Vol.21 No.4 JOURNAL OF TROPICAL METEOROLOGY December 2015

Article ID: 1006-8775(2015) 04-0311-15 HIGH-RESOLUTION NUMERICAL SIMULATION OF TYPHOON LONGWANG (2005) WITH THE SPECTRUM NUDGING TECHNIQUE

1, 2 2 2 LI Jing (李 静) , TANG Jian-ping (汤剑平) , FANG Juan (方 娟)

(1. Shanghai Central Meteorological Observatory, Shanghai Meteorological Bureau, Shanghai 200030 China; 2. Key Laboratory of Mesoscale Severe Weather (MOE), School of Atmospheric Sciences, Nanjing University, Nanjing 210043 China)

Abstract: With the Weather Research and Forecasting model (WRFV3.2.1), the application of spectrum nudging techniques in numerical simulation of the genesis and development of typhoon Longwang (2005) is evaluated in this work via four numerical experiments with different nudging techniques. It is found that, due to the ability to capture the large-scale fields and to keep the meso-to small-scale features derived from the model dynamics, the experiment with spectrum nudging technique can simulate the formation, intensification and motion of Longwang properly. The improvement on the numerical simulation of Longwang induced by the spectrum nudging depends on the nudging coefficients. A weak spectrum nudging does not make significant improvement on the simulation of Longwang. Although the experiment with four-dimensional data assimilation, i.e., FDDA, also derives the genesis and movement of Longwang appropriately, it fails to simulate the intensifying process of Longwang properly. The reason is that, as the large-scale features derived from the model are nudged to the observational data, the meso- to small-processes produced by the model dynamics important to the intensification of typhoon are nearly smoothed by FDDA. Key words: typhoon; genesis; intensification; spectrum nudging; FDDA CLC number: P444 Document code: A doi: 10.16555/j.1006-8775.2015.04.001

1 INTRODUCTION numerical model, the nudging methods are usually adopted in the numerical simulation. A widely used Along with the development of numerical nudging technique in the numerical model is the calculation techniques and the appearance of traditional nudging, i.e., four-dimensional data high-speed computers, numerical models become an assimilation (FDDA), which is conducted by adding important tool in the studies on tropical cyclones nudging terms in the model equations to reduce the (TCs). To improve the ability of numerical models to deviations of the model-simulated fields from the simulate TCs, many efforts have been made, such as observations (Stauffer and Seaman[13, 14]; Stauffer et increasing the horizontal grid resolution to as much as [15] [1] [2] al. ). Different from FDDA, the spectral nudging to 1 km (Chen et al. ; Menelaou et al. ), modifying [16] [3] technique, proposed by Waldron and then improved the parameterization schemes (Lin et al. ; Ma and by von Storch[17], is conducted by adding nudging Tan[4]; Deshpande et al.[5]), and improving the initial [6] [7] terms in the spectral space. In this way, the conditions (Zou and Xiao ; Zhang et al. ; Yuan et model-derived large-scale fields can be adjusted to be al.[8]; Ma and Tan[9]; Yuan et al.[10]; Liu et al.[11]; [12] close to the observations while the model-simulated Zhang et al. ). meso- to small-scale fields are left unmodified. Due to the vital role of the large-scale Recently, many regional climate models adopt background flow played in the motion and evolution the spectral nudging technique in simulating the of TCs, the appropriate description of the large-scale climatological activity of TCs (Knutson et al.[18]; background circulation in which TC is embedded by Feser and von Storch[19]; Cha et al.[20]; Feser and the numerical model is a precondition of successful Barcikowska[21]; Cao et al.[22]). Knutson et al.[18] simulation of TCs. To ensure that the large-scale showed that a RCM with spectral nudging improved background flow of TCs is properly captured by the the interannual variability of hurricane occurrences by

