地理学論集
Vol. 92, No. 1（2017）

Geographical Studies
Vol. 92, No. 1（2017）

Analysis of the Strong Local Wind in Northwestern Hokkaido,
Japan
Koki KASAI1, Keiji KIMURA2, Kei SHIOMI3,4, Atsushi KONNO4, Takeo TADONO3,4 and Masahiro HORI3,4

Abstract

A strong local wind has been observed in Rumoi, northwestern Hokkaido, Japan. In order to investigate the windaround Rumoi, an analysis was performed using 30-year AMeDAS wind observations and numerical experiments with theWeather Research and Forecasting (WRF) model. Observation results show that there is a frequent strong wind in Rumoi incomparison with other areas around Rumoi. In relation to seasonal changes in wind direction, more than 50% of the windin summer blows from ESE, but westerly winds are frequent in winter. To clarify the mechanisms involved in producing
a strong local wind in winter, we compared two events using observations and numerical experiments. One case involves
a strong local wind with a speed of approximately 12 m/s in Rumoi and a weak wind of less than 5 m/s in the surrounding
areas; the prevailing wind direction is ESE (03:00 on January 28, 2003). The other case is that of a strong wind over 10 m/s
blowing over the entire study area (on the afternoon of March 8, 2005), with a westerly prevailing wind. The experimental
results show a relatively good correspondence with those of the observation. A cross-section view demonstrates theexistence of a mountain wave when the strong local wind is observed in Rumoi. The wind then passes through the valleyshaped topography, and wind speeds are accelerated in the vicinity of Rumoi, which suggests the existence of a gap wind.
Additionally, a sensitivity experiment is performed by changing the land surface model without considering snow, and thewind speed over land is seen to be reduced by 2 m/s, with very little change in the wind direction.

Key words: local wind, numerical experiment, statistical analysis, wind speed, influence of topography
I. Introduction
Wind consists of the bulk movement of air. In the fieldsof mesoclimatology and microclimatology, it is necessary
to gain an understanding of the distribution of meso-β
scale winds to explain the distribution of temperature andprecipitation (Kawamura, 1963). In a study conducted overa large area in Japan, Kawamura (1966, 1981) investigatedthe distribution of winds over central Japan and the Kanto
Plain. In the above-mentioned previous studies, the surface
wind distribution was estimated by the prevailing winddirection, and determined according to wind roses of themost frequent wind direction. Suzuki (1991, 1992) usedground-based observation data and focused on the windover central Japan to clarify the response of surface windspeed and direction to the synoptic pressure gradient. In
these studies, it is acknowledged that the observed windincludes both the effect of synoptic pressure gradient andthat of local circulation.

The present study focuses on an analysis of wind speed
and wind direction in the area of Rumoi, Hokkaido, Japan(Fig. 1). Japan lies predominantly within the temperate
climate zone, but the Hokkaido and Tohoku regions
lie on the main transition band between temperate andsubarctic climates (Kasai and Kimura, 2013). Therefore,
Hokkaido experiences a subarctic climate, and most of theregion is covered with snow in winter. Kawamura (1963)
investigated the surface wind distribution in Hokkaido, andclassified eight types of large-scale flow patterns during
winter. Winds in the area of Rumoi blow from W or ESE
direction. Kato (1983) analyzed daily mean wind speed andsurface pressure data using a principal component analysis
to investigate the wind distribution in Hokkaido. The author
suggested that W and ESE winds blew over Rumoi as theprimary and third principal component, respectively. Suzuki(1994) also analyzed the surface wind over the coastal
area surrounding Japan (including Rumoi); the direction

1

Graduate student, Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan

2

Faculty of Letters, Nara University, Nara, Japan

3

Earth Observation Research Center, Japan Aerospace Exploration Agency, Tsukuba, Japan

4

Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan－17－

Fig. 1. Study area
Circles indicate AMeDAS locations around Rumoi. A triangular denotes the summit of
Mt. Shokambetsu.

