12th International Symposium on Turbulence and Shear Flow Phenomena (TSFP12)
Osaka, Japan (Online), July 19-22, 2022

LES-based generation of time sequential data of instantaneous urban wind in typhoon-comparison with observational data

Masaharu Kawaguchi
Department of Architecture and Building Engineering
Tokyo Institute of Technology
Ookayama 2-12-1, Meguro, Tokyo, 152-8550, Japan
kawaguchi.m.ag@m.titech.ac.jp

ABSTRACTIn this paper, LES of urban area was performed using inflow
turbulence with meteorological disturbance of strong typhoon
Hagibis (2019) and the simulated turbulent characteristics arecompared with the field measurement data. The method isfurther applied to the analysis of the wind pressure on a building
in a densely built-up area.

Tetsuro Tamura
Department of Architecture and Building Engineering
Tokyo Institute of Technology
Ookayama 2-12-1, Meguro, Tokyo, 152-8550, Japantamura.t.ab@m.titech.ac.jp

Firstly, a meteorological model simulation was performed
to replicate the detailed structure and movement of the typhoon.
Secondly, high frequency components attenuated in themeteorological field were regenerated using the meteorologicalmodel/engineering LES hybrid method (Kawaguchi, Tamura etal., 2019). Finally, the LES of the target urban area was carriedout by an LES with the turbulent inflow obtained in the previous
simulations.
Typhoons Hagibis (2019) was simulated using Japan
Meteorological Agency Nonhydrostatic Model (JMA-NHM;
Saito et al., 2006). Triply nested domains with resolutions of 1
km, 250 m, 50 m were employed as shown Figure 2. Grid
resolution and other configurations of domains are descrived inTable 1. The initial time of the outermost domain was set to
15:00 on October 12, 2012, in Japan Standard Time (JST). Initialand boundary conditions of the outermost domain were provided
with the JMA mesoscale analysis. For cloud microphysics, abulk-type scheme that predicts the mixing ratios of six water
species was utilized. In all three domains, subgrid turbulent
mixing is treated using a 1.5-order turbulent kinetic energy
closure scheme.

BACKGROUND
Strong tropical cyclones, such as typhoons and hurricanes,
strike cities in the temperature zone more frequently in recent
years and raised great concerns for urban environment safety. It
is important to accurately simulate velocity variability in densely
built-up areas and evaluate the peak value of wind pressure
acting on buildings in such extreme weather events. For
assessing wind resistance design of buildings, more realistic
replication of actual atmospheric conditions including detailed
flow structures of severe storms is anticipated. Previously,
Kawaguchi and Tamura et al. (2021) performed a large eddy
simulation (LES) of the urban flow field in the significant
Typhoon Jebi (2018), and the influence of the meteorologicalfield on the formation of near-ground gusts was analyzed. This
paper aims to compare turbulence characteristics reproduced in
the LES analysis using inflow with meteorological disturbanceto the surface observational data in the case of the strong
Typhoon Hagibis (2019) and apply further to evaluate the windload on an actual building in the urban area Shibuya.

METHODTo perform a LES of an urban area cusing the inflow
turbulence based on the meteorological model simulation, threedifferent simulations were conducted in series as shown Figure
1.

Figure 2. Calculation domains.
Table 1. Domain configuration of JMA-NHM.
Domain
D1
D2
D3

Dimensions[grid points]
1601*1601*92
1601*1401*100
1101*1781*100

Horizontal grid
spacing [m]
1000
250
50

Time step [s]
3
1
0.25

In order to enhance the fine turbulent flow structuresattenuated in the meteorological model, two sets of LES for
turbulence regeneration were executed at 25-m resolution

Figure 1. Framework of the hybrid
meteorological/engineering LES method.