Received 2014-10-20; Revised 2015-08-11; Accepted 2015-10-15 Foundation item: Nature Science Foundation of China (41475046, 41130964); State Key Program of China (2012CB417201) Biography: LI Jing, Assistant Engineer, Shanghai Central Meteorological Observatory, primarily undertaking research on tropical cyclogenesis. Corresponding author: FANG Juan, e-mail: [email protected] 312 Journal of Tropical Meteorology Vol.21 decreasing the number of simulated hurricanes in typhoon in the northwest Pacific in the 2005 typhoon inactive seasons. In Feser and von Storch[19], it is season. In the wake of Saola, there were several found that regional models with spectral nudging can positive vorticity centers. Since the vorticity was improve simulated typhoon developments from global rather weak, no closed cyclonic circulations formed in forcing reanalysis data by giving lower core pressure the wake of Saola. About one day later, the vorticity and higher wind speeds and more realistic strengthened and a closed cyclonic circulation precipitation patterns. In the experiments to evaluate appeared in the wake of Saola. The cyclonic simulated typhoon sensitivities to spectral nudging, circulation enlarged and intensified gradually in the Cha et al.[20] adopted the intermittent spectral nudging next two days and finally developed into a tropical to avoid the suppression that the intensities of depression by 0600 UTC 25 September 2005 simulated typhoons decrease as the spectral nudging (Fig.1b-1d). effect increases while the tracks of simulated Li and Fu[24] argued that TC could induce the typhoons are improved. Recently, Feser and Rossby wave, which might lead to tropical Barcikowska[21] indicated that spectral nudging has cyclogenesis in the wake of the preceding TC via mostly positive effects on typhoon formation, location energy dispersion. To investigate how well the and general circulation patterns in the generation tropical cyclogenesis mechanism proposed by Li et al. areas of TCs. In addition to being used in the (2006) worked in the formation of Longwang, the numerical simulations on the climatological activity signature of Rossby wave was extracted from the of TCs, the spectral nudging method can also be 850-hPa wind fields in the period from 22 to 25 adopted in TC case studies. In Feser and von September 2005 via a 3- to 8-day bandpass filtering Storch[23], the performance of spectral nudging in the following Li and Fu[24]. In the early developing stage construction of a very strong and large typhoon, of Saola, the 3- to 8-day filtered 850-hPa wind fields Winnie (1997), is evaluated. The results indicate that did not exhibit any Rossby wave signals in the wake the spectrum nudging can considerably improve the of Saola because the TC was very weak. From Fig.2a simulation of the motion and evolution of Winnie. we can see that a weak anticyclonic circulation However, due to the relative coarse horizontal grid appeared in the southeast side of Saola at 0600 UTC resolution, i.e., 18 km, the simulation conducted by 22 September. In the following 24 hours, a Feser and von Storch[23] cannot capture the details of northeasterly was considerably enhanced in the east of Winnie evolution and is not good enough for the the anticyclonic circulation and a closed cyclonic studies on the meso-scale processes occurring in the circulation developed gradually in the southeast side evolution of Winnie. of the anticyclonic circulation (Fig.2b and 2c). The In the northwest Pacific, two or more TCs may cyclonic circulation intensified gradually and finally coexist for several days. Under such a circumstance, developed into a tropical depression, i.e., Longwang, successful simulation on TCs becomes difficult. With at 0600 UTC 25 September (Fig.2d). On after the the intention to evaluate the performance of spectral other, the cyclonic, anticyclonic and cyclonic nudging in the numerical simulation of TCs that circulations appeared in the wake of Saola, clearly evolve in an environment with complicated indicating that a Rossby wave train was activated by circulations, a numerical high-resolution simulation of Saola. Obviously, the Rossby wave played an typhoon Longwang (2005) is conducted in this work. important role in the formation of Longwang via The remainder of this paper is organized as follows: offering a low-level cyclonic circulation to trigger the Section 2 presents an overview of the evolution of wind-induced surface heat exchange (WISHE). Typhoon Longwang. Section 3 describes the experiment and methodology. The performance of the spectral nudging in simulation of Longwang is investigated in section 4 and concluding remarks are given in section 5.

2 OVERVIEW ON THE EVOLUTION OF LONGWANG

2.1 Formation of Longwang Figure 1 displays the 850-hPa horizontal streamlines and relative vorticity derived from the NCEP/NCAR reanalysis data in the northwest Pacific during 22-25 September 2005. At 0600 UTC 22 September 2005, a TC, coded Saola, was active at around 25°N, 144°E (Fig.1a), which is the eighteenth

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Figure 1. The 850-hPa horizontal streamlines and relative vorticity (colored every 2×10-5 s-1) over the tropical Pacific at (a) 0600 UTC 22, (b) 0600 UTC 23, (c) 0600 UTC 24 and (d) 0600 UTC 25 September 2005. The letters “S” and “L” denote typhoon Saola and Longwang, respectively.

Figure 2. The 3- to 8-day bandpass-filtered 850-hPa wind fields and 850-hPa wind speed (shaded) at (a) 0600 UTC 22, (b) 0600

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314 Journal of Tropical Meteorology Vol.21 UTC 23, (c) 0600 UTC 24 and (d) 0600 UTC 25 September 2005. The letters “S” and “L” denote Saola and Longwang, respectively.

