Takemi and Ito Progress in Earth and Planetary Science
https://doi.org/10.1186/s40645-019-0317-7(2020) 7:4

RESEARCH ARTICLE

Progress in Earth andPlanetary Science

Open Access

Benefits of high-resolution downscaling
experiments for assessing strong windhazard at local scales in complex terrain: acase study of Typhoon Songda (2004)
Tetsuya Takemi1*and Rui Ito2

Abstract
This study investigated the representation of surface winds in complex terrain during the passage of Typhoon
Sondga (2004) in downscaling simulations with the horizontal grid spacing of 200 m. The mountainous areas in
Hokkaido where forest damages occurred in the typhoon event were chosen for the present analysis. The 200 m
grid simulations were compared with the simulations with the grid spacing of 1 km. The 200 m grid simulations
clearly indicated more enhanced and more frequent extremes both in the stronger and weaker ranges of surface
winds than the 1 km grid case. Both in the 200 m grid and 1 km grid cases, the mean and maximum winds in theanalysis areas during the simulated time period increase with the increase in the terrain slope angle, but in the 200m grid case, the relationships of the mean and maximum winds against the terrain slope angle includes wide
scatter. In this way, the response of the wind representations to the grid spacing appears differently between the
200 m and 1 km grid cases. A parameter characterized subgrid-scale orography was used to quantify the influencesof the terrain complexity on surface winds, demonstrating that the area-maxima and spatial variance of surface
winds are more enhanced with the increase in the subgrid-scale orography in the higher-resolution case. It issuggested that the high-resolution simulations at the 200 m grid highlight the fluctuating nature of surface winds
in complex terrain, because of the better representation of the model terrain at 200 m. Benefits of the representation of
surface winds in simulations at the resolution on the order of 100 m are due to the better representation of complex terrain,
which enables to quantitatively assess the impacts of strong winds on forest and natural vegetation in complex terrain.
Keywords: Dynamical downscaling, Strong wind, Typhoon hazard, Impact assessment, Mesoscale meteorological model

Introduction
Dynamical downscaling is a useful approach in weather
and climate researches in order to assess the impacts ofweather disturbances and extreme weather phenomena
at mesoscales and local scales. Because deep cumulus
convection plays a critical role in spawning severe weather such as heavy rainfalls and strong winds, a horizontal grid spacing that can adequately resolve the activities
and effects of deep convection is required to better represent severe weather phenomena in numerical simulations. As an example of operational numerical weather* Correspondence: takemi@storm.dpri.kyoto-u.ac.jp
1
Disaster Prevention Research Institute, Kyoto University, Gokasho, Uji, Kyoto
611-0011, Japan
Full list of author information is available at the end of the article

prediction models, the Japan Meteorological Agency is
now conducting regional weather forecasting at the 5
km and 2 km grid spacings. A numerical model with thegrid spacing on the order of 1 km was considered to bea cloud-resolving model (CRM) (Grabowski et al. 1996),
and Weisman et al. (1997) showed that the nonhydrostatic effects of convection are properly represented at such resolutions. On the other hand, Bryan
et al. (2003) demonstrated that the grid spacing on theorder of 100 m is necessary to adequately resolve turbulent behaviors of convective motions, and Takemi andRotunno (2003) indicated an improper spatial filtering insimulating convective clouds at the grid spacing on theorder of 1 km from a subgrid-scale-modeling point of© The Author(s). 2020 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made.

