95

Problems in Risk Management of Regional, Local
and Micro-scale Winds in Relation to Global Change
Masatoshi YOSHINO
Professor Emeritus, Tsukuba University, Japanand
Senior Advisor, Sustainable Environmental Development, United Nations University
e-mail:mtoshiyo@poplar.ocn.ne.jp

Abstract
This article first describes winds at regional, local and micro-scales, with special reference to methodsof observation and analysis according to the order of the scales. It shows that in studies at the regional scale,data derived from detailed observations under the network of second-order observation stations andAMeDAS arranged by the Japan Meteorological Agency are utilized, with the main study method being
synoptic climatology. These were established in Japan in 1960s and 1970s. Mesh data were used mainly inthe 1980s and 1990s, presenting air flow in accordance with directions of prevailing upper winds, geostrophic winds, etc.
Regarding local-scale winds, the main topic of concern has been winds occurring under certain
topographical conditions such as in V-shaped or U-shaped valleys, on mountain ridges and along valley sideslopes. Observations are carried out by temporally organized groups using instruments and indicators suchas wind-shaped trees. These studies started in Europe in the early 20th century, but have been undertaken
intensively since the 1950s. For agricultural land use, wind power stations, air pollution, city planning andconstruction of structures such as bridges, tall buildings, highways, etc., knowledge of local wind conditions
has been an essential requirement in recent years. It has been pointed out that these conditions should be
elucidated not only by model simulation or wind tunnel experiments, but also by field observations. This
article also considers changes in winds on the local scale under the influence of global warming, taking an
example of Santa Ana in the U.S.A. It discusses risk problems based on recent studies. The last part deals
with risk management and insurance, based on statistics available.
Key words: global change, local winds, risk management, wind scale, wind-shaped tree

1. Introduction
In order to discuss the problems of risk caused bywinds, the most important points that we should firsttake into consideration are the time and space scales. In
other words, we should discuss the problems from scale
to scale.
Climate on a global scale (macro-scale) as well as regional scale has been studied by modeling during thelast 30 years enabled by the development of computers.
On the other hand, micro-scale simulation studies havebeen made possible by computer or experimentation,such as wind tunnel experiments, to give one example,
because conditions at that scale can be easily abstracted.
In other words, the factors can be sorted relatively easily.
On the intermediate scale (meso-scale) between themacro- and micro-scales, often called the local scale ortopoclimatic scale, studies have been made by fieldobservation and their results have been analyzed mainly
Global Environmental Research
13/2009: 95-106
printed in Japanby description, because the local processes involved are
difficult to analyze physically (Yoshino, 1975; 2008).
The present article intends, therefore, first to describe
the winds at each scale, giving examples.
At the ASEAN foreign ministerial meeting of July
2006, four new sectors were added for high-level cooperation, including rural development and poverty
eradication, women’s issues, disaster risk management
and emergency response, and minerals (de Prado, 2007).
From the viewpoint of wind hazard risks, it is hoped thatsuch cooperation could be established urgently among
the ASEAN countries, where typhoons/cyclones cause
huge amounts of losses: human deaths, economic losses
and structural damage every year.
According to a survey by the World MeteorologicalOrganization on risk reduction activities, “strong winds”
were top ranked among the twenty-eight kinds of disasters occurring in the world (Love, 2009). The Hyogo
Framework for Action negotiated in 2005 sets out priori©2009 AIRIES96

M. YOSHINO

ties for disaster risk reduction and calls upon theinternational community to take practical steps to makecommunities safer. As mentioned in the preface of thespecial issue on “Winds and Risk Management” of the
Global Environmental Research, 13/2009, wind engineering researchers have been intensively promoting
measures in Japan.
The present article intends to contribute to national
and international exchange of knowledge among themany fields of wind sciences.

2. Time and Space Scales
2.1 Scale division
In the years from the late 1920s to the early 1930s,
scale divisions of climatic came into wide use. Therewas general acceptance of calling small-scale phenomena “micro-climate” and large-scale phenomena“macro-climate.” However, there were different names
for intermediate-scale phenomena. The most representative names were meso-climate, local climate, orographical climate, Gelaendeklima or Kleiklima (Geiger, 1961).
This continued until the 1970s (Yoshino, 1975; Bitan,
1975) and seems to be ongoing to the present (Yoshino,2008).
Other pertinent facts are: (1) the absolute distancesof each scale were given by Moerikofer (1947) and thenby Flohn (1959), Yoshino (1975) and Barry (1970);(2) the topoclimate scale was established first by
Thornthwaite (1953) and defined as being intermediate
between the micro-climate and meso-climate; and (3)the micro-climate scale involves regions on a scale of
1 cm to several hundred meters, and the topoclimate(local climate), 10 meters to an order of 10 km.
Although there are several differences among the
researchers, the general definitions can be summarized
as shown in Table 1.
2.2 Risk division according to scale
Risks should be managed in accordance with thescales mentioned above, as shown in Table 2. A flow
chart showing processes of risk assessment and risk
management was presented elsewhere (Yooshino, 2009).
At the macro-scale, which can also be called the global
scale, risks should be managed by international bodies
such as the WMO, WHO, FAO, ASEAN or EU or
national governments. A representative example isglobal warming. At the meso-scale or regional scale,risks should be managed at the level of national bodies,mainly national governments. Hazards caused bytropical cyclones and typhoons are examples. Losses
should be paid by insurance or additional budget outlaysby the governments of the regions affected.
At the local scale, risks should be managed by the
local government or communities. Severe storms or
gales are examples, losses from which can be paid by
insurance or additional budget outlays, same as for the
meso-scale or regional scale cases. At the micro-scale orsmall scale, risks should be managed by individuals