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12th International Symposium on Turbulence and Shear Flow Phenomena (TSFP12)
Osaka, Japan (Online), July 19-22, 2022
focusing for each urban area of the subsequent simulation. The
dimension of the calculation domains is 12-km (E–S)*7-km (N–S)*2.7-km (vertical). Numerical schemes and other calculation
conditions were listed in Table 2.

and immersed boundary method to perform hyper large parallel
computation in an optimum load balance.

(a)Figure 3. Domains of the turbulence regeneration calculation(Orange boxes) The red boxes correspond to the domains ofsubsequent LES of the urban areas.
(b)
Table 2. Calculation conditions for turbulence
regeneration.
Domain dimensions[grid points]
Horizontal grid spacing [m]Vertical grid spacing [m]Calculation period
Time step [s]Time integration

Spatial discretization

Pressure solver

480 (N-S) *280 (E-W) *60(vertical)
25
18.1–70.5
20:48:00 – 21:00:00 (JST)0.02s
2nd order Adams–Bashforth(for advection term)
1st order Euler explicit(for other terms)
4th order central difference(for velocity)
2nd order central difference(for equivalent potential
temperature)
SOR(c)Figure 4. Surface models for LES of urban area.
(a) Case 1, Otemachi (White dot indicates the observationsite). (b) Case 2, Shibuya. (c) Case 3, Shibuya, detailed
analysis.
GENERATION OF THE METEOROLOGICALDISTURBANCE INFLOW
Figure 5 shows the actual and simulated typhoon tracks andcentral pressure. Simulated typhoon roughly followed the actual
track and made the closest approach to the target area about thesame time, around 21:00. Although the track had been improved
from the preliminary cases where the initial time was set earlier,it shifted about 40-50 km to the northwest. The simulated centralpressure was slightly lower than the actual case during thetyphoon’s closest approach to the target area. In order to improve
the reproduction of track and intensity and consider uncertaintyof the event, the use of ensemble analysis may be effective.
Figure 6 shows the velocity fields at different altitudes at theclosest approach time. The meteorological model simulation
successfully reproduced cumulonimbus and finer wind speed
fluctuations based on longitudinal vortices in the typhoonboundary layer. Bands of high and low wind speeds wereapparently aligned in the wind direction at intervals of several
kilometers.
Using the method of Kawaguchi and Tamura et al. (2019),
high frequency component was regenerated to themeteorological field. The visualization of the horizontal crosssection and the time history in Figure 7 shows that fine turbulent
structures were generated based on the meteorological field
through the turbulence energy cascade.