(2020) 7:4

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view. Thus, the grid spacing of 1 km may not be sufficient to represent convective motion.
From a climate research point of view, Kitoh et al.
(2016) emphasized that the importance of high-resolution
downscaling in representing extremes lies in reproducing
well-resolved topography in numerical models. Takayabu
et al. (2016) also listed high-resolution topography as oneof the factors that add values to dynamical downscaling.
However, most of the previous studies employed grid
spacings on the order of 1 km, and the numerical representations at the grid spacing of 100 m have not been well
explored. This study deals with the issue on the benefits
or added values of dynamical downscaling by conducting
a higher-resolution grid spacing on the order of 100 m.
The representation of terrains is critically important innumerically reproducing the spatial and temporal variability of surface winds (Jimenez et al. 2008; Bonnardot
and Cautenet 2009; Jimenez et al. 2010; Jimenez andDudhia 2012). By using the Weather Research and Forecasting (WRF) model, Jimenez and Dudhia (2012) foundsystematic overestimations of surface wind speeds in valleys and underestimations at mountain tops. This result
suggests the importance of terrain representations inwind simulations. Oku et al. (2010) investigated the representations of surface winds during a typhoon passage
by comparing two different mesoscale meteorologicalmodels with the grid spacing set to 1 km and found that
more accurately reproduced terrain features even at thesame grid spacing result in capturing more highly variable natures of surface winds. Takemi (2013) conductedregional-scale meteorological simulations of surface
winds in complex terrain and demonstrated that thefine-scale spatial variability of winds is reproduced betterin the 400 m grid simulation than in the 2 km grid simulation, notifying that the influences of the complex terrain can be found for winds up to the 200 m height
above the ground level. In this way, the representation ofterrains requires special care in simulating airflows especially in complex terrain.
Strong winds are one of the major meteorological hazards in places with complex terrain features. In 2018, amajor typhoon, called Typhoon Jebi (2018), landed in
the central part of Japan and spawned severe damages
induced by strong winds not only in urban areas but also
in mountainous areas (Cabinet Office 2018). Typhoon
Jebi (2018) produced extreme winds in a business district of Osaka City which is characterized by complex
geometrical features of building distribution and height(Takemi et al. 2019). Much severer winds are considered
to have occurred in mountainous areas, because of theirhigher elevations and more complex terrain features;such severe winds led to serious damages to trees, forest
ecosystems, power lines, etc. In the past, major typhoons
caused severe damages to trees and forest ecosystems inmountainous areas. For example, due to Typhoon
Songda (2004), widespread forest damages occurred in
Hokkaido (Sano et al. 2010; Hayashi et al. 2015; Takano
et al. 2016; Morimoto et al. 2019). Because major damages to trees by typhoon’s winds have serious influences
on forest ecosystems for a long period of time, assessing
the impacts of typhoon winds on forest areas and identifying their climate change factors are critically important(Ito et al. 2016; Takemi et al. 2016a; Toda et al. 2018).
In this sense, simulating airflows in complex terrain is ascientific challenge.
This study investigates the representations of surface
winds in complex terrain in numerical simulations at the
200-m grid spacing. By further downscaling from 1 km to
200 m under the experimental setups of Ito et al. (2016)
who examined strong winds due to Typhoon Songda(2004), we explore the benefits of high-resolution numerical simulations of airflows in complex terrain. Identifying
the benefits or added values in high-resolution modelingis a key to quantitatively assess the impacts of typhoon
winds at local scales.

Takemi and Ito Progress in Earth and Planetary Science

Numerical model and experimental design
The model used in this study was the WRF model, theAdvanced Research WRF (ARW) version 3.6.1 (Skamarock et al. 2008). The model was configured in two-way
nested computational domains: the outermost domain(D01) covers the Japanese islands and the surrounding
areas in the western North Pacific at the 9 km horizontal
grid spacing, downscaled to the area (D02) covering thenortheastern part of Japan at the 3 km grid, to the Hokkaido area (D03) at the 1 km grid, and finally to theareas (D04) having the 200 m grid (Fig. 1). For the D04
areas, eight domains were separately placed within D03
in order to save computational costs. The reason why
we set these specific regions as D04 is that in these D04
areas, significant damages to forests were reported(Takano et al. 2016; Morimoto et al. 2019) and hence
evaluating the numerical representations of the typhoon
winds on these forest areas is critically important. The
model settings here were exactly the same with thoseused in Ito et al. (2016) except employing the 200 m gridareas. The number of grid points in each domain is 451
by 451 for D01, 301 by 301 for D02, 532 by 421 for D03,
and 166 by 161 for D04. The number of vertical levels
was 57, with the top set at the 20 hPa level.
It is important to use high-resolution digital elevation
model (DEM) data to set the model terrain used in numerical simulations. The model terrains for D01, D02,
and D03 in this study were created with the use of the
global DEM data having a horizontal grid spacing of 30
arc-seconds (about 1 km) provided by the United States
Geological Survey, while the terrains for D04 were generated with the 50 m horizontal resolution DEM data

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Fig. 1 The areas covered in the present numerical simulation. The computational areas for a the 9 km grid domain (D01), the 3 km grid domain(D02), and 1 km grid domain (D03) and b the 200 m grid domains (D04) nested in D03

provided by the Geospatial Information Authority ofJapan.
The physics parameterizations chosen here were the samewith those used in Ito et al. (2016): the Kain-Fritsch cumulus
parameterization (Kain 2004) (applied only for D01), theWRF single-moment 6-class scheme (Hong and Lim 2006)
for cloud microphysics, the Mellor-Yamada-Janjic (MJY)
scheme (Janjic 2002) for boundary-layer mixing, a revised
Monin-Obukhov scheme for surface flux parameterizations(Jimenez et al. 2012), a revised version of the Rapid Radiative
Transfer Model shortwave/longwave scheme (Mlawer et al.
1997) for atmospheric radiative transfer, and simple fivelayer thermal diffusion scheme (Skamarock et al. 2008) for
land-surface processes. The MYJ boundary-layer mixing

scheme was also chosen in Oku et al. (2010), Takemi (2009),
Takemi et al. (2010), and Takemi (2013) in their study on
the numerical representation of airflows in complex terrain.
Corresponding to this MYJ boundary-layer scheme, theMonin-Obukhov surface scheme provides better turbulent
momentum and heat fluxes on various land-use surfaces
under various stability conditions. The surface roughness features are represented with roughness lengths depending on
the land-use types. Because the surface roughness is not explicitly represented and resolved in the WRF model, the surface winds especially in complex geometry such as densely
built urban districts are not decelerated in the surface layer
and are overestimated in the WRF model (Nakayama et al.
2012; Takemi et al. 2019).