Table 1 Time and space scales (Yoshino, 1975; 2008).

Order ofSpace scale horizontal
distance
Macro200 – 50,000 km
Global
Meso1 – 200 km
Regional
Local
100 m – 10 km
Micro1 cm – 100 m
Small

Time scaledecades, historical,
geologicalmonths, seasons,
years
days, months
hours, weeks, months

Table 2 Risks at various space scales.

Space
Risk
scale
management
Macro- International &Global National bodies
MesoNational bodies
Regional
Local
MicroSmall

Local governments orcommunities
Individuals

ExamplesCovered by

GlobalReinsurance
warmingTyphoon,Insurance,
Hurricane,
Additional
CyclonebudgetSevereInsurance,storms, Gales Additional
budgetSevereInsurance,
tornadoes,
PrivatemarketTorrential
rainthrough insurance or the private market. Tornadoes orextreme wind events which develop in small spaces areexamples.
Risk management comprises generally: (1) recognition and structural analysis of risks, (2) minimizing risks
in selling strategies and structures of commodities and(3) risk transfer, for example, weather derivatives.
To minimize risks, adaptation to changing environments and countermeasures to natural catastrophes suchas extreme events, gales, high wind velocities and highgustiness should be taken into consideration. In suchcases, wind conditions associated with heavy rain andsevere snow storms are also important to consider fromthe viewpoint of wind technology.

3. Wind Studies at Various Scales
3.1 At the regional scale
The regional scale covers areas on an order of about
200 km to several hundred km. For such areas, there arenormally 10-40 meteorological observatories in Japan.
During recent years, there are secondary level automated stations which autonomously record conditions
and send the records to the primary level observatories.
For example, there are about 1,400 AMeDAS stations.
The hourly observed records at these stations since 1976
are available and can be used in regional-scale and local
scale studies, which will be described later. Since the1980s, wind distribution studies have been carried out
making use of these data. The wind velocity and direction distributions are arranged in accordance with thedirection of the gradient winds as representative ofProblems in Risk Management of Regional, Local and Micro-scale Winds in Relation to Global Change

synoptic pressure patterns such as anti-cyclones,
cyclones and fronts. Examples for central Japan include
studies by Suzuki and kawamura (1989) and Suzuki(1991 a, b).
Summarizing the results of such studies, Suzuki(1996) concluded that the mean sea breeze component isabout 1.6 m/s and the land breeze, 0.8 m/s, when there isno synoptic pressure gradient, while the land and see
breezes become smaller as the surface geostrophic
winds become stronger. The land and sea breeze components eventually vanish when the surface geostrophic
wind speeds are greater than 14 m/s. He came to theconclusion also that an extended sea breeze-accompanied wind pattern, shown in Fig. 1, dominates on 63%
of the days in July while a winter monsoon-type pattern
dominates on 53 % of days in December at 15:00 (JST)in the afternoon.
The study methods developed to include simulation
studies making use of mesh data. In particular, theyclarified the structures of sea breezes, up-valley winds
and their combined wind systems on the Kanto Plainand its surrounding mountainous regions. The air
streams formed thermally and topographically were
simulated in two dimensions, including plains, slopes
and hills and in the three dimensions, including seas,
plains, slopes and hills (Kondo, 1990). In the twodimensional studies, calculation was made over 6.67-km
intervals at 60 grid points, and in the case of three
dimensions, by 60 grid points with an upper boundary of
5,900 m. According to the study results in two dimensions, winds intensified in the area between the sea
breeze front and the valley wind. The thickness of themixing layer was almost equal to the valley wind systemin the valleys, but the intensified winds were thicker
than these valley wind systems. By considering thewidth of the valleys, it was shown that the winds were
weaker in broad valleys. Another interesting result wasthat where the distance between the sea coast and mountains is relatively short, the sea breeze develops more

Fig. 1 Typical wind distributions in summer during daytime overcentral Japan. The base of each vector corresponds to an
AMeDAS station. Shaded areas indicate elevation above
600m. A southeasterly “extended sea breeze” can be seenon the Kanto Plain (Suzuki, 1991b; 1996).