gradually. 2.2 Intensification of Longwang 2.3 Movement of Longwang Figure 3a displays the time evolution of the minimum sea-level pressure and maximum wind Figure 4a displays the track of Longwang. It is speed at z = 10 m of Longwang. In the environment obvious that Longwang moved west-northwestward with relatively weak vertical wind shear and abundant and westward in its lifetime. Comparing the moving moisture (Fig.3b), Longwang intensified gradually speed of Longwang to the speed of the steering flow after being identified as a tropical depression at 0600 (Fig.4b), we can see that the movement of Longwang UTC 25 September. It developed into tropical storm was mainly determined by the steering flow. At the and typhoon at 0000 UTC 26 and 0000 UTC 27 early stage, Longwang moved towards northwest September, respectively. By 0600 UTC 28 September, under the effect of the southeasterly steering flow. As Longwang became a strong typhoon with the the meridional component and zonal component of the maximum 10-m wind speed of ~50 ms-1 and the steering flow decreased and increased respectively, minimum sea-level pressure of around 940 hPa. the moving direction of Longwang shifted to the Subsequently, the vertical wind shear was enhanced a west-northwest from the northwest. From about 0000 little bit. Longwang stopped the intensification and UTC 28 September to 0000 UTC 30 September, the underwent a weak decaying. After 1200 UTC 30 steering flow was very weak, especially its meridional September, Longwang strengthened again in component. Correspondingly, Longwang moved corresponding to the weakening of vertical wind shear. westwards lowly. After 0000 UTC 30 September, At 0000 UTC 1 October, Longwang reached its both the zonal and meridional components of the maximum intensity with the maximum wind speed of steering flow enhanced gradually. Longwang turned 55 ms-1 and minimum sea-level pressure of 935 hPa. to move west-northwestwards with a higher speed. Due to the distinct increase of the vertical wind shear From 0000 UTC 2 October, Longwang was driven by and the approaching and then landfalling at the southeasterly flow and took a northwestward Island and the mainland of China, Longwang decayed journey. rapidly after 0000 UTC 2 October and dissipated

Figure 3. Time evolution of (a) the minimum sea level pressure and the maximum 10-m wind speed (ms-1) of Longwang and (b) vertical wind shear defined as the difference between the area-averaged horizontal winds averaged at 200 and 850 hPa, and the area-averaged low-to middle-level (1 000-500 hPa) relative humidity. The area is a 20°×20° domain around the typhoon center.

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No.4 LI Jing (李 静), TANG Jian-ping (汤剑平), et al. 315 Figure 4. (a) Track of Longwang derived from the best-track data. Each symbol is plotted at the interval of 6 hours and the solid circle is plotted every 24 hours. (b) Time series of the speed of Longwang movement (black lines) and the steering flow (gray lines). The solid and dashed lines denote the zonal and meridional component, respectively.

(Kain[32]). The initial and boundary data of the model 3 MODEL CONFIGURATION AND are from NCEP/NCAR reanalysis. The model EXPERIMENT DESIGN integrates from 0000 UTC 18 September to 1800 UTC 5 October 2005. The mesoscale model for this study is the To investigate the impacts of spectral nudging on Weather Research and Forecasting (WRF) V3.2.1 the simulation of Longwang, four numerical (Skamarock et al.[25]). The model domain, which has experiments are conducted in this work, i.e., control 650 × 500 grid points in each of the zonal and experiment without nudging (CNTRL), sensitivity meridional directions ranges from -3°S to 40°N, and experiments with moderate and weak spectral nudging 110°E to 170°E with a horizontal grid spacing of 9 km. (SN and SN1) which are described below in detail, There are 35 vertical layers between the model top at and traditional four-dimensional data assimilation 50 hPa and the surface. The physical parameterization technique (FDDA). In SN and SN1, the schemes used in this study are WRF model-derived horizontal winds of the scale larger Single-Moment6-class (WSM6, Hong et al.[26]), Rapid than certain scales (described below) are nudged to Radiative Transfer (RRTM) long-wave radiation the NCEP/NCAR reanalysis data every 6 hours while scheme (Mlawer et al.[27]), Dudhia shortwave the horizontal winds on all scales are nudged to the radiation scheme (Dudhia[28]), Monin-Obukhov reanalysis data directly in FDDA. surface layer parameterization scheme (Monin and In the WRF model, we strictly use the spectrum [29] nudging technique which was firstly proposed by Obukhov ), Noah land surface scheme (Chen and [33] [30] Waldron et al. and then was implemented by von Dudhia ), Yonsei University high resolution [34] planetary boundary layer (Noh et al.[31]), and Storch et al. . Consider a WRF variable, Kain-Fritsch convective parameterization scheme

Jm ,Km φ λ,ϕ,t = α m t exp ijλ / L exp ikφ / L ()∑ j,k ()()λ ()φ (1) j=−Im ,k=−Km with zonal coordinates λ , zonal wavenumbers j, and L wavenumbers by k, and the meridional extension φ . L zonal expansion of the area λ . Meridional Also, t represents time. When using spectrum nudging, φ the nudging terms are added to the model results in coordinates are denoted by , meridional the spectral domain in both directions:

JaKa a m ∑η j,k []α j,k ()t −α j,k ()t exp()ijλ / Lλ exp()ikφ / Lφ j=−I ,k =−K a a (2) η 4.1 Performance of the numerical simulations j,k represents the lowest wavenumbers (j and k). So the whole results become similar to the large-scale The model-derived tracks of Longwang are input data. All weather phenomena larger than these shown in Fig.5a. In CNTRL, Longwang forms at wavenumbers were nudged and meso- to small- 20°N, 150°E at around 0000 UTC 22 September, processes could freely develop. The wavenumbers which deviates as far as 5° from the observation and chosen in SN were i=2 and j=2 which correspond to about 5 days ahead of the observation. Although the spatial scales of 650×9 km/2(2925) km in the zonal simulated typhoon (L) also moves direction and 500×9 km/2(2250) km in the meridional west-northwestwards, its track deviates remarkably direction; all weather phenomena larger than those from that of the observation at the same time. The were nudged in their corresponding directions. The maximum deviation is about 850 km at 0600 UTC 25 wavenumbers chosen in SN1 were i=3 and j=3 which September. In the spectral nudging experiments, i.e., correspond to spatial scales of 650×9 km/3(1950) km SN and SN1, the genesis time of simulated typhoon (L) in zonal direction and 500×9 km/3(1500) km in are about 36 h and 30 h respectively in advance as meridional directions. compared to the observation. Both the genesis locations of simulated typhoon (L) are in the southeast 4 RESULTS of the observation in SN and SN1. Compared to

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316 Journal of Tropical Meteorology Vol.21 CNTRL, the errors in the model-simulated tracks are period from 25 September to 3 October, are about small in SN and SN1. The maximum deviations from 155.9 km in SN and 292.1 km in SN1, respectively. It the observation are about 360 km at 0000 UTC 27 is clear that SN works better than SN1 in the track September in SN and about 440 km at 0600 UTC 30 simulation of Longwang. From Fig.5a, we can see that September in SN1, respectively. In the early stage, i.e., the genesis time and location of the simulated typhoon before 1800 UTC 27 September, the track of the (L) derived from FDDA are much better than that simulated typhoon (L) in SN1 is much closer to the obtained in CNTRL, SN and SN1. Moreover, the observation than that in SN. However, SN performs model-derived track of Longwang is very close to the better than SN1 in the track simulation after 1800 observation in FDDA. The maximum and mean UTC 27 September. The mean errors in the deviation of the simulated track from the observation model-derived tracks of Longwang, averaged in the are about 230 km and 86.5 km in FDDA, respectively.

Figure 5. (a) Tracks of Longwang derived from the best-track data (solid line with solid circle), CNTRL (hollow circle), SN (hollow square), SN1 (dashed line with hollow circle) and FDDA (solid square). The time marked in the figure represents the formation time of Longwang in the best-track data and simulations. Each symbol is plotted every 24 hours. (b) Time evolution of the maximum 10-m wind speed (unit: m/s) and minimum sea-level pressure (unit: hPa) of Longwang derived from the best track data and the four experiments.

Figure 5b shows the time evolution of the observation in SN and SN1. Although the maximum minimum sea-level pressure and 10-m maximum wind intensity of the simulated typhoon (L) is a little speed of Longwang derived from the best-track data weaker in SN than that in SN1, the time evolution of and the numerical simulations. From the image, it can the storm intensity described in SN is more in phase be found that, consistent with the early occurrence of with the observation than that in SN1. Contrary to the genesis, the intensification of simulated typhoon (L) appropriate simulation on the track of Longwang, was advanced to 0000 UTC 20 from 0600 UTC 25 in FDDA fails to simulate the development of Longwang CNTRL, which is about 5 days in advance. Compared properly. The storm obtained in FDDA only reaches to the observed maximum 10-m wind speed of 55 ms-1, the intensity of tropical storm. the intensity of the simulated typhoon (L) is a little Horizontal wind fields for typhoon Longwang on weaker in CNTRL, in which the maximum 10-m wind 29 September are presented in Fig.6. For evaluation speed is about 50 ms-1 (Fig.5b). As compared with the purpose the reanalysis data were used: NCEP CFSR intensity evolution of Longwang obtained in CNTRL, reanalysis data (with horizontal resolution 0.5° the intensifying and decaying episodes of the (~55km)). In Fig.6a, it shows the structure of simulated typhoon (L) are more consistent with the Longwang with a tiny eye and the eye wall around the

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No.4 LI Jing (李 静), TANG Jian-ping (汤剑平), et al. 317 center. Compared to the reanalysis, the CNTRL case are obvious. Compared to the CFSR, the size of clearly shows the eye and the eye wall but a larger simulated typhoon (L) seems to be smaller in SN and size (i.e. the size of the 10 ms-1 wind circle) for the slightly larger in SN1. In FDDA, the characteristic of simulated typhoon (L). As can be seen, for the spectral wind field is much similar to the reanalysis, but the nudging cases, the characteristics of eye and eye wall intensity is too weak.

Figure 6. Horizontal 10-m wind speed fields (ms-1) for typhoon Longwang on 29 September derived from (a) CFSR, (b) CNTRL, (c) SN, (d) SN1 and (e) FDDA. 10 ms-1 are contoured with solid line.