Takemi and Ito Progress in Earth and Planetary Science(2020) 7:4

One may argue that the present simulations can be
conducted in a large-eddy simulation (LES) mode. The
main reason why we did not use the LES framework isthat the grid spacing of 200 m is considered not to be a
sufficiently high-resolution to conduct LES. There are
two issues here. One issue is that the 200 m grid spacing
will not appropriately resolve turbulent motion within
an inertial subrange. Takemi et al. (2010) showed thateven with the 80 m grid spacing, the WRF model is notable to reproduce realistic wind fluctuations. The second
issue is that proper generation of turbulent flows to beingested in the main computational domain must be
performed to simulate turbulent flows; without turbulent
ingestions, the flows look laminar (Nakayama et al.
2012). Therefore, the present study is not intended to
employ an LES model but to use a standard boundarylayer mixing scheme in order to focus on the influencesof terrain representation on airflow simulations.
The initial and boundary conditions for the numerical experiments were prescribed by the Japanese 55year reanalysis data (JRA55, Kobayashi et al. 2015)
having the spatial resolution of 1.25° and the timeinterval of 6 h. It is critically important to closely reproduce the actual track of a typhoon for impact assessment studies, because the typhoon hazards at
local scales strongly depend on the track as well as
the intensity of the typhoon. For this purpose, a spectral nudging technique (Skamarock et al. 2008) wasapplied to the horizontal winds above the 700 hPa
level in D01. In this nudging technique, the maximum
wavenumber and the nudging coefficient were chosen
as 2 and 0.00028 s−1, respectively. Note that the spectral nudging plays a critical role in better reproducing
the tracks of tropical cyclones (Takemi et al. 2016b;
Takemi 2018a; Takemi 2019). Furthermore, a typhoon
bogus scheme which prescribes a Rankine-type vortex
was employed to initialize a typhoon vortex at theinitial time of the time integration. The maximum
wind speed of 70 m s−1 was set at the radius of 75
km from the typhoon center, based on the best-trackdata of the RMSC Tokyo-Typhoon Center. With
these approaches, we are able to favorably reproduce
the track and intensity of the typhoon as explained in
the next section. Although the ratio of the resolution
between the JRA55 data the D01 domain seems to belarge (Denis et al., 2003), the successful reproductionof such typhoon properties is considered to warrant
the present experimental setting.
The time integration was conducted starting at 0000
UTC 3 September 2004 for D01, 0000 UTC 5 September
for D02, and 0000 UTC 7 September for D03 and D04
until 0000 UTC 9 September. The model outputs in D03
and D04 were obtained at the 10 min interval and wereused for the present analyses.

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Results
Comparison with the observations

Ito et al. (2016) examined the representation of the track
and central surface pressure of the simulated typhoon bycomparing with the Regional Specialized MeteorologicalCenter (RSMC) best-track data and also by referring to
the analysis by Kitabatake et al. (2007) and confirmed
the validity of the typhoon simulation. This study further
compares the simulated results with the observations ofsurface winds. The wind data measured at automatedweather stations (AMeDAS) of Japan Meteorological
Agency (JMA) were used. The wind speeds observed at
the AMeDAS stations are temporal mean values averaged for 10 min and are available at the 10 min interval.
In order to evaluate the performance of the simulation
over Hokkaido, we validate the results simulated in D03
with the data obtained at the AMeDAS stations includedin D03. The time series of the surface winds at each station during the period from 0000 UTC 7 September to0000 UTC 9 September 2004 are used to compute somestatistics.
Figure 2a shows the difference of the simulated mean
wind speed against the observed one at each location. In
general, the simulation overestimates the observation
but mostly reproduces the observed value within a few
m s−1. There are some places in which overestimation islarge. As shown shortly, the larger overestimation appears at locations where mean wind speed is higher.
The representation of the wind speed variability in thesimulation is examined in terms of the ratio of the variance of the simulated wind speed to that of the observation. Note that the ratio of 1 means that the observed
wind variability is successfully reproduced in the simulation. The spatial distribution of this variance ratio isdemonstrated in Fig. 2b. At most of the stations, the ratios are seen to be within the range of 1 to 3, meaningthat the observed variability is well captured in the simulation. There are some stations indicating larger valuesof this ratio, in areas to the east or to the south ofmountains.
The correlation of the simulated wind speed with theobservation is further investigated. For this purpose, theSpearman’s rank correlation coefficient between thetemporal changes of the simulation and the observation
was chosen. The idea of using this correlation is basedon a thought that the temporal change of wind speed
strongly depends on the track and translation speed of atyphoon in addition to local effects such as topography
and roughness and hence the exact correspondence ofthe simulation with the observation would never be expected. In contrast, the rank of the observed wind speed
has a chance to be reproduced in the simulation. The
spatial distribution of the Spearman’s rank correlation
coefficients over Hokkaido is exhibited in Fig. 2c,