of the land (sea) breeze was shown to be ESE (WNW).
However, these previous studies focused on the entire areaof Hokkaido or on ground observation points in the coastal
area, and therefore, the distribution of the local wind in thenorthwestern part of Hokkaido has not yet been adequately
clarified. Currently, we can analyze this in detail using themeteorological dataset of long-term multipoint observations
to investigate not only the mean field, but also local events
such as strong winds.
Winds that are influenced by local topography andrestricted to a range of 100 km or less are known as
local winds (Defant, 1951). In studies of local winds in
Hokkaido, Kawai et al. (2008) researched the climatologicalcharacteristics of the local wind in Tokachi district. In
addition, Sagawa (2004) carried out field observations andanalyzed the strong wind blowing in the Suttsu region in
the western part of Hokkaido, concluding that both the areaof strong wind and the discontinuity line of the wind speedin Suttsu were related to the valley-shaped area. Theseprevious studies discussed local winds using observational
data and/or reanalysis data, and therefore, spatial wind
distributions were not clarified. Furthermore, there are
no existing studies to date analyzing wind distribution in
the northwestern part of Hokkaido using meteorologicalmodels.
Numerical simulations are performed to determine thestrong local wind and its spatial distribution in the areaof Rumoi. In order to adequately study the local wind,abundant meteorological data are required. However, there
is only a small volume of obtainable observed data at a
certain spatial resolution, and therefore it is necessary to
perform additional numerical simulations to determine
the spatial distribution of a number of meteorologicalfactors. For example, Prtenjak et al. (2010) investigated
the interaction of a summer frontal bora and the sea-landbreeze along the northeastern Adriatic coast, and Belušić
et al. (2013) also studied the Adriatic bora wind using a
numerical model simulation. In addition, Powers (2007)
simulated the major Antarctic event of the May 2004
McMurdo windstorm, and Steinhoff et al. (2013) analyzed
a case study of a representative summer foehn event inorder to identify and explain the McMurdo Dry Valleys
foehn mechanism. In Japan, Kawaguchi et al. (2010)
analyzed Yamase, which is a cold, humid, northeasterly
wind blowing towards the coast on the Pacific side of the
Tohoku region during summer, which blows in a southerly
direction in the Kitakami Basin. Furthermore, Sasaki etal. (2010) performed numerical simulations of the strong
southeasterly “Kiyokawa-dashi” wind in Yamagata, Japan,
during the summer, and reproduced it at a resolutionof 1 km. Inamura et al. (2009) carried out a numericalexperiment to investigate a local wind known as the
Matsubori-kaze, and discussed the influence of the uniquetopography of Mt. Aso, Kumamoto. Similarly, Sakamoto etal. (2014) investigated the simulation and mechanism of the
Matsubori-kaze.
Weather simulation models are not exclusively used forinvestigations of wind speed and direction, and are also
used to model other meteorological factors. Takane et al.
(2013) investigated the actual conditions of mesoscale
summer high temperatures recorded in the Osaka-Kyoto
urban area of Japan using a numerical simulation as well as
an observation network and found that a significant amountof heat was transported from the tropics at the synoptic
scale and/or mesoscale and airflow also contributed to thehigh temperature events.

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When using meteorological models, it is useful tomake sensitivity studies of model parameters to evaluate
and understand the model accuracy and characteristics.
Previous studies have investigated meteorological model
sensitivity using different dataset input methods (Tatsumi
et al., 2008; Akimoto and Kusaka, 2010; Zhang et al.,2012). In addition, numerical simulations have been applied
over complex terrain in order to evaluate the ability of
meteorological models (Jiménez and Dudhia, 2013; Zhang
et al., 2013). Horvath et al. (2012) performed sub-kilometer
dynamical downscaling using the Weather Research andForecasting (WRF) (Skamarock et al., 2008) and Mesoscale
Model Version 5 (MM5) models (Grell et al., 1994), andconsidered the characteristics of these models. In thepresent study, as the study area is covered with snow during
the winter season, a sensitivity experiment is performed toevaluate the influence of the land’s surface.
The present study has three main aims, as follows.
Firstly, this study applies a statistical analysis of the windspeed and direction to the area of northwestern Hokkaido(around Rumoi). Secondly, the strong local wind in Rumoi
is modeled using numerical experiments, with the aim of
reproducing the observed weather conditions during datacollection and to show the spatial distribution of the wind.
In addition, the experimental result is then compared witha strong wind event that occurred over the entire area of
Rumoi. Finally, the influence of the topography aroundRumoi is discussed as being the mechanism for the strong
local wind, and a sensitivity experiment is conducted tounderstand the change in winds in relation to a change inland surface schemes, with or without the consideration ofsnow.
A statistical analysis of the wind around Rumoi is

described in Chapter 2, and the methodology used to
select the strong local wind event in the area is explained.
The model datasets used in this study are explained anda summary of the WRF model is provided in Chapter 3.
Two types of meteorological data are used, sea surface
temperature (SST) and digital elevation model (DEM) data,
and the numerical method used in this study is described,
together with the nested domains. The two reproductive
experimental results of the WRF are shown in Chapter 4.
Chapter 5 discusses the orographic wind, which is thesource of the strong local wind in Rumoi, and the result ofa sensitivity experiment is also shown. Finally, Chapter 6
provides a summary of this study.

II. Local wind around Rumoi
1. Statistical wind analysis
A statistical analysis was performed to gain anunderstanding of the wind movement around Rumoi, andthe observation datasets of the Automated Meteorological
Data Acquisition System (AMeDAS) operated by theJapan Meteorological Agency (JMA) were used for thisanalysis. The observation points were Rumoi, Mashike,
Haboro, Horonuka, Ishikari-Numata, Fukagawa, Tappu,
Shumarinai, Horokanai, and Yagishiri (see Fig. 1) and themeteorological elements were the daily mean wind speed,
daily maximum wind speed, and the most frequent daily
wind direction. Data from the period 1981 to 2010 wereused as normal values, but from Haboro the daily mean
wind speed data were available only from 1981 to 1999,
and the daily maximum wind speed from 1981 to 2002, due
to discontinuous datasets.
Figure 2 shows the mean and maximum wind speeds
recorded at the observation points. The mean wind

Fig. 2. Wind speed comparison
White and gray bars indicate average wind speed and maximum wind speed of the AMeDAS observation points,
respectively. Error bars represent standard deviations.