97

strongly in the morning hours before noon, but it does
not develop in the afternoon.
3.2 At the local scale
In order to ascertain wind conditions at a scale
smaller than regional, we must observe and analyze thephenomena using wind tunnel experiments, simulation
by models, observed records obtained from ordinary[?]or temporary meteorological observation networks, orfield observation using indicators. One of these studies
in Japan used AMeDAS data, as mentioned above. For
simulation studies in Japan, mesh sizes are normally
50-100m, because they use mesh-sized topographical(altitude of land surface) data provided by the
Geographical Survey Institute; data on roughness of theland surface obtained from topographical maps at ascale of 1/25,000; and land use data of 100 m mesh size
also obtained from 1/25,000 topographical maps. In thefield, temporary observations of winds are carried out
using instruments, such as anemometers and wind vanes,or wind indicators such as wind-shaped trees and windy
landscapes. The following are some examples of thesestudies.
The first example is a large-eddy simulation study(LES), which has made it possible to estimate windconditions over complex terrains (Kondo et al., 1997;
Wood, 2000; Iizuka and Kondo, 2004, 2006; Fujiyoshi,2008) and over urban areas (Kondo et al., 2006; Takemi
et al., 2006 a, b). Recently, Iizuka (2009) summarized
simulation results of the vertical distribution of the mean
wind velocity along the main direction of winds(x-direction) obtained by wind tunnel experiments,
where the S-model (Standard Smagorinsky model),I-model (Inagaki-model), and MI-model (Modified
Inagaki-model) were used, as shown in Fig. 2(a). A
detailed explanation of each model is omitted here due
to space, but they coincide rather well. It is interesting tonote that separation heights on the lee side of the mountain ridge were obtained precisely in this figure,conforming to separation that had been visually demonstrated by wind tunnel experiments about forty years
prior (Nemoto, 1961 a,b,c, 1962; Soma, 1969; Yoshino,1975). In Fig. 2(b), vertical distribution of turbulent
energy on the lee side of the mountain ridge is shown,which also precisely demonstrates the status under aneutral thermal state. After inspection of the comparison,
Iizuka (2009) concluded that the results by the
MI-model showed the best fit to the experiment. There
are, however, still gaps between the meteorologicalsimulation models and LES in terms of scale. Recently,
Ohya and Uchida (2006) reported the results oflaboratory and numerical studies on stable atmosphericlayers.
The Santa Ana winds, a representative local wind in
the U.S., dominate in southern California in autumn andwinter (Conil and Hall, 2006). Hughes et al. (2009)
investigated the response of the frequency and intensityof Santa Ana wind events to anthropogenic climatechange during the last 44 years. To accomplish this local

98

M. YOSHINO

Fig. 2 Vertical distribution of (a) mean wind velocity and (b) turbulent flow energy, along
the main wind direction under neutral temperature conditions. A result of large-eddy
simulation (LES) (Iizuka, 2009).

perspective, they down-scaled reanalysis data from theEuropean Center for Medium-Range Weather Forecasts(ECMWF) and a coarse-resolution climate model with
regional atmospheric models. This dynamic down-scaling technique has been proven to give a more realistic
view of local climate conditions according to recent
studies.
The dynamics of the Santa Ana winds are based on
both synoptic and local conditions: (1) When strong
synoptically forced offshore flow prevails in southern
California, due to the topographical situation, offshore
momentum can be transported to the surface, causing
Santa Ana conditions. (2) Offshore winds are bolsteredby a local temperature gradient between the cold desert
and warmer air over the ocean. The temperature gradientpoints from the desert to the ocean, and is reinforced by
the negative buoyancy of the cold air as it flows down
the sloped surface of the major topographical gaps.
Hughes et al. (2009) presented changes in the number ofSanta Ana days due to anthropogenic forcing, which has
resulted in global warming. Using MM5 (Penn
State/NCAR Meso-scale Model Version 5) and WRF(Weather Research and Forecast model), the total number of Santa Ana Index days from 1959 to 2001 was
obtained as shown in Fig. 3. Here, the Santa Ana Index

Fig. 3 Total number of days with Santa Ana winds for each
winter season, where the Santa Ana Index is greater than6, 8 and 10 m/sec in the MM 5 simulation. The upper righthand illustrates the number of days per season with Santa
Ana Index greater than 10 m/sec in the WRF. The green
bar shows the present day and the yellow bar the future
simulation. All trends are significant at the 99%
confidence level (Hughes et al., 2009).