Takemi and Ito Progress in Earth and Planetary Science(2020) 7:4

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Fig. 2 Comparison of the surface winds simulated in D03 with the observations at surface stations. a The difference of the temporally averaged
wind speed from the simulation to the observation. b The ratio of the variance of the simulation wind speed against that of the observation. c
The Spearman’s rank correlation coefficient of the temporal change of wind speed between the simulation and the observation. The color shading in
all the panels indicates the ground elevation (m)indicating that the coefficients are mostly in the range of
0.6 to 1.0. Thus, the temporal change of the simulated
wind speeds correlates favorably with the observedchange.
Lastly, the bias of the simulated wind speed against
the observation is shown. The wind bias at each station
is calculated by:
1 XN pﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
22
Bias ¼
;
U
−U
þ
V
obs
t¼1
N
where U and V are the simulated wind components at
the 10 m height in the eastward and northward directions, respectively, Uobs is the observational wind speed,t is a time step, and N is the number of the time steps.
How the bias of the simulated winds changes with themean wind speed at each station is indicated in Fig. 3a.

The bias generally increases with the mean windspeed, and the linear regression line indicates this increasing trend. The larger overestimations shown in
Fig. 2a are considered to be due to larger biases athigher wind speeds. The relative bias at each stationcompared to the overall mean bias in D03 is demonstrated in Fig. 3b. Here, the mean bias averaged over
all the stations in D03 was computed, and the difference of the bias at each station from this area-meanbias was then derived. It does not appear that there isany clear feature in the geographical characteristics ofthe relative bias. However, it is seen that the relative
biases at inland locations (especially included in the
D04 areas) are generally small, suggesting that local
topography has no preferred influences on the wind
representation and bias. This is a positive aspect inTakemi and Ito Progress in Earth and Planetary Science(2020) 7:4

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Fig. 3 The characteristics of the wind speed bias. a The relationship between the wind speed bias against the simulated mean wind speed. The
linear regression line is indicated by red line. b The difference of the wind speed bias at each station against the spatially averaged bias over all
the stations in Hokkaido. The color shading in (b) indicates the ground elevation (m)investigating the wind representation in complex
terrain.
Based on the characteristics demonstrated in this subsection, we will investigate the representations of winds
simulated in D04 and how the horizontal grid spacing
affects the wind representations in the following
subsections.
Sensitivity to horizontal resolution

In order to examine the sensitivity of the wind representations to the horizontal grid spacing, we choose one area
among the three D04 domains, respectively, in northern,eastern, and southern Hokkaido. The analysis areas chosen
here are denoted as A, B, and C in northern, eastern, andsouthern Hokkaido (Fig. 4a). These areas A, B, and C include
forests that were damaged by the typhoon (Takano et al.
2016; Morimoto et al. 2019), and each area A, B, and C is referred to as Nakagawa, Abashiri, and Niseko (see Fig. 1 of
Takano et al. 2016). The differences in the terrain representation between the 1 km grid and the 200 m grid case arecompared in Fig. 4b, c, d for the area A, in Fig. 4e, f, g for thearea B, and in Fig. 4h, i, j for the area C. As clearly seen, thedetailed features of the terrain height are more realistically
reproduced in the 200 m grid case than in the 1 km gridcase. Peaks, ridges, and valleys are represented in a more enhanced way in the higher-resolution case. With these terraincharacteristics in mind, the wind representations will be investigated in the following.
Figure 5 compares the representation of the maximum
winds between the 1 km grid and the 200 m grid case interms of the frequency distribution of the maximum surface