Based on the above analysis on the track and the intensity of low pressure systems. With regard to intensity of the simulated typhoon (L), conclusions the 500-hPa geopotential field, Fig.7b-7n (the second can be drawn as follows: The nudging methods help column) indicates that both the intensity and position the numerical model to simulate the genesis and track of a subtropical high pressure over the northwest of Longwang properly but cause the numerical model Pacific are properly simulated by SN, SN1 and FDDA to underestimate the intensity of Longwang. while CNTRL underestimates the intensity of the Considering all the factors, including genesis, track subtropical high pressure near the southeast coast line and intensity evolutions, the numerical model using a of China and derives a low pressure center in the area spectral nudging with appropriate nudging with 15°-25°N and 125°-145°E which is absent in the coefficients, i.e., SN, seems to do the best job in the reanalysis data and other experiments. Fig.7b-7n also simulation of Longwang. shows that the mid- and lower-level vapor conditions 4.2 Large-scale fields derived from the numerical described by the experiments are qualitatively experiments consistent with the reanalysis though the relative Since the large-scale background fields usually humidity over the Northwest Pacific is underestimated - play a vital role in the TC formation, evolution and a little bit. In the upper troposphere, Fig.7c 7o (the movement, the model-simulated environmental third column) shows that the geopotential field circulations in which Longwang was embedded are obtained in SN is the one closest to the reanalysis data investigated in this section with the intention to among the four experiments. The areas enclosed by understand the improvement achieved in the the iso-geopotential height of the value of 12 480 gpm numerical simulations of Longwang with the nudging are considerably enlarged in the other experiments. method used. To quantitatively evaluate the performances of 4.2.1 LARGE-SCALE CIRCULATION PATTERNS the numerical experiments in the large-scale Figure 7 displays the mean large-scale fields environment in the evolution of Longwang, the derived from NCEP/NCAR reanalysis data and numerical fields derived from the simulations are numerical simulations in the period from 0000 UTC interpolated to the same grid as the reanalysis data and 18 September to 1800 UTC 3 October. From then the root mean square errors (RMSE) of the Fig.7a-7m (figures in the first column) we can see that simulated fields are calculated. Fig.8 shows the time the patterns of the sea-level pressure and near-surface series of RMSE for sea-level pressure, near-surface wind speed derived from the four numerical zonal and meridional wind, geopotential height at 500 experiments are generally similar to that of reanalysis and 200 hPa. It is evident that the growth of RMSE is data except that there are two more low pressure evident in CNTRL but considerably suppressed in the centers appearing in the south of 20°N in the experiments with nudging methods. Table 1 gives the numerical simulations and the near-surface wind pattern correlation (COEF) and RMSE derived from speed near the two centers are stronger than that the simulation in the whole longwang’s lifetime. It can derived from the reanalysis data. These differences are be found that the COEF is the highest (lowest) and the partly related to the fact that the grid-spacing of the RMSE is the smallest (biggest) in FDDA (CNTRL). reanalysis data (one degree) is too coarse to describe Besides, the COEF is higher and the RMSE is smaller

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318 Journal of Tropical Meteorology Vol.21 in SN than those in SN1. From Fig.8 and the values of simulation of the atmospheric flows in the model RMSEs and COEFs summarized in Table 1, we can domain, followed in turn by SN1 and CNTRL. see that, SN and FDDA do the best job in the

Figure 7. The mean (a) sea-level pressure (contour, units: hPa) and 10-m wind speed (dashed contour, units: ms-1), (b) 500-hPa geopotential height (contours, units: dagpm) and low- to middle-level (1 000 to 500 hPa) relative humidity (dashed) and (c) 200-hPa geopotential heights in the period from 0000 UTC 18 September to 1800 UTC 3 October. (a-c) NCEP/NCAR reanalysis; (d-f) CNTRL; (g-i) SN; (j-l) SN1; (m-o) FDDA.

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Table 1. COEF and RMSE derived from numerical simulations in the whole Longwang’s life from 0600 UTC 25 September to 0600 UTC 3 October. VAR sea-level U10 V10 hgt at 500 hPa hgt at 200 hPa pressure RMSE COEF RMSECOEF RMSE COEF RMSE COEF RMSE COEF CNTRL 5.17 0.72 4.79 0.75 5.78 0.72 31.28 0.71 25.59 0.96 SN 1.03 0.98 2.29 0.91 2.41 0.89 13.50 0.98 15.12 0.98 SN1 2.11 0.93 3.06 0.86 3.60 0.81 13.80 0.94 23.61 0.98 FDDA 1.01 0.98 2.16 0.91 2.30 0.90 14.22 0.98 20.17 0.98