Problems in Risk Management of Regional, Local and Micro-scale Winds in Relation to Global Change

refers simply to the offshore wind strength at the exit ofa large gap, presenting its time index, SAt.
A decreasing tendency is clear in all cases, suggesting the impact of anthropogenic forcing. It is thoughtthat this reduction is due to differential warming; more
warming in the desert interior than over the ocean in thecourse of transient climatic change conditions.
In local-scale field studies, wind indicators such as
wind-shaped trees are powerful tools, which have beenused since the early 20th century (Yoshino, 1975). An
international scale of wind-shaped trees as climatic
indicators has been proposed (Yoshino, 1989). Deformations of trees as an indicator are still a topic of discussion because of their utility (Cullen, 2002).

99

Alcoforado (1984) studied wind distribution, observing wind-shaped trees in an area (about 12 km × 19 km)
near Cabo da Roca, west of Lisbon, Portugal. Applying
the deformation grade scales proposed by Barsch (1963)
and Yoshino (1973, 1975), she observed wind-shaped
trees (Pinus pinaster, P. pinea, P. halepensis and others)
and presented maps illustrating detailed wind conditionsin the area, as shown in Fig. 4. The main points raisedby her study were: (1) Observations were possible atmany spots with high density. (2) Wind forces and directions could be estimated using the wind-shaped tree
grades. An example of Grade 6, the most severely
shaped grade, is shown in Fig. 5. (3) Observing additional indicators such as hedges, allowed detailed

Fig. 4 Distribution of prevailing winds observed using bio-indicators of winds inthe region surrounding the Serra de Sintra, Portugal (Alcoforado, 1984).

100

M. YOSHINO

Fig. 5 An example of a wind-shaped pine tree (Pinus halepensis),
showing Grade 6 (almost creeping on the ground),according to Yoshino’s scale. This photograph was taken ata point 3 km ESE of Cabo Rosa, Portugal, on 21 Aug.,1986.

prevailing wind directions to be estimated, because thehedges were built in most cases at almost a right angle
to the prevailing wind directions. (4) Because northerly
winds prevailed in this area, air streams in the valleys
running in a north-south direction were strong. She
called this phenomenon “canalization.” On the other
hand, the prevailing northerly winds were noted on thehills. (5) Cyclonic deviation of the winds from theAtlantic Ocean was found on the windward side of Serrade Sintra. (6) The shapes of the trees could be deformed
quite under micro-topographic conditions.
3.3 At the micro-scale
One example of wind damage occurred in Japan
when Typhoon No. 19 struck in 1991, causing the worst
damage in the last several decades, as mentioned later in
Section 4.2. About 660,000 houses were damaged bystrong winds, but the damage was mostly seen on
certain parts of the houses, which is interesting from thestandpoint of wind technology at the micro-scale.
According to a report on net claims for fire insurance inwestern Japan, the number of cases of damaged roofs
was 1,440 (among which 1,248 had slate tile roofs), andthere were 799 cases of interior damage not related to
the roof, 775 of exterior features such as sliding doors
and windows, 336 of attached structures such as antennas, and 682 of unattached structures such as storage
sheds and car ports.
Another survey of damage caused by the same
typhoon in 1991 reported that 10% of houses older thanten years suffered from roof damage at a rate (area ofdamaged roof/ total roof area) of about 45%.
Such statistics are important to the study of windinfluences at the micro-scale, but, unfortunately, we
have very limited survey results.
Tornadoes, tatsumaki in Japanese, are another important subject among problems at the micro-scale in Japan.
The mean annual number of tornado occurrences isabout fifteen in Japan. About 80% of the tornadoes
cause damage. Because they occur under the synoptic
situation of typhoons, southern Japan suffers more ofthem than Honshu. Damage from tornadoes occurs
52 times annually in Okinawa Prefecture and 38 in

Kagoshima Prefecture. It is to be noted, however, thatsevere cases are seen occasionally on the Pacific-side
plains of Honshu, such as in Aichi Prefecture, Chiba
Prefecture and the Kanto Plain. In recent years, tornadoes have frequently occurred on the plains on the Japan
Sea side, such as in Tottori, Toyama and Niigata Prefectures. The reason they are occurring in these areas canbe considered related to their topographical situationand land-sea distributions under certain synoptic situations.
Damages, occurred by tornadoes in Japan have beenreported based on case studies during the recent years(Matsui et al., 2009). They came to conclusion that theactual wind damage to structures was a little different
from the assumption in their design. For example,
enclosed conditions were sometimes not achieved andthe major load was not always wind pressure but impact
of flying debris.
Tornadoes are a severe hazard in the U.S. They havebeen actively studied for more than half a century. The
most destructive tornadoes form in and aroundmeso-cyclones. They are produced by super-cells, rotating thunderstorms that have acquired a circulation, also
known as meso-cyclones. The “average” tornado is
100m wide on the ground and proceeds on a path 1 km
long, with a maximum wind velocity of 45 m/sec. The
number of tornadoes has been increasing in the U.S.
during the period from 1950 to 2007 (Schmidlin, 2009).
However, it should be noticed that violent tornadoes, EF
4 and EF 5, have been decreasing in contrast to theincreasing trend of relatively weak tornadoes. The
upward trend may be due to a larger number of weak
tornadoes, or it may be due to better reporting andrecording of tornadoes. Many of these were not reported
in the middle of the 1900s when the population was notas big and there was less tornado awareness, no warnings and no TVs.
3.4 Future problems
Problems in studies at the meso-scale have beensummarized by Suzuki (1996). Here, future problems at
the local and micro-scale are summarized as follows: (1)It is difficult to evaluate the effects of micro-scale
topography and obstacles such as dams, bridges, buildings and towers. (2) The frequency distribution curvesof winds differ from point to point under the influenceof micro-topography. This makes it difficult to estimate
the extreme values of wind speed with high accuracy,
which is needed in wind engineering. (3) It is difficult toevaluate the effects of orographically-formed clouds
above the ridges of mountains, which are controlledstrongly by valley winds and up-slope winds during sunshine hours and thermal conditions in mountainous
regions. (4) The effects of even the same forms ofvalleys, mountain slopes and mountain ridges on windconditions differ according to their actual directions. (5)When air streams cross over a mountain ridge,
precipitation occurs on the windward slope and a drydescending wind on the lee slope due to the Foehn effect.