wind speed during the simulated time period (i.e., from 0000
UTC 7 September to 0000 UTC 9 September 2004) at eachgrid point in each analysis area. In the 200 m grid case, thehigher (lower) extremes become stronger (weaker) and more
frequent, and the distribution is more widely spread. Because
the detailed terrain features are more enhanced in thehigher-resolution case, the contrasts between higher andlower elevations and between sharper and flatter slopes affectthe magnitude of the maximum winds. Takemi (2018b)
demonstrated that the difference in terrain representation between higher-resolution and lower-resolution topography
data is very significant even at the same grid spacing, leading
to major differences in quantitative rainfall simulations. This
study reveals such influences of the terrain representation on
the maximum winds.
Representations of surface winds in the areas A, B,
and C in the simulations with the 1 km grid and the
200 m grid are investigated by referring to the slope
angle of the model terrain. Figure 6 shows how thepointwise temporal mean wind speed changes withthe terrain slope angle. Because the number of grid
points is different between the 1 km grid and the 200m grid case, the number of points in Fig. 6 is also
different between these different grid cases. In all theanalysis areas, the range of the scatter of the mean
wind speed is wider in the 200 m grid case than in
the 1 km grid case. The slope angle is also spread
more widely between 0 and 30° in the higherresolution case than in the lower-resolution case, inagreement with the widely distributed terrain heightin the higher-resolution case (see Fig. 5). The linear

Takemi and Ito Progress in Earth and Planetary Science(2020) 7:4

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Fig. 4 Terrain features of the analysis areas. a Terrain height and the D04 areas in D03. Red boxes denoted as A, B, and C are the analysis areas.
The terrain height in the area A (b) at the 1-km grid spacing in D03 and (c) at the 200 m grid spacing in D04. d The frequency distribution ofterrain height in the area A (left) at the 1 km grid and (right) at the 200 m grid. e, f, g The same as (b, c, d) except for the area B. h, i, j The
same as (b, c, d) except for the area C

regression line has a positive slope, which means thatin general the mean wind speed increases with the increase in the slope angle. However, the rate of the increase of the mean wind speed is largely different
between the two resolution cases and is gentler in thehigher-resolution case. In other words, at a certain
slope angle of the terrain, the mean winds can beboth stronger and weaker.
The relationship between the maximum wind speed
during the simulated time period and the terrain slope

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Fig. 5 The frequency distribution of the maximum surface wind speeds in the analysis areas. The relative frequency distribution (in %) of the
maximum wind speeds during the simulated time period at the grid points in the area (a) A, (b) B, and (c) C for (left) the 1 km grid case and(right) the 200 m grid caseangle is shown in Fig. 7. The features identified for themean wind speed indicated in Fig. 6 are similarly seen
for the maximum wind speed. The maximum winds aswell as the terrain slope angles are widely spread in all
the areas. The increasing tendency of the maximum
wind with the increase in the slope angle is also seen,
but the difference in the regression line’s slope between
the 200 m grid and the 1 km grid case appears to besmaller for the maximum wind speed than for the mean
wind speed.
Table 1 summarizes spatially evaluated statistics computedfor the pointwise mean and maximum wind speeds withineach analysis area. The area-mean values for both the temporal mean and maximum winds in all the analysis areas are
not so different between the 1 km grid and 200 m grid cases.
In contrast, the area-maximum values are 1.6 to 2.1 times
larger in the 200 m grid case than in the 1 km grid case. The
spatial variances are also larger in the 200 m grid than in the
1 km grid case; for the temporal maximum wind speed, thevariances are 2.6 to 5.7 times larger in the 200 m grid than in
the 1 km grid case. These statistics are the reflection of thescatter characteristics indicated in Fig. 6.
From the analysis shown in Figs. 5, 6, and 7, the characteristics of the temporal and spatial variability of winds appear

Takemi and Ito Progress in Earth and Planetary Science(2020) 7:4

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Fig. 6 Relationships between terrain slope and mean wind speed in the analysis areas. Relationships of the temporal mean wind speeds against
the terrain slope angle for the 1 km grid case (black dots) and the 200 m grid case (gray dots) in the area (a) A, (b) B, and (c) C. Red solid anddotted lines indicate the linear regression line for the 200 m grid and the 1 km grid case, respectively

differently in the 1 km and 200 m grid cases. In the next subsection, we will further show how the representation of terrain features in the higher and lower-resolution cases affectthe variability of surface winds.
Relationship with the variability of terrain height

The representation of the model terrain at a higher resolution compared with a lower resolution is investigated interms of the subgrid-scale orography used in Jimenez andDudhia (2012). Subgrid-scale orography can be represented
as how terrain heights vary spatially within a certain area andis here defined as the standard deviation of terrain heights(σSSO) at the 200 m grid spacing within a 1 km by 1 km area.
In this study, we evaluate the subgrid-scale orography as thearea-mean standard deviation of terrain heights over each
D04 area as follows:Takemi and Ito Progress in Earth and Planetary Science(2020) 7:4

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Fig. 7 Relationships between terrain slope and maximum wind speed in the analysis areas. The same as Fig. 6, except for the maximum windspeed during the simulated time period

8vﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ9
200 m u 1 nX=t
Subgrid−scale orography ¼
σ SSO;i ¼h j −h
;
:
;
n
200
m
i¼1
i¼1
j¼1
NX
1 km

u
NX
1 km <iwhere hj and h are, respectively, the terrain height at
the 200 m grid and its mean at a grid point j within a 1
km by 1 km area, n200 m is the number of 200 m resolution grid points within a 1 km by 1 km area (i.e., 25),
N1 km is the number of 1 km resolution grid points
within a D04 area, and i is a certain grid point within
the D04 area.