The LES of the actual urban areas during the closest
approach of Typhoon Hagibis was performed for two districts inTokyo: Otemachi district, where the observation site of JMA
Tokyo Regional Headquaters (Tokyo RHQ) is located, andShibuya district, which is a densely built-up area.
For each case, the calculation domain was set to have adimension of 6 km*3 km*1.5 km, and the minimum resolution
at 1.5 m. For Case 2, the central part of Shibuya district(1.9km*1.9km*0.95km) was further one-way nested to a domain
with 93-cm resolution grids and separately simulated forevaluation of wind load on the target building. Figure 4 shows
the calculation domains of Case 1 (Otemachi), Case 2 (Shibuya),
and Case 3(Shibuya, nested). The number of computational grids
were 0.72 billion, 0.59 billion, and 0.35 billion, respectively.
Timestep increment was set to 0.005 s. The simulation was
performed for the period between 20: 48 and 21:00 (2 min forfield initialization and 10 min for analysis).
The realistic turbulent flow in the typhoon obtained in theprevious steps was used for the initial field and the inflow dataat the south, east, and west boundaries. CUBE developed by
RIKEN R-CCS was used for the numerical solver. The solver
incorporates a multi-block hierarchical Cartesian grid system212th International Symposium on Turbulence and Shear Flow Phenomena (TSFP12)
Osaka, Japan (Online), July 19-22, 2022
COMPARISON OF THE NEAR-GROUNDTURBULENCE WITH THE OBSERVATIONAL DATA
Near-ground turbulence in Case 1 was compared withobservational data at Tokyo RHQ during the closest approach of
Typhoon Hagibis. The location of the observation site isindicated in Figures 4(a).
Figure 8 shows instantaneous velocity fields at 300m above
sea level (ASL), 200 m ASL, and 56 m ASL. (The elevation at
Tokyo RHQ is about 20 m.) Turbulent structures shown in
Figure 7 intruded over the urban area. Wake regions of high-risebuildings is extended downward at a considerable extent,
making bands of wind speed fluctuation. Local variation of thewind direction is evident near the ground.
Figure 9 shows the time history of the simulated wind speed
and the spectra of horizontal wind speed at Tokyo RHQ, at thealtitudes z = 300 m and 40 m. The waveform showed very fineand acute velocity fluctuations. The spectra reveal that
reproduced turbulence was in accordance with Kolmogorov's 5/3 power law up to approximately 0.5 Hz at an altitude of 300
m, and to frequencies above 1 Hz at an altitude of 40 m. The
low-frequency side of the spectrum maintained a large amountof energy, suggesting that a broadband turbulent inflow windwith meteorological disturbances was successfully obtained.
The wind profile at Tokyo RHQ and visualizations of
instantaneous velocity field in the vertical cross-sections areshown in Figure 10. The simulated wind profile was a powerlaw type, although it was observed that longitudinal vortices
locally and continuously transferred high winds to near-groundregion.
JMA now publicly releases their observational data
resampled in 10-s interval. In Figure 11, the simulated windspeeds were compared with the observational data at Tokyo
RHQ. The time series of 3-s maximum and minimum gustspeeds during 10 s of evaluation time and 10-s mean wind speed
are shown along with the time history of 10-s mean winddirection. From the velocity waveforms of the observation,
considerable variability of turbulent intensity or gust ratio intime exists and the influence in actual field. Especially, gust ratio
was signifi1cantly increased around velocity maxima. The
simulated time series also reproduced variability of turbulent
intensity, although it has somewhat different waveform and peak
pattern from those of observation. Fluctuation of wind direction
was evident in both of the simulation in the present method andthe observation. The waveform of the simulation contains many
cycles of fluctuation in the range of about 45 to 90 degrees.
Fluctuation amplitude of the observation was even larger.
Table 3 shows statistics of wind speed simulated andmeasured at Tokyo RHQ. The simulated mean wind speed wasabout 5 m/s higher than the observation, while the estimatedmaximum wind gust was not as high as the actual case.
The result show that the present method at current stagecannot always predict exact turbulence condition at a particularlocation and time during the actual extreme weather event. Yet,the near-ground flow field was successfully analyzed withappropriate inflow turbulence that reflects characteristics ofmeteorological field strong typhoon. Differences are most likely
to be attributed to the reproducibility of the event such as
intensity and track of the typhoon. Previous research reported
the difference of wind profiles (Franklin, 2003; Kepert andWang, 2001) and the near-surface coherent structures (Ito et al.,2017) depending on the distance from the tropical cyclone core.
Uncertainty of the turbulent phenomena and structure of
meteoritical field has also influence on the result.
The improvement of qualitative reproducibility of thephenomena and quantitative accuracy by utilizing the ensemble
analysis data will be the subject of future research.

Figure 5. Actual and simulated typhoon tracks and centralpressure of Typhoon Hagibis (2019)]

Figure 6. Velocity fields of Typhoon Hagibis (2019)
simulated with JMA-NHM. Result of Domain 1 at z=85 m
above ground level (AGL) (top). Result of Domain 3, at
z=146m AGL (lower left) and 30 m AGL (lower right).
(a)

(b)

Figure 7. Turbulence regeneration to the meteorological field.
(a) Velocity field of Case 2 at z=300 m AGL before (left) andafter (right) the turbulence regeneration. (b) Time series ofwind speed at z=430 m.

