hybrid Coordinate Ocean Model (HYCOM) reanalysis is produced by coupling with Navy Global Environmental Model (NAVGEM) with a 1/12o of horizontal resolution. To figure out the effect of ocean initialization and coupled model, we design four experiments, UC_GFSSST (uncoupled, initialized with OISST), OML_OISST, OML_HYCOM, PWP_HYCOM. Test cases are TC Soulik and initialized at 0000 UTC 19 August 2018. Typhoon Soulik is tested since it experiences the noticeable cooling because of remarkably slow (~2 m s-1) translation speed over cold wake. For the Soulik, the spectral nudging with GFS analysis data is applied to reduce the variation by track forecast difference.
5.3. Tropical cyclone-Ocean interaction
Figure 5.2 shows the maximum wind speed forecast of UC_GFSSST, UC_HYCOM, and COAWST. All experiments overestimate the intensity compared to the best-track data. UC_HYCOM and COAWST relatively simulate well the intensity in early forecast time. However, UC_GFSSST rapidly intensify and overestimate the intensity. It is because the initial sea surface temperature is much smaller in HYCOM (Figure 5.3). Since the HYCOM is produced by atmospheric coupled model the cooling effect in last timestep exist in initial field.
In addition, the SST is generally lower along with the simulated track. UC_GFSSST experiment tend to overestimate the intensity for whole forecast time. UC_HYCOM shows the similar intensity with COAWST, but there is sudden intensification at the late forecast time. On the other hands, COAWST well simulate the decaying processes compared to other experiments. It is because it moves slowly, and significant ocean cooling occurs at the late forecast time. The UC_OISST and the UC_HYCOM experiments cannot realistically resolve the decaying because of the absence of the TC-ocean interaction.
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Figure 5.2 Simulated surface maximum wind speed of UC_GFSSST, UC_HYCOM, and COAWST for TC Soulik initialized at 0000 UTC 19 August 2018.
Figure 5.3 Sea surface temperature of HYCOM and OISST at initial time. Black box indicates the initial location of forecast.
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Figure 5.4 shows the heat flux from ocean to atmosphere for all experiments. By initializing HYCOM, UC_HYCOM and COAWST simulate lower heat flux at early forecast time. It corresponds to the result that maximum wind speed is weakly simulated in UC_HYCOM and COAWST at early forecast time. By coupling ocean model, COAWST simulate lower heat flux at decaying process. It is because significant cooling is reproduced in COAWST experiments and ocean cooling feedback suppress the sudden intensification.
Figure 5.4 Heat flux from ocean to atmosphere for all experiments.
The lower heat flux of COAWST at decaying process is derived from ocean cooling feedback. Figure 5.5 shows surface temperature of UC_GFSSST and COAWST at 90 hours forecast time. There is a significant sea surface temperature cooling in COAWST experiment nearby the Jeju island. This cooling occurs because of the slow translation speed and low heat potential. Figure 5.6 shows the vertical cross-section of ocean temperature nearby the cooling region. It shows that underlying cold water ascends by the upwelling process, and surface temperature is significantly decreased.
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Figure 5.5 Surface temperature of UC_GFSSST and COAWST at 90 hours forecast time.
Figure 5.6 Vertical cross-section of ocean temperature at latitude of 33°N and longitude from 123°E to 128°E
Tropical cyclone heat potential (TCHP) is calculated by below equation, and figure 5.7 shows the TC translation speed and TCHP of underlying ocean. At the late forecast time, the TC moves very slowly, and
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heat potential of underly ocean is low. Therefore, significant cold wake occurs, and the simulation of decaying process is improved. This result shows the possibility to further improvement of intensity forecast if 3- dimensional ocean model is coupled to the integrated forecast model.
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6. Verification of integrated TC forecast model
6.1. Integrated TC forecast model for real-time forecast
The integrated TC forecast model is developed from previous results and real-time TC forecast system is constructed. The model consist of selective and optimized spectral nudging and moving nesting domain. The ocean coupling model is not included in integrated TC forecast model since the data is not available in real- time. Figure 6.1 shows the processes of real-time TC forecast system with the integrated TC forecast model.