As an example, Fig. 8 indicates the spatial distributionof the standard deviation of the 200 m grid terrain
within the area B (see Fig. 4f). The area-mean standard
deviation for this area becomes 60.7. In this way, thearea-mean standard deviation of terrain heights is computed for the 8 D04 areas. In the followings, the relationships between the subgrid-scale orography and thewind representations are indicated.
Figure 9 demonstrates how the area-means, areamaxima, and spatial variances of the temporal mean
wind speeds within the D04 areas vary against theTakemi and Ito Progress in Earth and Planetary Science(2020) 7:4

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Table 1 Statistics of the mean and maximum wind speeds in the analysis areas. The results of mean and maximum wind speeds
during the simulated time period obtained in the 1 km grid and 200 m grid cases for the areas A, B, and C are summarized. The
statistics are the area-mean, area-maximum, and spatial variance values computed from the temporal mean and maximum winds ineach analysis area
Area-mean (m s−1)

Area-maximum (m s−1)

Area-variance (m2 s−2)

Mean wind speed
Grid spacing

1 km

200 m

1 km

200 m

1 km

200 m

Area A

9.8

9.7

14.3

26.0

2.2

6.4

Area B

12.0

11.5

20.3

33.8

6.7

17.2

Area C

10.4

10.4

18.6

31.1

3.3

7.1

Grid spacing

1 km

200 m

1 km

200 m

1 km

200 m

Area A

27.3

29.0

37.9

65.7

10.9

44.2

Area B

27.2

32.3

40.2

83.7

19.0

108.4

Area C

27.2

28.3

50.1

80.8

15.2

39.9

Maximum wind speed

subgrid-scale orography that is defined as the area-mean
standard deviation of the terrain heights at the 200 m
grid spacing. In Fig. 9a, the area-mean value of the temporal mean wind speeds at the grid points in each D04
area is shown. It is seen that the area-mean values fromthe 200 m grid and the 1 km grid case are very similar
with each other but slightly higher in the 1 km grid casethan in the 200 m grid case. The reason why the mean
wind speeds are stronger in the 1 km grid case than in
the 200 m grid case is due to the difference in roughness. That is, the more complex feature of the terrain
represented in the 200 m grid case results in larger

roughness on average in a certain area, compared to theterrain represented in the 1 km grid case. It is also foundthat the mean winds gradually increase with the increasein the subgrid-scale orography.
Figure 9b examines the relationship of the area-maxima
among the time-mean wind speeds in each D04 area against
the subgrid-scale orography. Overall, the area-maxima arestronger in the 200 m grid case than in the 1 km grid case.
Furthermore, in the 200 m grid case, the area-maximum
wind becomes stronger with a larger subgrid-scale orography, while in the 1 km grid case, the area-maximum does
not change much with the change in the subgrid-scaleFig. 8. The standard deviation of subgrid-scale orography. The spatial distribution of the standard deviation of the subgrid-scale orography
computed in a 1 km by 1 km area from the model terrain at the 200 m grid spacing, for the area B (see Fig. 4f) as an example

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Fig. 9 The relationships between the subgrid-scale orography and the wind representations. The relationships of the area-mean standard deviation ofterrain heights within each D04 area with a the area-mean wind speed, b the area-maximum wind speed, and c the spatial variance of wind speed in
the same D04 area. Black and gray points indicate the results of the 200 m grid case and the 1 km grid case, respectively

orography. These features can also be found for the spatial
variances of the time-mean wind speeds in the D04 areas, as
indicated in Fig. 9c.
In this way, it was found that the area-maxima and spatial
variances of the time-mean surface winds appear more pronouncedly in the higher-resolution case than in the lowerresolution case. We have also examined the features of thearea-means, area-maxima, and spatial variances for the temporal maximum wind speeds in the D04 area and found thatthe area-mean, area-maxima, and variances all increase with
the increase in the subgrid-scale orography (not shown). In
other words, the complexity of the terrain features enhances
the mean and maximum wind speeds over the simulated
time period as the forms of the statistics examined above.