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speed in Rumoi was approximately 5.0 m/s, which is thesecond highest recorded speed. The highest wind speed
was recorded in Yagishiri (approximately 5.4 m/s) on aremote island of Hokkaido, and hence, the wind speed in
Rumoi is relatively strong compared to other locations innorthwestern Hokkaido. The mean wind speed at coastal
locations (Rumoi, Mashike, and Haboro) was higher
than those in other areas. However, the speed recorded
at Fukagawa was an exception, as it was strong in spiteof being an inland location. The lowest wind speed wasrecorded at Shumarinai (approximately 1.5 m/s), and themean wind speed of the observation points located inland(except Fukagawa), and consisting of Horonuka, IshikariNumata, Tappu, Shumarinai, and Horokanai, was less than
2 m/s. The maximum wind speed had the same tendency asthat of the mean wind speed, although the maximum windspeed of Rumoi (approximately 8.7 m/s) was slightly higher
than that of Yagishiri. The lowest maximum wind speed
was recorded at Shumarinai, at approximately 3.6 m/s.
A wind frequency analysis was then conducted tounderstand the frequency of the strong winds recorded at
the observation points. The winds speeds were divided into
intervals of 5 m/s up until the daily maximum wind speed,
and Fig. 3 shows the result of the frequency distribution,
where it is evident that Rumoi had the least frequency (7%)
of weak winds (< 5 m/s). The weak wind (< 5 m/s) occupied
more than half of the wind in Horonuka, Ishikari-Numata,Tappu, Shumarinai, and Horokanai. Rumoi recorded thesecond largest number of days with a maximum wind speedof more than 10 m/s, after Yagishiri. This shows that thewind in Rumoi was strong in both frequency and windspeed. In addition, there were minor frequencies of strong

winds (> 20 m/s) in Rumoi (0.11%), Fukagawa (0.06%),
and Yagishiri (0.68%).
As the above results related only to wind speed, ananalysis was also performed in relation to wind direction.
Wind roses were made on the basis of the daily mostfrequent wind direction at the observation points. Figure 4
shows the wind roses for an entire year, and during winter(DJFM) and summer (JJAS). From an analysis of the annual
wind roses, it is evident that Rumoi, Horonuka, Fukagawa,
Tappu, Shumarinai, and Horokanai experience two main
wind directions with no seasonal changes occurring inthese wind directions, even when the frequency of the windchanges. These winds are, therefore, considered to be theprevailing winds at each location. Mashike and Haboro
also experience two dominant wind directions, but seasonalchange exists in the wind direction frequency. IshikariNumata has three most frequent wind directions (NE, S, and
NW). In addition, the wind roses of Yagishiri show various
directions, and it is therefore evident that no prevailingwind direction exists at Yagishiri.
In relation to seasonal change, more than 50% of thewind blows from ESE in Rumoi during summer. In winter,however, the westerly wind is more frequent (at more than30%), although the ESE wind continues to blow in Rumoi.
In a previous study, Kato (1983) divided the observation
points into four kinds of wind systems to clarify regional
characteristics of the wind in Hokkaido, and Rumoi wasthen classified into clusters where the wind from the westchanges seasonally. During winter in Mashike, Haboro,
Tappu, Shumarinai, and Yagishiri the wind tends to blow
from northward in contrast to summer, but the wind rosesof Fukagawa and Horokanai rarely change seasonally.

Fig. 3. Frequency distribution of maximum wind speed between the AMeDAS observation points－20－

Fig. 4. Wind roses for the AMeDAS observation points (a–e)
Three wind roses for each observation point, representing (from left to right) annual, winter (DJFM), and summer (JJAS) prevailing winds,
respectively.

－21－

Fig. 4. Continued (f–j)－22－

2. Selection of events
This subsection describes the methodology used to
select strong local wind events around Rumoi. As shown in
Fig. 2 and Fig. 3, Rumoi has a wind speed higher than thesurrounding AMeDAS observation points, and strong winds
blow frequently (except for Yagishiri, which is located on aremote island). It is evident from results of analysis of themost frequent wind direction in Rumoi (Fig. 4a), that thetwo main wind directions are ESE and W.
This study uses numerical experiments with the WRF
model to clarify the condition of the wind on a day when astrong local wind occurred in Rumoi. To achieve this aim,
the strong wind record observed in Rumoi is investigated.
For comparative purposes, data for Mashike and Haboro
were also checked, as these are coastal areas proximal to
Rumoi (Fig. 1). Here, a correlation analysis is performed
between Rumoi and Mashike, and between Rumoi andHaboro using wind speeds recorded every 1-hr. Table 1
shows the result of the correlation analysis focusing on thetwo main wind directions in Rumoi. When the prevailingwind direction is ESE, the correlation coefficient has alower value of less than 0.3, and a regression coefficientof 0.07–0.22. Using a comparison of the regression
coefficients, it is evident that Rumoi experiences an ESE
wind that is over four times stronger than at the otherlocations. However, when the W wind prevails in Rumoi,
the wind speed in Rumoi correlates strongly with that insurrounding areas (correlation coefficient ~0.8), and theregression coefficient is larger compared to the case of the
ESE wind.
A strong wind event occurring in Rumoi at 03:00 LST
on January 28, 2003, was selected for the first case, as it
occurred concurrently with a weak wind event in Mashikeand Haboro. The wind direction in Rumoi at this time
was ESE (shown in Fig. 10 compared with numericalexperiments). The wind speeds in Rumoi, Mashike, andHaboro were 11.9 m/s, 3 m/s, and 2.2 m/s, respectively. The
pressure pattern at this time was obtained from objective
analysis datasets and is shown in Fig. 5. Figure 5a shows
low pressure located over the Sea of Japan to the westof Hokkaido at 03:00 LST January 28, 2003, which isconsistent with the wind direction in Rumoi. In addition,
as a contrasting second case, 15:00 LST March 8, 2005,
was selected because western winds dominated in Rumoi,
Mashike, and Haboro (shown in Fig. 14). The wind speed