The forecast starts based on the TC warning data released by JTWC. When the 120 hours forecast of GFS is ended, the system download the GFS data to use as initial and boundary data for the integrated TC forecast model. The simulated track by the GFS forecast is obtained by tracking the GFS forecast data. The track of GFS forecast is used to determine the cluster of TC. After calculating the distance between track of GFS forecast and center point of each cluster as listed in Table 6.1, the cluster whose the distance is minimum is determined as the cluster of target TC. If the determined cluster is C1, C2 or C5, the spectral nudging is applied during the model integration. If the determined cluster is C3 or C4, the model is integrated without the spectral nudging. The five-day forecast of the integrated TC forecast is conducted with moving nesting domain, and then simulated track and intensity data is produced by post-processing system.
Figure 6.1 Forecasting system of integrated TC forecast model.
Tropical cyclone occurs
Data download (JTWC warning, GFS forecast)Tracking of GFS forecast Clustering
Post-processing Spectral nudging
Moving nesting
Moving nesting
cluster 1,2,5 cluster 3,4
59 Table 6.1 Center latitude and longitude of each cluster
Center latitude Center longitude
C1 23.1 131.1
C2 17.6 138.3
C3 13.2 127.5
C4 13.0 150.1
C5 25.2 148.7
6.2. Data and experiments
To verify the improvement of this study, the simulations of six experiments are conducted and compared (Table 6.2). The integrated TC forecast model is same as SOSN_2DM experiment. The forecast error of GFS forecast and the HWRF model are also compared together. JTWC best-track data is used for the verification.
A total of 115 five-day forecast for 34 TCs occurred in 2013 – 2017 is designated for the test cases
Table 6.2 Description of each experiment
Experiments Domain Spectral nudging
NOSN_1DM Single domain (12 km) No
SN_1DM Moving nesting (12 – 4 km) Yes
NOSN_2DM Single domain (12 km) No
SN_2DM Moving nesting (12 – 4 km) Yes
SOSN_1DM Single domain (12 km) For cluster 1,2,5
SOSN_2DM Moving nesting (12 – 4 km) For cluster 1,2,5
6.3. Verification of track and intensity error of integrated TC forecast model.
Figure 6.2 shows the track errors of each experiments. The NOSN_1DM experiments has the largest error among experiments and especially large at 120 hours. The spectral nudging effectively reduce the track error, and the track error is further decreased by SOSN. In addition, there are slight decrease in track error by inreasing horizontal resolution by comparing 1DM and 2DM experiments. The SOSN_2DM experiment has the smallest track error. The SOSN_2DM has smaller track error compared to the GFS and HWRF.
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Figure 6.3 shows the maximum wind speed bias and RMSE of each experiments. The GFS forecast and 1DM experiments generally underestimate the mamximum wind speed through all forecast time.The intensity of 2DM experiments are relatively increased and the biases are significantly reduced with 2DM experiments.
However 2DM experiments tend ot overestilate the intensity at the late forecast time. HWRF shows the smallest MWS bias but has relatively large RMSE. SN_2DM and SOSN_2DM has the smallest RMSE.
Table 6.3 shows the correlation coefficients and normalized standard deviation, and Figure 6.4 shows the Taylor diagram of maximum wind speed for each experiment. The correlation coefficients of experiments are similar between all experiments, but standard devations are increaed in 2DM experiments. However, the standard deviation is still lower with moving nesting domain compared to the HWRF. This result indicate that the intensification and decaying process are needed to be improve for the realistic forecast of the intensity.
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Figure 6.2 Track errors of each experiment with that of GFS forecast and HWRF.
Figure 6.3 Maximum wind speed bias (top) and RMSE (bottom) of each experiment with that of GFS forecast and HWRF.
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Figure 6.4 Taylor diagram of maximum wind speed for each experiment. The correlation coefficients and normalized standard deviations are calculated with JTWC best track data.
Table 6.3 Correlation coefficient and standard deviation of maximum wind speed for each experiment. The correlation coefficients and normalized standard deviations are calculated with JTWC best track data.