Problems in Risk Management of Regional, Local and Micro-scale Winds in Relation to Global Change

It is difficult, however, to evaluate the grade (intensity,
seasonality, diurnal changes etc.) of this effect.
At the small scale, we must consider the following
points further: (1) In valleys, winds with components of
not only x- and y-directions, but also z-direction should
be observed and analyzed in relation to topographicalsituations. In particular, the behavior of eddies in thez-direction may play an important role in planning of
construction and understanding the risks to traffic passing over bridges/higher banks. (2) The effects of
micro-topography on wind conditions differ in accordance with the length of period chosen for calculating
mean wind velocity. Therefore, the shape of the windrose, frequency in each of the 16 directions, etc., differ
with the period: years, seasons, months, days, and daytime/nighttime hours. More detailed statistical analyses
are needed. (3) At the points on a valley floor wherebranch valleys meet with the main valley, wind directions are complicated. At the present stage of our knowledge, they can be estimated only empirically. (4) We
need knowledge about the future status of wind conditions in valleys and on mountain ridges under the influence of global warming. For the purpose of risk assessment/management, this is important. (5) Diurnalchanges in wind systems are clear. Theoretical studies
have been done since the first half of the 20th century,
but in the natural field, it is not always easy to evaluate
wind behavior in valley bottoms, along either valleyside slope or on ridges. These should be surveyed using
plant indicators from place to place. (6) Because windtunnel experiments have been done under conditions ofthermally neutral air layers, the results should be interpreted carefully with regard to the natural field underlapse conditions in the daytime and inversion conditions
at night with weaker synoptic circulation systems.
(7) Experiments for specific purposes should bedesigned. For example, because actual tornadoes arevery complex, it is hoped that experiments will be
developed to elucidate the flow fields and pressure
fields of tornado-like swirling flows and their effects.
Such studies have been designed under the “Global
COE Research Projects” by the Wind Engineering
Center, Tokyo Polytechnic University. (8) From thestandpoint of ecology, Hollig (1992) discussed crossscale morphology, geometry and dynamics of
ecosystems. Analogically, cross-scale or trans-scaleconsideration can be introduced to the problems underdiscussion. An initial attempt along this line wasreported by Takahashi et al. (2006).

4. Winds and Risk Management
4.1 Strategy
Risk management is designed in several steps: Firstly,
atmospheric phenomena are uncertain even in shortterm forecasting and therefore they can be projected asprobabilities with significant levels of uncertainty. Gusts
as short-term atmospheric phenomena can be classified
at this level. Secondly, although we have a high degree101