of the steering flow in CNTRL became stronger than the reanalysis. Although the meridional component of the steering flow in CNTRL has the same variation as the reanalysis, its direction is opposite to the reanalysis in the period from about 0600 UTC 28 to 0000 UTC 30 September and from 1800 UTC 30 September to 1800 UTC 1 October (Fig.9b). The mean deviation of the model-derived zonal and meridional components of the steering flow from that derived from the reanalysis data are about 0.93 ms-1 and 0.73 ms-1 in CNTRL, respectively. In contrast, the mean deviation of the model-derived zonal and meridional components of the steering flow is only about 0.18 ms-1 (0.26 ms-1) and 0.3 ms-1 (0.27 ms-1) in the experiment with spectral nudging, i.e., SN (SN1), respectively. The steering flow derived in FDDA is the closest to that derived from the reanalysis in the four experiments with the deviation of the zonal and meridional components from the reanalysis being 0.16 ms-1 and 0.24 ms-1, respectively. Since the track of TCs is mainly controlled by the large-scale steering flow, the proper (poor) simulation on the steering flow in FDDA (CNTRL) leads to the best (worst) track simulation of Longwang. Although the steering flow simulated in SN and SN1 is not as good as that in FDDA but is much better than that in CNTRL. Correspondingly, the tracks of the simulated typhoon (L) in SN and SN1 are closer to the observation than

Figure 8. Time series of RMSE about (a) sea level pressure, (b) CNTRL. near-surface zonal wind, (c) near-surface meridional wind, Figure 10 displays the vertical wind shear and (d)-(e) geopotential height at 500 and 200 hPa. vapor condition obtained from the reanalysis data and the four experiments in the most lifetime of 4.2.2 STEERING FLOW, VERTICAL WIND SHEAR AND Longwang. In CNTRL, it can be found that the MOISTURE SUPPLY vertical wind shear is generally larger than that The steering flow, vertical wind shear and derived from the reanalysis data. The distinct moisture supply are the key factors that influence the deviation of the shear from the reanalysis appeared in motion and development of TCs. Fig.9 displays the three periods, i.e., from 0600 UTC 25 to about 1200 environmental steering flow derived from the UTC 26, from 0600 UTC 29 to 1800 UTC 30 reanalysis data and the four experiments. In CNTRL September, and from 0000 UTC 31 September to (Fig.9a), the zonal component of the steering flow is 1200 UTC 1 October. In the first period the shear got relatively weak as compared to the observation before its peak value and 4 ms-1 greater than that of 1800 UTC 28 September and the maximum deviation -1 reanalysis. Corresponding to the strong shear, the was as large as ~2.2 ms at 0600 UTC 26 September. simulated typhoon (L) did not intensify in this stage in From 0000 UTC 29 September, the zonal component CNTRL (Fig.5b). Due to the strong shear, the

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320 Journal of Tropical Meteorology Vol.21 simulated typhoon (L) weakened quickly in the late Furthermore, the vapor increased from 1800 UTC 28 two periods in CNTRL. The mean vapor is about 60% September, which was about 2 days in advance than but less than 72% in the reanalysis in CNTRL. that in the reanalysis.

Figure 9. Time series of (a) the zonal steering and (b) the meridional steering obtained from the observation and the four experiments. Solid line: the observation; dotted line: CNTRL; gray dashed dotted line: SN; dashed line: SN1; gray line: FDDA.

Figure 10. Time series of (a) vertical wind shear and (b) 1 000 to 500 hPa relative humidity derived in the observation and vv v v simulations. Vertical wind shear is defined as (VV− ), where V and V are the horizontal wind averaged in the 200hPa 850hPa 200hPa 850hPa domain from 15 to 30°N and 110 to 155°E. The low- to middle-level (1 000 to 500 hPa) relative humidity is averaged over the same area.

Contrast to CNTRL, the vertical wind shear in simulated typhoon (L), it is obvious that the the experiments with nudging was more consistent application of nudging technique improved the ability with that derived from the reanalysis data (Fig.10a). of the models to describe the key factors that affect The value deviation between the shear derived from the changes of TC intensity. However, only the experiments with nudging and reanalysis is about 0.9 experiments with spectral nudging, i.e., SN and SN1, ms-1 (SN), 1 ms-1 (SN1) and 0.9 ms-1 (FDDA), described the intensification of Longwang better than respectively, which are smaller than the deviation CNTRL. The simulated typhoon (L) in FDDA failed between the shear obtained in CNTRL and reanalysis to intensify though the environmental vertical wind data (1.5 ms-1). In addition, the variations of shear shear and moisture derived in FDDA is the most derived from experiments with nudging were consistent with the reanalysis data. This is ascribed to generally in phase with the reanalysis, especially the fact that the traditional nudging applied in FDDA before 29 September. For the vapor condition, all the not only modified the large-scale fields but also experiments with nudging underestimate the vapor adjusted the meso- to small-scale fields that produced (see Fig.10b, as also mentioned in Fig.7b) but the via model. variations of the low- to mid-level relative humidity 4.2.3 SIMULATION ON TYPHOON SAOLA were in phase with the reanalysis data. From Fig.10b, As mentioned in Section 2, typhoon Saola that it is obvious that FDDA does the best job in formed at 0000 UTC 19 September played an describing the environmental vapor condition in all important role in the formation of Longwang. The experiments and the moisture condition of Longwang ability of the model to simulate Saola determined is better in SN than that in SN1 after 0000 UTC 28 whether the formation of Longwang can be properly September. simulated. From the above analysis on the environmental Figure 11a displays the track of Saola simulated vertical wind shear and moisture condition of in the four experiments. In CNTRL, Saola formed at