Correlation coefficient Standard deviation
NOSN_1DM 0.62 0.58
NOSN_2DM 0.63 0.76
SN_1DM 0.62 0.56
SN_2DM 0.62 0.71
SOSN_1DM 0.61 0.57
SOSN_2DM 0.62 0.73
GFS 0.57 0.64
HWRF 0.64 0.86
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The SOSN_2DM which is developed through this study are verified with NOSN_1DM, GFS forecast and HWRF. The track errors and maximum wind speed RMSEs for three-days, five-days, and five-day mean are compared to each model errors (Table 4.4 and Table 4.5). The NOSN_1DM is initial version of this study, and SOSN_2DM shows the significant improvement in terms of track and intensity forecast. However, the maximum wind speed RMSE at five-day are slightly increased because of the overestimated intensity at the late forecast time. The track error is also decreased compared to the GFS. The decrease rate is especially large at three day forecast. The intensity forecast error is significantly decreased compared to the GFS. This shows the advantage of higher-resolution domain of the regional model in intensity foecast. The HWRF shows the large track error through the whole forecast time. The SOSN_2DM shows the notably smaller track error compared to HWRF. The MWS RMSE at 72 hours are decreased with 5.1% but it increased with 2.1% at 120 hours. The track forecast of SOSN_2DM is more accurate than GFS and the intensity forecast of it is more accurate than HWRF. By the spectral nudging, track error is decreased with 8%, and by the higher-resolution, track error is decreased with 1%, and intensity error is decreased with 16 %.
Table 6.4 Track error decrease rate of SOSN_2DM experiment compared to NOSN_1DM, GFS forecast, and HWRF.
Error decrease rate compared to
NOSN_1DM GFS HWRF
SOSN_2DM Track error (km)
3-days -11.6% -11.5% -9.9%
5-days -12.9% -1.9% -16.1%
5-day mean -9.3% -3.9% -12.4%
Table 6.5 Maximum wind speed RMSE decrease rate of SOSN_2DM experiment compared to NOSN_1DM, GFS forecast, and HWRF.
Error decrease rate compared to
NOSN_1DM GFS HWRF
SOSN_2DM Max wind speed RMSE (m/s)
3-days -8.4% -35.7% -5.1%
5-days +1.3% -13.7% +2.1%
5-days mean -16.2% -31.8% -4.5%
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7. Discussion and conclusion
7.1. Discussion
Diagnosing model forecast error is an essential task in terms of identifying the problem and establishing the solution for model development. This study examined the TC track and intensity forecast for the western North Pacific TCs. Since the western North Pacific has an inherent climate system (e.g., the western North Pacific subtropical high or mid-latitude trough), different characteristics for TC forecast according to the location were utilized to establish an integrated TC forecast model. However, other results may appear depending on the basin. For improving the TC forecast of another basin, it is necessary to understand the environmental climate system. After understanding the surrounding environment, the forecast results of the model could be interpreted with relation to the environmental system. If the distinct results are shown in the global and regional model, the forecast of the regional model can be improved by using selective and optimized spectral nudging.
Previous studies on the relationship between model resolution and TC forecast did not thoroughly deal with the impact of model resolution on track forecast. This study identified the biases in the TC forecast longer than three days and verified that the tendency is decreased through the high-resolution domain in the TC core region.
Due to the limitation of computing resources, 4 km grid spacing was employed for the moving nesting domain in this study, but the more distinct result can be obtained by increasing model resolution to less than 2 km.
Since this study dealt with a large number of cases, only general results were analyzed using mean values.
When computing resources are sufficient, it is recommended to increase the resolution to around 1 km. In addition, several studies show that increasing vertical layers and more detailed topography and coastline also could improve the track and intensity forecast. Furthermore, better representation of TC and ocean surface is necessary for further improvement.
The air-sea coupled model is studied, and a positive result is expected when involved in the integrated TC forecast model. However, it is not utilized since it is not adequate for the real-time forecast system. It is because accurate ocean analysis data is not provided in real-time. Besides, the 3-dimensional ocean model needs a large amount of computational resources comparable to the atmospheric model. In addition, the ocean feedback affects limited TC cases, which have slow translation speed and significant heat potential of the underlying ocean. A more in-depth study is needed, and ocean coupling only for TCs in coastal regions is also considered.