of confidence that average temperatures will increase
because of global warming, there is a very low degree ofconfidence that average the wind velocity will increaseor decrease. Thirdly, extreme events such as peak gustswith tropical low pressure circulations and downbursts
associated with strong cumulonimbus formation or
tornadoes are much harder to predict than changes inaverage values. These events, however, pose a great risk
to buildings.
Scientific and technical groups have been contributing to reduction of risks from wind-related disasters at
the country and international levels for a long time, forexample, the International Association of Wind Engineering (Solari, 2009) It must be stressed that they are notnecessarily linked closely with policy-making processes,
but the important point is that wind-related disaster
reduction cannot be done by wind engineers alone
because of its complexity.
The Bureau of Rural Sciences (BRS) of Australia has
been encouraging a risk management approach to dealing with climatic variability and changes which was
adopted first by the insurance industry. This approach
treats the risks posed by climatic variability and changes
in the context of other business risks, placing strongemphasis on review with identification, analysis, evaluation and management of challenges and opportunities(Clark et al., 2006; Grant et al., 2007; Steffen et al.,2006). These were proposed regionally for the impactsof floods and severe droughts on agricultural production
originally, but the main points can be applied to strong
wind impacts on building technology.
Risks are defined several ways as follows：
Risk = Hazard + Vulnerability ···························· (1)Risk = Hazard × Vulnerability ···························· (2)Risk = Probability × Losses································ (3)Risk = Probability + Vulnerability ······················ (4)Risk = Hazard × Consequence···························· (5)In the present paper, I do not discuss each definition
in detail, but definitions (1) and (2) would be more suitable for risks at smaller scales and definitions (4) and(5) at larger scales. Definition (3) is thought to be suitable for phenomena at all scales. In recent years, insurance has been discussed in relation to climate change(Dlugolowski, 2004; Miyoshi, 2008). Some of these
discussions will be cited later. In China, these definitions have been introduced (Wang, 2008) for the studyof risks and insurance for flood damage in the middle
reaches of the Yangtze River. From the standpoint ofwind technology, it is hoped that studies will be encouraged based on these definitions.
4.2 Risk management and insurance at various
scalesZhang and Li (2007) summarized basic theories forrisk assessment and management of meteorologicaldisasters caused by typhoons, introducing a risk index.
They proposed five classes based on empiricallydetermined ranges, as shown in Table 3. The ranges of
the areas indicating space scales are shown in terms of

102

M. YOSHINO

numbers of deaths, population affected, houses damaged
and economic losses in China from the end of the 20th
century to the early 21st century, which may change
according to development of social and economic
conditions. It is important, however, to examine therisks quantitatively at their respective time and spacescales in their past, present and future status. Table 3
will contribute to this purpose.
Important points in assessing risk costs (the cost oflosses) in insurance include: adequacy without excessiveness, being not unfairly discriminatory while making insurance available and affordable (Miyoshi, 2008).
Miyoshi also stressed the importance of considering loss
trend factors and loss prevention, in other words,
decreasing the frequency and amount of losses. A
statistical approach to risk management is first to collectinformation on cases and total amounts of losses (cost,
number of people affected, number of deaths, etc.).
These have been particularly significant in the analysis
of natural hazards, including wind damage in the past.
Their scales are mainly regional to macro-scale, in
accordance with the scale division described above. On
the other hand, it would be better to apply technologicalrisk assessments to the present problems in wind damage caused by extreme events occurring, for example, in
association with typhoons (routes, minimum pressure,maximum wind speed, etc.), as well as building construction, city planning, etc. These are mostly approaches at the micro-scale as mentioned above.
As an Asian total, the cost of direct damage fromtropical cyclones increased by more than five times in
the 1980s and about 35 times in the 1990s, as comparedwith the cost in the 1970s (Yoshino, 2006). The costsrose about three times and eight times, respectively, forflood damage. Table 4 shows the amount of insurance
payments in the case of natural disasters, excluding
earthquakes and tsunamis. From the first to the ninth
rank, payments occurred in association with typhoons.
Only in the case of the tenth rank, in which a hailstorm
caused the damage, was it different. It is also noteworthy that no damage was caused by strong winds alone,
because they occur mostly at the micro-scale or local
scale.
The amount of damage caused by natural hazards has
been increasing tremendously in high-income countries
in Asia since the 1990s. Before the early 1990s, the total
amount of natural hazard damage was highest on the
North American continent, but it has shifted since thento Asia. The increasing rate in the number of deaths,
however, has not changed in parallel with the increase indirect natural hazard damage. In the high-income countries of Asia, like Japan, the number of deaths has shown

Table 3 Classes of damage caused by typhoons and their standard values in the middle reaches of the Yangtze
River, China.

DamageClass
Affected Area (km2)
Deaths (persons)Population affected(persons)
Houses destroyed(10,000 houses)
Direct economic
losses(10,000,000 yuan)

Most severe
5More than 1,000
More than 100

Severe4500-1,000
20-100Medium3
200-500
10-20

Weak
2
50-200
0-10

Weakest10-50
0More than 2,000

500-2,000

100-500

10-1001-10More than 10

5-10

1-5

0.1-1

0-0.1

More than 1,000

300-1,000

100-300

10-100Less than 10(Zhang and Li, 2007)

Table 4 Payment of insurance in cases of natural disasters, Ranks 1 to 10 in Japan, 1991-2008.