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12th International Symposium on Turbulence and Shear Flow Phenomena (TSFP12)
Osaka, Japan (Online), July 19-22, 2022
(a)

(b)

(c)

Figure 8. Instantaneous wind speed in the urban area ofOtemachi. (a) 300 m above sea level (ASL). (b) 200 m ASL.
(c) 56 m ASL.
(a)

Figure 10. Vertical profile at the observation point andvisualizations of instantaneous wind speed in the cross-sections
approximately perpendicular and parallel to wind direction.

(b)

Figure 9. Wind speed simulated at the observation site at z=40
m above ground level (AGL). (a) Time series of 3-s mean windspeed (dark blue) and instantaneous value (light blue). (b)Spectra of wind speed fluctuation at 300 m AGL (top) and 40m
AGL (bottom).
Table 3. Comparison of statistics of wind speed.
Mean Max 3-s
Turbulence
gust
intensity (10-smean wind)
LES (40m AGL)
22.4
34.0
16.4
Obs. (35.3m AGL) 17.8
41.5
26.9

Figure 11. Time series of horizontal wind speed and directionof Case 1. Simulation result (top), observational data (bottom).
Orange and blue lines show the 3-s maximum and minimum in
10 s of evaluation time. Dark gray lines 10-s averaged winddirection.

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12th International Symposium on Turbulence and Shear Flow Phenomena (TSFP12)
Osaka, Japan (Online), July 19-22, 2022
OCCURRENCE OF STRONG GUSTS IN THE HIGHLY
BUILT-UP URBAN AREA
In Case 2, the pattern of strong gusts occurrence wasanalyzed in a highly built-up urban area Shibuya. Figure 12
shows the distribution of mean and maximum instantaneouswind speeds within the urban area at 100 m ASL, 50 m ASL and
25m ASL. (The elevation at the target building is approximately
15 m); a similar tendency to the results in Osaka during Typhoon
Jebi (2018) was observed.
At 100 m ASL, numerous trajectories of high-speed gusts
were observed running downstream several hundred meters witha narrow width about 100 m. Many of the them were straight,
slightly deviating from the direction of the mean flow. Largerscale variation due to the flow structures of the meteorologicalfield seems to be superimposed.
At 50 m ASL, streaky strong wind patterns in more random
directions were observed and high wind were evident in open
areas and near the high-rise buildings.
At 25 m ASL, strong gusts were similarly prominent aroundhigh-rise buildings, but was further transported near the groundalong the downstream streets, including areas with relatively
small mean velocities.

ESTIMATION OF WIND LOAD ON THE BUILDINGIn Case 3, the wind pressure acting on the target building
was estimated by the analysis of downtown of Shibuya districtin the nested domain from Case 2 with 93-cm grid resolution.
Figure 13(a) shows the spatial distribution of wind pressure
coefficients on the target building in Figure 4(c). The averaging
time for peak wind pressure is 0.3 s. As the majority of thebuilding is situated in the wake region of the adjacent upstream
high-rise building, large positive pressure was exerted only on
the upper part of the high-rise towers. Negative peak windpressure was found at the front edge of the side wall f high-risebuilding near where the flow over the upstream building acted.
The time history of the pressure coefficient at point P, where
the maximum negative pressure was experienced, is shown in
Figure 13(b). The result with 93-cm grid (about 1/75th of thebuilding width) reproduced sharp pressure drop in time history.
The minimum value reached -2.7 at 411 s. The same result
obtained in Case 2 with a resolution of 1.5 m (approximately
1/47th of the building width) is presented in the same figure.
Analysis yielded similar fluctuation trends, but with muchsmaller amplitudes. Vortex generation is not captured clearly,
due to use of immersed boundary method in insufficient
resolution.
The time history shows that significantly strong negative
pressure was frequently occurred during a couple of minutes
before and after the minimum wind pressure. The time historyof wind speed at upstream from the target building shown in
Figure 13(c) reveals that it was coincided with the increase ofwind speed. The instantaneous wind speed in the cross-wisevertical section at 411 s shown in Figure 13(d) reveals that it wascaused by the flow structure of the meteorological field structure
which locally lowered at the location of the building.
Figure 13(e) shows the 3D iso-surfaces of pressure
coefficient -1, -2, -3 around the target building at 411 s. Strong
inverted conical vortex developed at about the height where theshear layer of the upstream building roof hit and it was stretchedin the wind shear in vertical direction.