65 7.2. Conclusion
The track forecast error of the WRF model for TCs over the western North Pacific was analyzed. A cluster analysis was applied based on TC location through the k-means clustering algorithm, and five clusters were classified. Since there were robust differences in the track error between clusters with dissimilar transition directions, we grouped the clusters into two groups: group 1 (C1 and C2) and group 2 (C3 and C4). The two groups had apparent differences in track errors. A higher number of large error cases were included in group 1 TCs, while more small error cases were included in group 2 TCs. To understand this regional difference in track error, we analyzed the optimal steering vectors. The steering vector difference between model and analysis data, which indicates the environmental wind error, was larger for group 1 TCs, especially at the late forecast lead time. This result corresponded to the rapidly increasing track error of group 1 TCs after 48 hours.
The pattern correlation of 500 hPa geopotential height, which is highly related to environmental wind, was also lower for group 1 TCs. This showed that the large track error for group 1 TCs arises from the unrealistic representation of environmental fields affected by the subtropical high or mid-latitude trough. The residuals, which contained the features not considered in the environmental wind, were also larger for group 1 TCs. The significant forecast error for group 1 TCs at the late forecast time can be attributed to the fact that the environmental wind error is larger for group 1 TCs. In addition, the more complicated processes of mid-latitude TCs are not adequately simulated.
The spectral nudging, which is not often used in TC forecast, is applied on real-time TC forecast. Case studies for Typhoons Neoguri and Vongfong showed that spectral nudging benefitted track forecasting by improving the simulation of large-scale wind and the subtropical high. To optimize the impact of spectral nudging on intensity forecast, the sensitivity of nudging options was tested. Sensitivity experiments showed that spectral nudging was optimized when the cut-off wavelength was increased and the nudging coefficient decreased. The spectral nudging generally improved the track forecast, especially as the forecast lead time increased. Spectral nudging improved the forecast of the large-scale fields which play an important role in steering TCs. Furthermore, our results showed that the effect of nudging depended on the locations of TCs.
The most effective method (Selective and Optimized Spectral Nudging, SOSN) was suggested to utilize the spectral nudging on real-time TC forecast.
12–4 km moving nesting experiments were conducted to understand the effect of increasing the model resolution on the five-day forecasts of TCs over the WNP. The 12 km single domain experiments did not properly reproduce the MWS, and strong wind speeds faster than 60 m s−1 were not captured using the 12 km horizontal grid spacing. However, the simulations of TC intensification improved in 12–4 km moving nesting experiments. The RMSEs of MWS and MSLP were reduced, and the wind-pressure relationship was improved in the 2DM experiment. The 1DM experiment tended to simulate rightward-biased TC tracks compared to the actual TC track in the track forecast. The tendency to deflect the TC track rightward decreased for intense TCs
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in the 2DM experiment, and was associated with an improved intensity forecast for the intense TCs. The reduction in cross-track bias was especially significant for the intense TCs located at lower latitudes. The TCs moving toward mid-latitudes seemed to be more affected by the prediction of the surrounding environmental field. The TCs in lower latitudes were more sensitive to the intensity forecast because the accuracy of the environmental field prediction was relatively high, and intensity forecasts were better than those for the mid- latitude TCs.
To understand the effect of air-sea interaction on TC forecast, the ocean model coupling and the initialization with high-resolution ocean reanalysis data were applied. For typhoon SOULIK, the initialization with HYCOM reanalysis reduced the sudden unrealistic intensification during the early forecast time. The cold wake was realistically simulated by the coupled model, and the ocean feedback improved the simulation of decaying process for the late forecast time. Therefore, the intensity forecast was improved in coastal regions.
An integrated TC forecast system based on the WRF model with the spectral nudging and moving nesting method was established for the real-time forecast of TCs over the western North Pacific. The integrated TC forecast system had the smallest track and intensity error among the experiments. The integrated TC forecast system had smaller or similar track error compared to GFS, which has advantages in the track forecasts. Its intensity error was comparable to that of HWRF, which has an advanced intensity forecast.
67 8. Reference
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