at the three points was similar (from 11 m/s to 15 m/s) andthis is therefore an example of a non-locally strong windoccurring in relation to Rumoi. From the pressure patternat this time (shown in Fig. 5b), the pressure gradient isin a north-south direction in Hokkaido. By setting events
based on the two main wind directions occurring in Rumoi,
this study then aims to clarify the different characteristicsof the wind conditions in this study area via numericalexperiments.

III. Experimental design
1. Initial data
National Centers for Environmental Prediction (NCEP)
Final (FNL) Operational Model Global TroposphericAnalyses product (NCEP/National Weather Service/NOAA/
U.S. Department of Commerce, 2000) is used as input
meteorological data, as it has spatial ranges covering allover the world. The datasets were prepared every 6 h on
1° × 1° grids. The analyses are available on the surface andat 26 levels from 1000 hPa to 10 hPa. In addition, mesoobjective analysis (MANAL) datasets by JMA were also
used in the experiment, because their spatial resolution is
10 km, which is finer than that of the FNL. The temporal
resolution of MANAL is the same as that of FNL (sixhourly). MANAL datasets consist of U and V components
for wind, temperature, relative humidity, pressure reduced
to mean sea level, and geopotential height. MANAL wasselected as the main product for use, although FNL wasused for supplementary elements that were not recorded in
MANAL.
The SST data used were the Optimum Interpolation
Sea Surface Temperature (OISST) by NOAA (Reynolds
et al., 2007), which were prepared separately using themeteorological datasets. OISST has a spatial resolution of
1° × 1°, which is equivalent to that of the FNL. However,it has a temporal resolution of one week (168 h), because
SST changes minimally in time compared to othermeteorological variables.
The DEM datasets used in the present study were
provided by the Geospatial Information Authority of Japan,
and cover an area of Japan from 40°N north, excluding
Etorofu, Kunashiri, Shikotan, and Habomai islands, witha spatial resolution of 10 m, which is much finer than theoriginal DEM datasets of the WRF model.

Table 1. Statistics from correlation analysis of observed wind speed in 2003 and 2005
Wind direction
Correlation coefficient
Regression coefficientRumoi vs. Mashike2003

Rumoi vs. Haboro

2005

2003

2005

ESE

0.09

0.11

0.25

0.28

W

0.81

0.89

0.85

0.78

ESE

0.07

0.11

0.19

0.22

W

1.00

1.02

0.69

0.64

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Fig. 5. Pressure distribution chart (a) at 03:00 LST on January 28, 2003 and (b) 15:00 LST March 8, 2005 by using MANAL datasets
The contour lines represent the isobaric lines of 4 hPa, and the black circles indicate the location of Rumoi. The description of MANAL
datasets is shown in section 3.1 because the datasets are used as input data for numerical experiments.

2. Meteorological model and experimental domains
Meteorological models, particularly mesoscalemeteorological models, are widely used as analysis tools inengineering and agriculture, as well as in meteorology (e.g.,
Kusaka, 2009). The WRF model is a numerical weather
prediction and atmospheric simulation system designed
for both research and operational applications. The WRF
effort is the result of a collaboration between university
scientists at the National Center for Atmospheric Research’s(NCAR) Mesoscale and Microscale Meteorology Division,
NOAA’s NCEP and Earth System Research Laboratory(ESRL), the Department of Defense’s Air Force Weather
Agency (AFWA) and Naval Research Laboratory (NRL),
the Center for Analysis and Prediction of Storms (CAPS)
at the University of Oklahoma, and the Federal Aviation
Administration (FAA) (Skamarock et al., 2008). WRF
version 3.6.1 was released on August 14, 2014, and is usedin this study.