Discussion
We have investigated the representations of typhooninduced surface winds in complex terrain in high-(2020) 7:4

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resolution simulations of a specific event in Hokkaido,
the northern part of Japan, as a case study for Typhoon
Songda (2004). The comparison of the simulated winds
at the 1 km grid and the 200 m grid indicates that therange of the simulated wind speeds becomes wider andthat the higher and lower extrema become more extreme in the higher-resolution case. We have also identified that the mean and maximum wind speeds during
the simulated time period increase with the increase in
the terrain slope angle and that the scatter becomeswider with a higher slope angle in the higher-resolution
case. In other words, the response of the simulated
winds to the slope angle appears differently between the
1 km grid and 200 m grid cases. By using a quantity
representing the subgrid-scale orography, we haveshown that the area-maxima and spatial variances overthe D04 areas become more enhanced with the increasein the subgrid-scale orography in the 200 m grid casethan in the 1 km grid case.
From these analyses, it is suggest that the temporaland spatial variability of surface winds becomes larger inthe higher-resolution case, which is closely related to themore complex features of the terrain reproduced in themodel. An implication of this result is that highresolution simulations at the grid spacing of the order of
100 m are able to highlight fluctuating natures of surface
winds and can be regarded as one of the benefits oradded values of the high-resolution simulations of airflows in complex terrain. Highly fluctuating winds leadto enhanced extremes both in the higher and lower
ranges, and hence more hazardous winds will be represented in the higher-resolution case. Therefore, thispoint is critically important in assessing the strong windhazards in complex terrain.
Because the current modeling framework does nottake into account explicitly the effects of turbulence
through an LES approach, the simulations are not expected to reproduce turbulent flows. In spite of this deficiency, it is emphasized that the present meteorologicalmodeling with the use of high-resolution DEM data can
reproduce enhanced fluctuating natures of surface winds
in time and space, which are generated by fine-scale features of terrains. Namely, high-resolution simulations ofairflows in complex terrain can benefit from detailed
representations of topographic features of terrain in
representing extreme winds.
Note here that the present simulations employ the
MYJ boundary-layer scheme, i.e., a type of Reynolds
averaging Navier-Stokes model, and hence the simulated
winds by the WRF model should be regarded as means
averaged for a certain period of time. Nakayama et al.
(2012) compared the surface winds among the observations, the WRF simulations at the 60 m grid spacing,
and the LES results and found that the WRF winds

correspond closely to the 10 min means. Although thetemporal averaging scale of the WRF winds may dependon the surface roughness features and the meteorological conditions, the WRF winds are regarded as the
temporal means for around 10 min. Even if we makeWRF outputs at a higher frequency (e.g., 1 min), theWRF winds does not fluctuate in time. Therefore, we do
not evaluate turbulent fluctuations in this study. What
we are evaluating here is regarded as the temporal variation of winds induced by undulations of topography,
corresponding to the time-scale of 10 min or longer.
Previous studies on airflows in complex terrain (Jimenez
et al. 2010; Jimenez and Dudhia 2012; El-Samra et al.
2018) conducted numerical simulations with the horizontal grid spacings of 1 km or 2 km. Although such grid
spacings appear to be insufficient to resolve actual terrains, the geometrical features of the terrains in their studies may be characterized with larger-scale undulations, ascompared with the terrain features of the present analysis
areas in Hokkaido, Japan. Bonnardot and Cautenet (2009)
demonstrated that the simulations at 1 km and 200 m resolutions better reproduced local circulations than those at
5 km resolution because of a better representation of thelocal terrain. By distinguishing meteorological situations,they further showed that under a weak synoptic flow condition, local circulations prevailed and the high-resolution
simulation at the 200 m grid better reproduced meteorological characteristics. Thus, it is considered that depending on the background meteorological conditions, theresponse of the meteorological changes to terrain featuresappears differently.
In this study, we examined a strong wind case induced
by a severe typhoon. It was found that gust factors arehigher in tropical cyclone cases than in extratropical cyclone cases (Paulsen and Schroeder 2005). Therefore,
wind fluctuations including gust factors occur moreclearly in typhoon cases, and hence reproducing highly
fluctuating natures of typhoon winds in high-resolution
simulations is an advantage in investigating the physicalprocesses of strong winds in complex terrain and moreover assessing the impact and hazard of strong winds at
local scales.

Takemi and Ito Progress in Earth and Planetary Science

Conclusions
This study investigated the representation of surface
winds in complex terrain in numerical simulations withthe horizontal grid spacing of 200 m. The strong wind
event in Hokkaido during the passage of Typhoon
Songda (2004) was chosen as a case study, because thestrong winds by this typhoon caused widespread damages to forests in Hokkaido. We used the WRF model toconduct regional-scale simulations at the 200 m grid byemploying the same computational settings with thosein Ito et al. (2016) except setting the 200 m domains.