Rank

Cause

Region

Date

1
2
3
4
5
6
7
8
9
10

Typhoon No.19
Typhoon No.18
Typhoon No.18
Typhoon No.7
Typhoon No.23
Typhoon No.13
Typhoon No.16
Heavy rain
Typhoon No.13
Hail

All of JapanAll of Japan
KyushuKinki
West Japan
KyushuAll of Japan
Aichi PrefectureWest Japan
Chiba, Ibaragi Prefectures

25-26, 9, 1991
4-8, 9, 2004
21-25, 9, 1999
22, 9, 1998
20, 10, 2004
15-20, 9, 2006
30-31, 8, 2004
10-12, 9, 2000
3, 9, 1998
24, 5, 2000

Total payment(× 100 million yen)
5,679
3,874
3,147
1,600
1,380
1,320
1,210
1,030
977
700

Problems in Risk Management of Regional, Local and Micro-scale Winds in Relation to Global Change

a decreasing tendency. One example is shown later inTable 6. Considering these tendencies, risk managementin Asia at the regional scale will change rapidly in the
coming decades.
The long-term tendency of insurance payments
worldwide has shown a sharp increase from the 1990s,
according to the GEO Risk Research Department,
Munich RE Report. However, its rate of increase isabout one-third the long-term increase in economic
losses, which is one of the more severe problems seen
globally. Another problem is great year-to-year variation,
which should be studied more in detail.
The amounts of net insurance claims worldwide
from 1970 to 1997 in the cases of severe disasters areshown in Table 5. Among the top ten cases, tropical
cyclones occupy six cases as seen globally. Hurricane

Katrina hit New Orleans, Louisiana, U.S.A., on
29 August 2005, resulting in a tremendous amount ofdamage, with 1,833 deaths, about 500,000 affected, economic losses of 125 billion US dollars, and net insurance claims of 41.1 billion US dollars. These will be
discussed later again.
It is noteworthy that the most severe damages
occurred in the case of storms in Europe in winter andautumn and blizzards on the east coast of the U.S. inwinter, as shown in Table 5. This should be noted,
because the damages were caused not by tropical
cyclones, but by strong winds in colder seasons inregions insured at relatively higher rates.
Shah (2009) dealt with the insurance problems inlower income countries. He proposed micro-insurance,
which should particularly (i) be able to protect against

Table 5 Payment of insurance in most severe cases of disasters worldwide, 1970-1997.

Rank

Cause

1
2
3

Hurricane Andrew
Typhoon No. 19
Winter storm “Daria”(strong winds)
4
Hurricane Hugo
5
Autumn storm
6
Winter storm “Vivian”(strong winds)
7
Hurricane Opal
8
Blizzard
9
Hurricane Iniki
10
Hurricane Frederic(For comparison)
Hurricane Katrina[Earthquakes]
South California
Hanshin-Awaji

U.S.
Japan
Europe24. 8. 1992
27. 9. 1991
25. 1. 1990

Total payment(× million US dollars)
18,286
6,542
4,636

Puerto Rico
EuropeEurope15. 9. 1989
15. 10. 1987
26. 2. 1990

5,427
4,230
3,917

U.S.A.
U.S.A.
U.S.A.
U.S.A.

4. 10. 1995
10. 3. 1993
11. 9. 1992
3. 9. 1979

2,211
1,943
1,829
1,660

U.S.A.

29. 8. 2005

41,100U.S.A.
Japan

17. 1. 1994
17. 1. 1995

13,5292,603

Region/Country

Date(Data source: Swiss Re, Sigma and others)
Table 6 Ratios of economic losses and insurance damages to GDP, insurance damages to GDP,
numbers of persons affected and deaths in extreme cases during the last 20 years.

CausesCountry
Date
GDP* (A)Economic losses* (B)Insurance damages* (C)Number of
persons affected
Number of deaths
Ratio B / A
Ratio C / A

Hurricane
Katrina
U. S. A.
29 . 8. 2005
12,421.9
125
41.1

Typhoon No.19
in 1991Japan
27. 9. 1991
3,454.310
5.7

500,000
1,833
1.01%
0.0033%

91,12862
0.29 %
0.0016%*Billions of US dollars
GDP values are for the year of the respective events.
(Collected from various sources)103

Cyclone Nargis
Myanmar
2. 5. 2008
13.5
4.1
not available
2,400,000
138,000
30.3 %―