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No.4 LI Jing (李 静), TANG Jian-ping (汤剑平), et al. 321 around 17°N, 155°E by about 1200 UTC 22 and 427 km at 1800 UTC 20 September, respectively. September, which deviates by about 3°N from the The mean deviations averaged in the whole lifetime of best-track and postpones by as long as 3 days from the model-derived typhoon (S, from 0000 UTC 20 observation. As far as the track is concerned (Fig.11a), September to 1200 UTC 26 September) in SN (SN1) it is very unsatisfactory in CNTRL which deviates from the best-track are about 104 km (238 km), which much from the observation with its mean track error indicates that SN performs better than the weak as much as 1 650 km. In the spectral nudging spectral nudging (SN1) on the track of Saola. In experiments, i.e., SN and SN1, the genesis time of FDDA, the genesis time of the simulated typhoon (L) model-derived typhoon (S) is much improved that is consistent with that in SN (SN1) with about 1 day they are both just 1 day behind the observation while behind the observation and the genesis location of the genesis locations in SN and SN1 are southwest of model-derived typhoon (S) is northwest of the the observation. Moreover, the genesis location in SN observation. Moreover, the track of model-derived is closer to the observation than that of SN1. typhoon (S) is also very close to the observation in Compared to CNTRL, the error of simulated-track in FDDA. The maximum and the mean derivation from SN (SN1) from that in observation is relatively small. the observation are about 297 km and 148 km in The maximum errors of simulated-track in SN and FDDA, respectively. SN1 are about 228 km at 0000 UTC 20 September

Figure 11. Similar to Fig.5 except for typhoon Saola.

Figure 11b displays the intensity evolution of the better job than SN1 on the intensity of the model-derived typhoon (S). From the image, we can model-derived typhoon (S) with stronger intensity. find that the initial intensification of the Contrast to proper performance on the track of Saola, simulated-typhoon in CNTRL is postponed by about 3 the maximum 10-m wind speed of the typhoon days, from 0000 UTC 19 September to 1200 UTC 22 simulated in FDDA is only 25 ms-1, which is too weak September, which is consistent to the lagged genesis as compared with the observation. time as mentioned above (in Fig.11a). Compared to From the analyses mentioned above, we can the observation with the maximum 10-m wind speed determine a similar conclusion that the models with of 45 ms-1, CNTRL overestimates the simulated spectrum nudging methods could describe Saola typhoon (L) with the maximum intensity of ~48 ms-1. properly since the nudging method could improve the In SN and SN1, although they underestimate the track and intensity evolution of the simulated typhoon intensity of Saola, the intensifying and decaying time (L). Besides, the improvement on both the track and of the simulated typhoon (L) are much closer to the intensity evolution of the simulated typhoon (L) is observation than that in CNTRL. It can be found that more significant in SN than that in SN1 and FDDA. the of model-derived typhoon (S) As mentioned in section 2, Longwang generates is lagged by about 18 h (42 h) from 1200 UTC 21 in the southeast side of Saola and the Rossby wave September (1200 UTC 21 September) to 0600 UTC activated by Saola plays an important role in genesis 22 September (0600 UTC 23 September) in SN (SN1), of Longwang. It is necessary to evaluate the role of which means that the evolution of the model-derived the genesis of Longwang in simulations. Fig.12 shows typhoon (S) in SN is more in phase with the the 850-hPa horizontal streamlines and relative observation that that in SN1. Besides, SN does a vorticity obtained from the four experiments at 0600