The experimental timeframes used were from 21:00 LST
on January 27, 2003 to 09:00 LST on January 28, 2003;
and from 09:00 to 21:00 LST on March 8, 2005. In thetwo numerical experiments, the settings used are identical:
6-hr prior to and after the event time. The spin-up period
is estimated as 6–12 hr with a spatial resolution of 10 km(Skamarock, 2004), and 4–6 hr with that of 4 km (Weiss,2008). These periods are shorter than those used in previous
studies because of the spatial resolution of 1 km in the
innermost domain.
When downscaling was applied in this experiment, thetwo-way nesting method was used, and Fig. 6 shows thethree nested experimental domains. The center point of
the outermost domain is located at 44°N and 141.5°E. The
outermost domain has a grid spacing of 9 km (132 × 120
grids), and this was decreased by a factor of three for eachof the nested domains; hence, the spatial resolution of theinnermost domain is 1 km (208 × 208 grids). The DEM－24－

Fig. 6. Experimental domains used in this study
The largest domain (d01) is represented by the outline of the figure; a circle indicates the location of Rumoi.

Fig. 7. Terrain height of DEM used in d03 (shown in Fig. 6) in the reproduced experiment
The large and small rectangles indicate areas mentioned in the results. The dashed line represents vertical section shown in Fig. 16, where bothends A and B correspond to Fig. 16; gray circle indicates location of Rumoi.

datasets are shown in Fig. 7, and areas mentioned in the
results are shown using two rectangles.
3. Selection of schemes
WRF offers multiple physics options that can be
combined in many ways. Steinhoff et al. (2013) describedseveral physical parameterization schemes of WRF in acase study of a representative summer foehn event over
the McMurdo Dry Valleys in Antarctica. Surface states in
Hokkaido are similar to those described in that case, and assuch, the same schemes can be adopted in the present study.
Therefore, for microphysics, the WRF Single-Moment
6-class (WSM6) scheme was used (Hong and Lim, 2006).
However, according to Skamarock et al. (2008), several
graupel-related terms of this scheme follow those of Lin
et al. (1983), but the ice-phase behavior of the scheme isconsiderably different, owing to the changes made by Honget al. (2004). However, Dudhia et al. (2008) implemented
a procedure unifying the snow and graupel particles byassigning a single fall speed to both that is weighted by the－25－

mixing ratios, and this scheme therefore incorporates ice,
snow, and graupel processes suitable for high-resolution
experiments and is thus used in our study. In addition, theRapid Radiative Transfer Model (RRTM) scheme wasused for long-wave radiation (Mlawer et al., 1997). The
molecular species treated in the model are water vapor,carbon dioxide, ozone, methane, nitrous oxide, and thecommon halocarbons. For short-wave radiation, the Dudhia
scheme was used (Dudhia, 1989). Furthermore, the MellorYamada-Nakanishi-Niino (MYNN) scheme was used forthe surface layer (Nakanishi, 2001; Nakanishi and Niino,
2004, 2006), and for simulation of the land surface, theNoah Land Surface Model (LSM) with soil temperatureand moisture in four layers, fractional snow cover, andfrozen soil physics was used (Chen and Dudhia, 2001). The
planetary boundary layer was represented by the MYNN
level 2.5 (Nakanishi and Niino, 2006), and the cumulus
parameterization was that of the Grell-Devenyi (GD)
ensemble scheme (Grell and Dévényi, 2002).

IV. Result of numerical experiments
1. Wind distribution at 03:00 LST on January 28, 2003
Figure 8 shows the reproduced experimental results
obtained using the WRF model in the study area. A strong
wind of approximately 25 m/s is apparent to the south ofMashike, and a wind speed peak also occurs in the vicinityof Rumoi, reaching approximately 18 m/s. The strong windarea near Rumoi spreads out to the Sea of Japan and forms
a strong wind area over the sea. However, Mashike itself
is associated with a weak wind, and is located between
areas of strong winds. In addition, as seen in the northernpart of Fig. 8, the coast of the Sea of Japan near Haboro is
associated with a relatively weak wind. Therefore, it canbe concluded that a strong local wind occurs in the area of
Rumoi.
Figure 9 shows the wind speed change at Rumoi,
Mashike, and Haboro observed by AMeDAS, and in the
numerical experiment from 21:00 LST on January 27, 2003
to 09:00 LST on January 28, 2003. In the observation result
represented by the solid lines in Fig. 9, all three points
experienced a weak wind of approximately 4 m/s or less at
21:00 LST on January 27, 2003. However, the wind speed
at Rumoi subsequently increased, although there was littlechange in the wind speed at Mashike and Haboro. Thisagain shows that a strong local wind was blowing in Rumoi.
According to the numerical experiment result represented
by dashed lines in Fig. 9, the numerical experiment tends
to overestimate the wind speed especially in Mashike incomparison to the observation, but the overall trend during
this event is well-represented, as is the local wind in Rumoi.
The wind directions for Rumoi and Mashike are SE andE, respectively. In the southeastern part of Fig. 8, the wind

Fig. 8. Numerical experiment result at 03:00 LST on January 28, 2003
Wind vectors represent wind speed and direction, and bold and fine lines represent coastline and isobaric lines of 1 hPa, respectively. In addition,
the shading in the figure indicates the scalar values of wind speed at intervals of 1 m/s. The black circle, square, and triangle indicate location of
Rumoi, Mashike, and Haboro, respectively.

－26－

Fig. 9. Temporal variation of observed and simulated wind speed from 21:00 LST on January 27, 2003 to 09:00 LST January 28, 2003
The dotted vertical line in the center shows the time simulated in the present study.