100m ASL
50 m ASL
25 m ASL
Figure 12. Distribution map of mean wind speed (top row) and 3s maximum wind speed (bottom row) in the central area of
Shibuya in Case 2

Figure 13. Evaluation of wind loading. (a) Spatial distribution of pressure coefficient on the target building in Case 3. (b) Time series ofthe pressure coefficient at Point P obtained in Case 2 at 1.5m resolution(blue) and in Case 3 at 93-cm resolution (orange). (c) The time
history of wind speed at 300 m upstream from the target building. (d) Instantaneous wind speed in the cross-wise vertical section at 411 s(e) 3D iso-surfaces of pressure coefficient -1, -2, -3 around the target building at 411 s

512th International Symposium on Turbulence and Shear Flow Phenomena (TSFP12)
Osaka, Japan (Online), July 19-22, 2022
CONCLUSIONSIn this study, we performed LES of actual urban areas inTokyo, Japan, using the meteorological model/engineering LES
method to reproduce the event of approach of strong typhoon
Hagibis (2019) and compared the simulation result to the groundobservation of wind speed and direction.
In the innermost domain of the meteorological model
simulation at a resolution of 50 m, longitudinal vortex in thetyphoon boundary layer and fine wind speed fluctuation weresuccessfully generated. However, the track shifted 40-50 km to
the northwest, and the central pressure was slightly (~ 5hPa)
lower than the actual case. This result presented the difficulty inreplicating actual weather events with sufficient accuracy
necessary for a city-scale simulation by deterministic analysis.
LES analysis of an actual urban area (Otemachi, 6km* 3km)
was performed at a resolution of 1.5 m with inflow turbulence
replicating Typhoon Hagibis. The spectral analysis confirmed
the generation of broadband turbulence, following
Kolmogorov’s -5/3 power law, up to frequency higher than 1 Hz
at 40 m height. The low-frequency side of the spectrum
maintained a large amount of energy, which is expected to becaused by meteorological disturbances.
Comparison between the time histories of velocity of LES
analysis and observed turbulent wind in Typhoon Hagibis
indicated some qualitative similarities, such as considerable
variations of turbulent intensity and wind direction within 10
min of evaluation time, although qualitative reproducibility ofthe meteorological disturbance and quantitative estimation of
turbulence statistics were not satisfactory yet.
The discrepancy between the numerical simulation and theactual meteorological field can be primarily caused by thedifference of wind profile and coherent longitudinal vortex
structure, which varies depending on the distance from thetyphoon core. The track and intensity of track can be affectedsignificantly by the uncertainty in the meteorological model
simulation of initial and boundary conditions. Stochastic natureof turbulence and flow instability may also have some influence.
Further improvement will be sought through LES of urban
areas coupled with ensemble analysis that reproduces theuncertainties of the meteorological simulation.
Subsequently, LES of the highly built-up urban area,
Shibuya was performed at the 1.5-m resolution, and the patternsof strong gusts occurrence at different altitudes were presented.

Similarities to the prior study in the mid-rise urban area wereobserved.
Finally, the unsteady pressure acting on a high-rise building
was analyzed by LES in a nested domain from the above analysis.
Prediction of sharp negative peak pressure was significantly
improved at the 93-cm resolution (about 1/75th of the building
width), in comparison with the result at the 1.5-m resolution(about 1/47th of the building width).
ACKNOWLEDGMENTS
This work used computational resources of thesupercomputer Fugaku provided by RIKEN through the HPCI
System Research Projects (Project ID: hp210158, hp210262).
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