(2020) 7:4

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The results of the 200 m grid simulations were examined and evaluated in comparison to those of the 1 km
grid case. After validating the simulated results with thesurface observations, we explored the benefits of thehigh-resolution simulations of surface winds in complex
terrain.
The 200 m grid simulations were able to clearly represent more enhanced and more frequent extremes bothin the stronger and weaker ranges of surface winds than
the 1 km grid simulations. This result corresponded tothe difference in the terrain representations at these two
grid spacings. It was also shown that the mean and maximum winds during the simulated time period increase
with the increase in the slope angle of the terrain bothin the 200 m grid and 1 km grid cases. Because the scatter of the mean and maximum winds is much wider inthe 200 m grid case than in the 1 km case, the slopes ofthe linear regression lines are smaller in the 200 m case.
The characteristics of the temporal and spatial variabilityof surface winds appeared differently in the 200 m and 1
km grid cases.
To assess the influences of the terrain complexity onwind variabilities, we introduced subgrid-scale orography
which was defined as the area-mean value of the standard deviations of the terrain heights at 200 m grid within
a 1 km by 1 km area. With this quantity, it was demonstrated that the area-maxima and spatial variance of surface winds over the 200 m grid computational domains
are more enhanced with the increase in the subgridscale orography in the higher-resolution case than in thelower-resolution case.
Based on the present results, it is suggested that thehigh-resolution simulations at the 200 m grid are able tohighlight and enhance the fluctuations of surface winds
in complex terrain, because the complex topographic
features of the terrain is better represented in the 200 m
grid than the 1 km grid case. This is considered to be
the benefits or added values of the high-resolution simulations of airflows in complex terrain. It is also considered that the topographic features of Hokkaido
examined in this study are highly complex and thus thegrid spacing on the order of 100 m is desirable.
Because the mountainous regions, characterized byabundant forest resources as well as complex topographic features, prevail over the Japanese islands, thetemporal and spatial variability of surface winds largely
affect the forest ecosystem. A strong wind event can bea great threat to the regions. Especially in the northernpart of Japan, the landfall or approach of typhoons israre, and hence the impacts of typhoons once occurred
are huge. We have investigated the case of Typhoon
Songda (2004), but there were a number of typhoons
that affected northern Japan. For example, Typhoon
Mireille (1991) landed on Hokkaido and caused severe

damages on agriculture and forestry. Actually, this specific typhoon spawned the most costly insurance loss
among the tropical cyclones in the Pacific region during
the period from 1970 to 2017 (Takemi et al. 2016a).
From viewpoints of disaster prevention and mitigation, it
is critically important to quantify the impacts of typhoons in complex terrain where trees and forest arevulnerable to strong winds and also assess the impactsof climate change on such typhoon hazards. Climatechange impacts on strong winds and heavy rainfalls in
Hokkaido during typhoon passages were investigated by
Ito et al. (2016), Takemi et al. (2016a), Kanada et al.
(2017), and Nayak and Takemi (2019a, 2019b). Such typhoon hazard information, once obtained with the resolution on the order of 100 m, needs to be fully used to
impact assessment studies on forest ecosystems.

Takemi and Ito Progress in Earth and Planetary Science

Abbreviations
AMeDAS: Automated Meteorological Data Acquisition System; DEM: Digital
elevation model; JMA: Japan Meteorological Agency; LES: Large-Eddy
simulation; RSMC: Regional Specialized Meteorological Center; WRF: Weather
Research and Forecasting
Acknowledgments
We thank the support from the TOUGOU program funded by MEXT, Japan.
Authors’ contributions
TT proposed the topic, designed the study, summarized the result, andwrote the original manuscript. RI carried out the numerical simulations,analyzed the simulated data, and interpreted the analysis results. Bothauthors read and approved the final manuscript.
Authors’ information
TT have been studying the impacts of climate change on extreme weather(tropical cyclones, heavy rainfalls, etc.) by using dynamical downscaling since2007 under the MEXT programs: KAKUSHIN Program (Innovative Program of
Climate Change Projection for the 21st Century), SOUSEI Program (Programfor Risk Information on Climate Change), and TOUGOU Program (Integrated
Research Program for Advancing Climate Models). RI is a program researcher,
who studied the impacts of typhoons in future climate under SOUSEI
program and, at the present, have been studying the uncertainty in regional
climate change obtained from various climate dataset under TOUGOU
program.
Funding
This study was supported by the “Integrated Research Program for
Advancing Climate Models (TOUGOU program)” from the Ministry of
Education, Culture, Sports, Science and Technology (MEXT), Japan. This work
was also supported by JSPS KAKENHI Grant Number 16H01846 and
18H01680.
Availability of data and materials
The data supporting the conclusions of this article are available uponrequest. Please contact the authors for data requests.
Competing interests
The authors declare that they have no competing interest.
Author details
1
Disaster Prevention Research Institute, Kyoto University, Gokasho, Uji, Kyoto
611-0011, Japan. 2Japan Meteorological Business Support Center, 1-1
Nagamine, Tsukuba, Ibaraki 305-0052, Japan.

Takemi and Ito Progress in Earth and Planetary Science(2020) 7:4

Received: 15 July 2019 Accepted: 24 December 2019

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