Fig. 10. Wind speed field (03:00 LST on January 28, 2003) observed by AMeDAS around Rumoi

blows towards Rumoi from this direction. Conversely, inthe northeastern part of the figure, the wind blows from E to
NE, and there is an apparent change in the wind direction.
The result observed by AMeDAS is shown in Fig.
10. Rumoi is determined as being the windiest of all the
AMeDAS observation points. The result also shows that
there is a strong local wind in Rumoi, in contrast to theweak winds observed in Mashike and Haboro (which
agrees with the experimental result). Weak winds are also
apparent across both coastal and inland areas. With respect
to the wind direction, there was also good correspondence
between the numerical experiments and the observations,
except near Mashike, where the observations indicate
a weak northerly wind but the experiment suggests aneasterly wind.

2. Details of events occurring in Rumoi and Mashikeat 03:00 LST on January 28, 2003
The previous section clarified the wind conditions in the
study area. Figure 11 is a magnified figure using the samedata as that presented in Fig. 8, showing the areas aroundRumoi and Mashike, but excluding Haboro. A low pressure
is located in the south side of Mashike, and wind exists inboth the west and east of this area. Additionally, there is awind stream over the area moving from SE to NW. Mashikeis located on the convergence line between two strong
wind areas, indicating that the area is experiencing a weakwind. There is also a weak wind area over the Sea of Japanto the west of Mashike, where SE and E winds appear toconverge.
3. Wind distribution at 15:00 LST on March 8, 2005
Figure 12 shows the results of another numerical－27－

Fig. 11. Same as Fig. 8 (magnified view), but density of wind vectors is different

Fig. 12. Same as Fig. 8, but for 15:00 LST on March 8, 2005

experiment obtained using the WRF model in relation to thestudy area at 15:00 LST on March 8, 2005. The wind speed
at sea is approximately 17 m/s, but when the westerly windreaches the shore, the wind speed weakens and the speed
on land is approximately 10 m/s. There is a strong windblowing along the shore at this time in Rumoi, and the windspeed at Mashike and Haboro is also strong in this respect.

According to the wind direction, the prevailing direction
is westerly in this area, and this is in good agreement withthe pressure gradient from the pressure distribution chart by
MANAL (Fig. 5b). There is no local wind direction change
at this time, and therefore an evident converging line does
not exist. The event is a typical representation of a strong
westerly wind blowing from the sea.

－28－

Fig. 13. Same as Fig. 12 (magnified view), but density of wind vectors is different from that in Fig. 12

Fig. 14. Same as Fig. 10, but for 15:00 LST on March 8, 2005

Fig. 15. Same as Fig. 9, but from 09:00 LST to 21:00 LST on March 8, 2005

－29－

Figure 13 is a magnified figure adopting the same data asthat presented in Fig. 12, where a weak low pressure occurs
in the south side of Mashike, but the wind is not separated
in this area. There is no convergence line, even on close
inspection, and therefore, the wind in Rumoi is as strong asthat in Mashike.
Wind speed fields are shown in Fig. 14. Areas where theobserved wind speed is relatively high (Rumoi, Mashike,
Haboro, Fukagawa, and Yagishiri) are well reproduced,
both in relation to wind speed and direction. However, thewind direction in relation to the inland area (Horonuka,
Tappu, and Shumarinai) is different from the results of
the numerical experiment; the experiment was unable toreproduce the weak northerly and southerly winds.
Figure 15 shows the temporal variation between Rumoi,
Mashike, and Haboro. A strong wind was blowing aroundRumoi at that time, and this tendency is shown in the resultof numerical experiment. However, there was a tendency
for the value of the wind speed to be validated highly in the
numerical experiments, which is the same tendency as forthe previous event.

V. Discussion
1. Consideration of orographic wind
In the previous section, the reproduced experimental

result demonstrated the wind distribution for a single
point in time. In this section, we describe the temporalvariation of the wind speed (shown in Fig. 9) in relation
to Rumoi, Mashike, and Haboro, using both observations
and numerical experiment for the event in 2003. In Rumoi,
the strongest wind measured is approximately 20 m/s;
but the maximum wind speeds in Mashike and Haboro
are approximately 10 m/s and 5 m/s, respectively. Fromthe start time of the experiment, the wind speed only
increased locally in Rumoi, and the wind velocity wasseen to be slightly elevated compared to the observation
result. According to Fig. 8 and Fig. 10, the wind directionin Mashike is not adequately reproduced for either windspeed or direction. This is because Mashike is locatedon the convergence line of wind directions, and theresolution of the numerical experiment was not appropriate
in this respect. The estimated wind speeds in numericalexperiments are systematically larger than those in actual
AMeDAS observations. Gómez-Navarro et al. (2015)
obtained qualitatively consistent results for complex terrain
in the Alps region. They determined that the reproducibilityof the ground surface roughness increases and the average
distance between the observation point and the nearest grid
point is shortened when the spatial resolution is improved,
thereby improving the reproducibility of the wind speed.