104

M. YOSHINO

all natural catastrophes including hurricanes/cyclones/
typhoons, etc., (ii) have an affordable premium structure,and (iii) be easy to adapt and adopt; that is, they shouldhave a simple premium structure, with simplified claim
adjustment, benefits and rules (if any) and simple trigger
definitions for claims. Through this, lower income countries would have a chance to access capital markets.
In Table 6, a comparison is given, taking examples of
Hurricane Katrina in the U. S., Typhoon No.19 in 1991
in Japan, and Cyclone Nargis in Myanmar. There areclear differences among the cases in the ratios of economic losses to GDP, insurance damages to GDP, andnumbers of deaths and people affected, representing
regionally the conditions of developed and developing
counties.
In 2004, ten typhoons landed on the Japanese islands,
the maximum number seen since the beginning of meteorological observation in Japan. Insurance covered anamount of more than 72 billion US dollars, but it wasestimated that the economic losses were more than several times this amount. Table 7 shows changes in net
claims paid in Japan for fiscal years 1948-1997. In thistable, it is clear that net claims paid in Japan increased
very sharply, rising about 60% from the 1980s to the1990s, and we can understand how high the amount of
72 billion US dollars mentioned above is as comparedwith the mean value of the 1990s. When we consider
future risks, these tendencies cannot be ignored, because
they are much higher than the rate of increase caused by
the estimated inflation rate. In other words, the increasing value of economic losses should be taken into
consideration in risk management.
Conventional reinsurance arrangements will betested if extreme events increase in frequency and/or
severity. Generally, the capital in insurance markets is
insufficient to cover these losses. Insurers are looking
for alternative risk transfer mechanisms to help diversity
their capital and manage liquidity problems following aseries of large claims. Because severe storms, stronger
tornadoes, high turbulence caused by complexes ofbuildings, etc., will result in huge amounts of economic
losses in the future, the reinsurance structure should befurther studied in depth, considering wind technology as
well.
Table 7 Secular changes in net claims paid* in Japan forfiscal years 1948-1997.

Year

Net claims(million
US dollars)
Mean

Year

Net claims(million
US dollars)Mean

1948-195053

1971-1980

7,374

1951-1960

188

1981-1990

17,171

1961-1970

1,288

1991-1997

28,613(Data source: Insurance Almanac, ed. by Insurance Statistic ResearchGroup, published by the Finance Bureau, Ministry of Finance, Japan)*Includes all kinds of insurance, such as fire insurance, automobile
insurance.
(Converted into US dollars at a rate of 1 US dollar = 120 Japanese yen)

5. Summary
This review article first discusses time and space
divisions, reviewing the results of previous studies in
the last half of the 20th century. According to these scale
divisions of winds and their impacts, risk division ispresented in relation to risk management.
At the regional scale, distributions of wind velocity
and direction are analyzed mainly for every 16 or 8
directions of gradient wind or prevailing upper winddirections. They have clarified the boundaries of sea
breeze invasion on coastal plains and their connection tovalley winds in surrounding mountainous areas inCentral Japan. Simulation studies have elucidated thestructures of sea breezes and valley winds. They haveshown the thickness of these winds, their diurnalchanges and their relation to topography.
At the local scale, there have been wind tunnel
experiments, simulation by models, analysis of observed
records by the network of the meteorological observatories and temporary field observations using instruments
or some indicators such as wind-shaped trees. Examplesof each method are introduced. The results of simulation
studies at various scales are discussed, presenting the
local and micro-scale wind structure.
Future problems are summarized firstly on theregional scale and secondly on the local scale in detail.
One of the important points is thought to be cross-scaleor multi-scale analyses. Human reaction and adaptation
should be considered at the various scales.
In the final part, winds and risk management are discussed. After reviewing the definitions of risks, anexample of a study on the middle reaches of the Yangtze
River in China in the case of strong typhoons is mentioned. Also, the amounts of direct economic losses inJapan are discussed in relation to insurance, which haveshown a sharp increase in recent years. Ratios of economic losses to GDP and insurance damages to GDP are
one order higher in the U.S. than in Japan. Economic
losses in developing countries are smaller, but the numbers of deaths and persons affected are two orders larger.
Insurance and reinsurance problems are discussed, taking into consideration small-income countries.

Acknowledgment
I would like to express my sincere thanks to Prof.
Yukio Tamura, Tokyo Polytechnic University, and Dr.
Rikie Suzuki, JAMSTEC, Yokohama, for collecting
references of the present article.
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(in Chinese)

Masatoshi YOSHINO
Born in Tokyo on 1 January 1928,
Masatoshi Yoshino graduated from Tokyo
Bunrika University, BA in 1951 and MA
in 1953, majoring in geoscience, particularly climatology. He obtained his
Doctor of Science in 1961. He was a
research fellow of Alexander-vonHumboldt Foundation, 1961-63, at the
University Bonn, Germany. He was an
Associate Professor at Hosei University, Tokyo, 1968-70, Full Professor at
the same University, 1970-74, Full Professor at the Institute of
Geoscience, University of Tsukuba, Japan, 1974-91 and at the Departmentof Geography, Aichi University, Japan, 1991-97. Currently, he is Professor
Emeritus at the University Tsukuba and Senior Programme Advisor,
Sustainable Environmental Development, United Nations University. In
the past he has served as President of the Association of Japanese
Geographers and of the Japanese Arid Land Studies, Vice-president ofInternational Geographical Union (IGU) and Former chairman of the
National Committee of International Geosphere-Biosphere Programme(IGBP) in Japan. He has published many books, including “Climate in asmall area,” (University of Tokyo Press, 1975), in English, 549 pages, andabout 300 articles. He served as a chairman of the editorial committee of‘Global Environmental Research’ for Volumes 1 to 6.

(Received 11 December 2009, Accepted 25 December 2009)