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322 Journal of Tropical Meteorology Vol.21 UTC 23 September 2005. In CNTRL, an active (Fig.5a), which is in advance of Saola, and the typhoon, i.e., Longwang, is located around 20°N, opposite relative location between Saola and 142°E with a large cyclonic center with positive Longwang, we speculated that the CNTRL could not vorticity (Fig.12a). It can be found that several reproduce the real genesis process of Longwang. vorticity centers developed in the southeast side of Moreover, the binary interaction between Saola and Longwang and the larger one belonged to Saola and Longwang in CNTRL is absent in the observation. formed at 1200 UTC 22 September in CNTRL, which Compared to CNTRL, the two cyclonic centers was 3 days lagged behind the observation. To find represented by the letter S and L are where the initial disturbance of the simulated typhoon northwest-southeast in SN, SN1 and FDDA (L) comes from, a 3- to 8-day bandpass filtering (Figs.12b-12d). From Figs.12b-12d, it can be drawn 850-hPa winds are studied in the periods of their that the initial disturbance of the simulated typhoon (L) initial genesis in simulations (Fig.13). In CNTRL, generated in the southeast of the cyclonic circulation after the simulated typhoon (L) formed, the Rossby of the model-derived typhoon (S), which is consistent wave, a cyclonic-anticyclonic-cyclonic circulation with the observation (Fig.1b). As the 3- to 8-day like that in Fig.2, does not appear in the southeast side bandpass of 850-hPa wind fields is concerned of the simulated-Longwang, and a cyclonic (Figs.13b-13d), it can be found that, consistent to the disturbance develops into a tropical depression, i.e., observation (Fig.2d), the cyclonic, anticyclonic and Saola, which is then affected through binary cyclonic circulations appeared in the wake of Saola interaction with Longwang (Figures not shown). From and Longwang generated in the cyclonic circulation, Fig.13a, it can be found that the relative location which means that the Rossby wave train activated by between Saola and Longwang turns to the the simulated typhoon (S) would play the same role in northeast-southeast from the northwest-southeast the formation of the simulated typhoon (L) in SN (Fig.12a). Considering the above mentioned results (SN1) and FDDA. that Longwang formed at 0000 UTC 19 September

Figure 12. The 850-hPa horizontal streamlines and relative vorticity (colored every 2×10-5 s-1) obtained from (a) CNTRL, (b) SN, (c) SN1 and (d) FDDA at 0600 UTC 23 September 2005. The letters “L” and “S” represents typhoon Longwang and Saola respectively.

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No.4 LI Jing (李 静), TANG Jian-ping (汤剑平), et al. 323

Figure 13. The 3- to 8-day bandpass-filtered 850-hPa wind fields and the 850-hPa wind speed in (a) CNTRL, (b) SN, (c) SN1 and (d) FDDA at 0600 UTC 25 September 2005. The letters “S” and “L” denote the location of Saola and Longwang respectively.

From the above evaluations on the ability of conducted in this work via four experiments, i.e., the simulating Saola, it can be found that the application control experiment without any nudging method of the nudging method helps the model to improve the (CNTRL), two spectral nudging experiments, i.e., SN ability to describe Saola properly on both the track with stronger spectral nudging and SN1, and a and the intensification. Moreover, the nudging traditional nudging method (FDDA). methods play an important role in the formation of In our experiments, the spectral nudging simulated typhoon (L) since they help the model to experiments, i.e., SN and SN1, simulate the genesis reveal the genesis process in simulations. The and track of Longwang properly but underestimate its improvement on the track and the intensification of intensity. Although FDDA gives successful genesis simulated typhoon (S) is more significant in SN than and track of Longwang, it fails to reflect its intensity, in SN1. For the FDDA case, it performs better (worse) which is too weak. In the spectral nudging on the track of Saola than SN1 (SN), it also fails to experiments, the time evolution of the storm intensity describe the intensity of the simulated typhoon (S), described in SN is more in phase with the observation which is too weak. than that in SN1. Therefore, in terms of the genesis, track and intensification of simulated typhoons, the 5 CONCLUSIONS SN with appropriate spectral nudging coefficients does the best job. In this study, in order to evaluate the To understand the improvement achieved in the performance of spectral nudging in the numerical numerical simulations of Longwang with nudging simulation of TCs that evolve in an environment with methods, large-scale fields in simulation are analyzed. complicate circulations different from the simple It can be found that the application of the nudging circulation with only one TC in previous studies, the technique helps improve the ability of models to numerical high-resolution simulation of typhoon describe the atmospheric flow in model domains. The Longwang (2005) using the mesoscale model WRF is improvement in SN and FDDA is considerable and

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324 Journal of Tropical Meteorology Vol.21 slightly better that in SN1. Besides, the vital factors, REFERENCES: including the steering flow, environmental vertical shear and moisture supply, which may influence the [1] CHEN H, ZHANG D L, CARTON J, et al. On the rapid motion and intensification of Longwang, are also intensification of Hurricane Wilma (2005). Part I: Model evaluated. The large-scale steering flow is properly prediction and structural changes [J]. Wea Forecast, 2011, 26(6): 885-901. described in all of the experiments due to the nudging [2] MENELAOU K, YAU M K, MARTINEZ Y. On the methods; correspondingly, all the experiments except dynamics of the secondary eyewall genesis in Hurricane Wilma CNTRL describe the track of typhoon properly since (2005) [J]. Geophys Res Lett, 2012, 39(4), L04801, the track of typhoon is mainly controlled by the doi:10.1029/2011GL050699. large-scale steering. Compared to the spectral nudging, [3] LIN Wen-shi, WU Jian-bin, LI Jiang-nan et al. 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