Fig. 16. Vertical distribution chart of wind with DEM height in km
Contour lines show equivalent potential temperature in K. Wind vectors represent V and emphasized W component of wind. Both ends A and B
on horizontal axis correspond to those shown in Fig. 7.

－30－

However, in this study, the spatial resolution of numericalexperiments is high at 1 km, and thus the cause of thesystematic overestimation is believed to be the influence ofthe planetary boundary layer scheme. Unresolved orography
is not considered in the MYNN level 2.5 scheme selected inthis research; therefore, even at a spatial resolution of 1 km
it may be insufficient to express the complex terrain of thisresearch area. As an example of finer experiments, Sheridan
and Vosper (2012) carried out numerical simulations with
333-m resolution and reproduced counterflowing wind over
the Owens Valley in the Sierra Nevada of California.
The topography of the region may explain why a strong
local wind is observed in Rumoi. A lone mountain exists
south of Mashike (Mt. Shokambetsu) that reaches a heightof 1492 m above sea level (see Fig. 1). From Fig. 11, it isclear that the prevailing wind around this area is forced tochange direction near this mountain. In addition, the windpasses through the valley-shaped topography between
Fukagawa and Rumoi (see Fig. 1 and Fig. 7), and windspeeds are accelerated in the vicinity of Rumoi. Thissuggests the existence of a gap wind (e.g., Lackmann andOverland, 1989). A similar result was shown in a study ofa local easterly wind “Kiyokawa-dashi” in Japan (Sasaki etal., 2010). To consider the influence of the lone mountain, across-sectional view is shown in Fig. 16. The cross section
dissects the summit of Mt. Shokambetsu, as indicated by
the dashed line in Fig. 7. According to Fig. 16, there isa part of the vertical wind component that is strong, as
evidenced by the waving flow of the wind in both upwardand downward directions. The distribution of the equivalentpotential temperature also varies along the direction of flow,
suggesting the presence of a mountain wave extending to aheight of approximately 10 km, which is in good agreementwith the findings of Ágústsson and Ólafsson (2014).
Kawamura (1963) determined the prevailing winddirection based on wind observation data in Hokkaido, andrevealed the distribution of the surface wind direction in
winter. Kato (1983) conducted principal component analysis
based on the daily average wind speed and atmosphericpressure data in Hokkaido, and created airflow charts. In
these previous studies, it is shown that the wind blows
from ESE and W in Rumoi, but no mention is made of
the strong local wind in Rumoi and its mechanism. In thisstudy, AMeDAS observation data are updated from 1981
to 2010, and strong winds in Rumoi are firstly shown using
an analysis of wind speed and direction from data obtainedin northwestern Hokkaido. The result of the prevailingwind direction is consistent with that in previous studies,and the mean wind speed of Rumoi is 77% and 33% larger
than that in Mashike and Haboro, respectively. In addition,by conducting numerical experiments for the study area or
northwestern Hokkaido, it is shown that the strong local
wind blows due to the influence of the orography aroundRumoi.
2. Sensitivity experiment using surface layer scheme
Finally, a sensitivity experiment was performed toconfirm any change in relation to using particular schemesin the numerical experiments. In Hokkaido, the land surface
is covered with snow in winter, and therefore, the land

Fig. 17. Sensitivity experiment result (03:00 LST on January 28, 2003)
Wind vectors represent the wind speed and direction, and bold and fine lines represent the coastline and isobaric lines of 1 hPa, respectively. In
addition, the shading in the figure indicates the scalar values of wind speed at intervals of 1 m/s. Gray circle indicates the location of Rumoi.

－31－

Fig. 18. Difference between sensitivity experiment and numerical experiment (control run)
Shading in figure indicates difference in scalar values of wind speed, and vectors represent vector differences of horizontal wind speed; black
circle and square indicate locations of Rumoi and Mashike, respectively.

surface schemes used in the reproduced simulation (NoahLSM) include a simple snow and sea-ice model (Chen andDudhia, 2001). For the sensitivity experiment, the land
surface schemes were, therefore, altered to 5-layer thermal
diffusion from the Noah LSM, and the layers are 1, 2, 4, 8,
and 16 cm; below these layers, the temperature is fixed ata deep-layer average (Skamarock et al., 2008). However,there are no vegetation processes or snow schemes in the
5-layer thermal diffusion scheme.
The result of the sensitivity experiment is shown in
Fig. 17 (magnified view). A comparison between Fig. 17
and Fig. 11 shows that there are no significant differencesin the wind directions. With respect to wind speed, astrong local wind at Rumoi and a weak wind area around
Mashike, related to the convergence line of the wind, arealso reproduced. However, the wind speed is decreased toapproximately 2 m/s over the land area. The differences
between results using a sensitivity experiment and thenumerical experiment (control run) are shown in Fig. 18,
which shows an expansion of the strong wind region
around Fukagawa and a weaker wind speed near Mashike.
The wind speed changes greatly around the convergenceline of the wind on the leeward side of Mt. Shokambetsu.
The difference in wind vectors shows complex changes,
and it appears that this wind change is affected by mountainwaves.
Land surface schemes affect ground roughness. Areas
covered with snow are relatively smooth, enabling a higher
wind speed. The result of the sensitivity experiment can beapplied to the discussion of seasonal changes in wind speed

at the same pressure distribution condition, but information
relating to the physical snow depth is included in the initial
data, and this may affect a slight change in the wind in thisexperiment. The statistical analysis in this study clarified
seasonal changes in wind direction at some observation
points around Rumoi. Other numerical experiments need
to be applied to understand the wind spatial distribution insummer and to discuss seasonal changes of wind.

VI. Conclusions
In this study, a statistical analysis using 30-year
AMeDAS datasets of wind speed and direction was
performed. The mean wind speed at coastal locations(Rumoi, Mashike, and Haboro) was higher than those inother areas except Fukagawa. The mean wind speed in
Rumoi was approximately 5.0 m/s, and the wind speed in
Rumoi is relatively strong compared to other locations innorthwestern Hokkaido. With respect to a wind frequency
analysis, Rumoi had the least frequency (7%) of weakwinds (< 5 m/s), and Rumoi recorded the largest number ofdays with a maximum wind speed of more than 10 m/s aswell as the Yagishiri island. As for the wind direction, ESE
wind prevails in Rumoi throughout the year, but in winter,
the westerly wind occupies more than 30% of the wind.
The prevailing wind direction of other points around Rumoi
was also ascertained, in addition to its seasonal changes. It
was also found that the wind tended to blow from the northin winter at several observation points, in contrast to thedirection during summer.
Two numerical experiments using the WRF model were－32－

applied to northwestern Hokkaido around Rumoi in orderto reproduce the observed weather conditions. One wasrelated to a strong wind event in Rumoi and a weak-wind
event occurring concurrently in Mashike and Haboro. The
other event related to a simultaneous strong-wind event
in Rumoi, Mashike, and Haboro. The results showed that
there were good correspondences between the numericalexperiments and observations. The numerical experimentresults showed that there was a strong wind in Rumoi when
the prevailing wind direction was ESE, but Mashike andHaboro experienced weak winds, even when a strong windblew in Rumoi. This demonstrates that the strong wind overRumoi is localized. The driving mechanism behind thisstrong wind is likely to be the result of a gap wind causedby the valley-shaped topography between Fukagawa andRumoi. Additionally, Mt. Shokambetsu is located to thesouth of Mashike, and this generates mountain waves that
affect the weak local wind over Mashike. However, when
the prevailing wind direction is a westerly, the observed
wind speeds in Rumoi, Mashike, and Haboro are almost
equal, because the strong sea wind reaches these points
simultaneously.
A sensitivity experiment was performed to clarifychanges in the land surface model of the meteorologicalmodel. Results showed that by changing the land surface
model to one that did not incorporate snow, there was analteration in the wind speed over land.

Acknowledgement
We acknowledge Associate Prof. Tomonori Sato forhis helpful suggestions, particularly regarding the verticalstructure of the wind. We also thank members of theIntelligent Robots and Systems Laboratory, at the Graduate
School of Information and Science, Hokkaido University,for discussions in the early stages of this work. This
work was supported by JSPS KAKENHI Grant Number
JP16J02111.

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(Accepted on 17 June 2017)－35－

北海道北部西岸における局地的強風に関する解析葛西

光希 1，木村圭司 2，塩見1

敦 4，田殿武雄 3, 4，堀雅裕 3, 4

北海道大学大学院情報科学研究科・院生
23

慶 3, 4，近野

奈良大学文学部

宇宙航空研究開発機構地球観測研究センター
4北海道大学大学院情報科学研究科

要旨
北海道北西部に位置する留萌において局地的強風が観測される。留萌周辺における風について調査するために，30 年間のAMeDAS による風の観測結果の解析と Weather Research and Forecasting (WRF) モデルによる数値実験を行った。
解析結果より，留萌は周辺の他の地点と比較して風が強く，かつ頻度に着目した場合においては他の地点よりも強風が吹く頻度が高い。風向の季節変化については，夏季の留萌においては東南東風が半分以上の割合で吹走する一方，冬季の留萌では西風の割合が高くなるということが明らかになった。
冬季の留萌周辺において局地的強風が吹走する原因を明らかにするため，二つの事例について数値実験を行い，比較を行った。一つは留萌において 12 m/s 程度の強風が吹き，かつ周辺地域では 5 m/s 程度の弱風となっている 2003 年 1 月 28 日午前 3 時の事例である。このときの卓越風向は東南東となっていた．他方は，研究対象地域全体にわたって 10 m/s 以上の強風が吹いていた事例である（2005 年 3 月 8 日午後）
。このときの卓越風向は西風である．実験結果は観測結果と概ねよい一致を得られ，実験結果から留萌で局地的強風が吹走するときの鉛直断面図を作成することにより，付近に山岳波の存在が示された。また，風が谷状の地形を吹走し，留萌周辺で風が加速されている。このことは，地峡風の存在を示唆している。最後に，地表面のモデルについて雪を考慮しないスキー
ムを設定することにより感度実験を行った。風向にはほとんど変化がないものの，風速が 2 m/s 程度減少するという結果が得られた。

キーワード：局地風，数値実験，統計解析，風速，地形の影響－36－