Journal of Heat Island Institute International Vol. 12-2 (2017)

Academic Article

Summer Heatstroke Risk Prediction for Tokyo in the 2030s
Based on Mesoscale Simulations by WRF
1

2

Masataka Kasai*

Tsubasa Okaze*

1

Miguel Yamamoto*1

Akashi Mochida*

3

Kazumasa Hanaoka**1 Department of Architecture and Building Science, Graduate School of Engineering, Tohoku University* School of Environment and Society Department, Architecture and Building Engineering, Tokyo Institute of Technology*3 Department of Geography, College of Letters, Ritsumeikan University
2

Corresponding author: Masataka KASAI, kasai@sabine.pln.archi.tohoku.ac.jp
ABSTRACTThis study aims to quantify the spatial distribution of summertime outdoor heatstroke risk in the Tokyo
metropolitan area of Japan. Heatstroke risk was quantified by assessing the overlap of hazard, vulnerability, andexposure. As the hazard index, the daily maximum wet-bulb globe temperature (WBGT) was selected. To predict
the spatial distribution of the WBGT at urban scale, mesoscale meteorological simulations using Weather Researchand Forecasting (WRF) model were performed. The actual daily incidence rate of heatstroke, which was assumed to
be a function of the observed daily maximum WBGT, was estimated as the vulnerability index. The monthly
incidence rate was obtained using the hazard and vulnerability indices. Finally, the total number of heatstroke
patients per month could be estimated as a heatstroke risk index by multiplying the monthly incidence rate by the
population density. To understand the impact of climate change on the heatstroke risk, the current (2000s) and near
future (2030s) risk distributions were estimated by applying the proposed method. The risk increased by more than20 at coastal regions in the 2030s, owing to changing climate conditions such as increase in humidity.
Key Words : Heatstroke risk, WBGT, Incidence rate, Future prediction, Tokyo metropolitan area, WRF

１. Introduction(WRF) model was used as the RCM, and MIROC4h data wasphysically downscaled using the WRF. Using the WRF

During the summer of 2010, Japan experienced a serious heatsimulation results, the standard weather data for the future

wave in which the number of heatstroke patients transported by(2030s) were constructed for major cities in Japan such as Tokyo,

ambulance in Tokyo rose to approximately ten times that of

Osaka, Nagoya, and Sendai.

(1)

previous years . Since 2010, the number of heatstroke patientsOn the other hand, several previous studies have estimated thehas remained at elevated values. The Intergovernmental Panel on

mesoscale heatstroke risk. Ohashi et al.(6) used the daily(2)

has concluded that continued

maximum wet-bulb globe temperature (WBGT) as an index ofwarming of 0.3 °C to 4.8 °C by the end of the 21st century is

the outdoor heat stress and estimated the number of heatstroke

probable. Therefore, an evaluation of heatstroke risk underpatients as the heatstroke risk in Tokyo in 2010. Kikumoto etfuture climate conditions is required in order to take theal.(7) estimated the incidence rates of heatstroke both in theappropriate countermeasures.

current and near future in the Tokyo metropolitan area. However,

Climate Change (IPCC)

The working group on future standard weather prediction using

the effect of the population distribution was not considered, andglobal climate model (GCM) at the Architectural Institute of

the absolute number of heatstroke patients was not discussed.

Japan has developed a method to construct standard weather

Recently, the authors(8) developed a method to estimate thedata used to calculate the building thermal load(3)-(4)absolute number of outdoor heatstroke patients as the heatstroke. The

working group used data from the Model for Interdisciplinaryrisk, and validated the method for the summer of 2010 in Sendai,

Research on Climate version 4 (MIROC4h) (5), which is one ofJapan.

the GCMs, as the initial and boundary conditions for a regional

To understand the impact of climate change on heatstroke risk,climate model (RCM). The Weather Research and Forecasting

this study aims to predict the distribution of the outdoor- 61 -

incidence rate of heatstroke and the number of outdoor

September in 2010 and 2011. We extracted only outdoor cases

heatstroke patients both currently (2000s) and in the near futurefrom the transport data to estimate the vulnerability index in this(2030s) in August in the Tokyo metropolitan area of Japan,

study. On the other hand, we estimated the daily maximum

based on mesoscale meteorological simulations with WRF.

WBGT using hourly-observed meteorological data near the
Tokyo station (Otemachi) by the Tokyo District Meteorological

２. Method for estimation of heatstroke risk

Observatory,Japan

Meteorological

Agency.

Previous

studies(6)-(7) used observational data from Otemachi to reveal the
Figure 1 illustrates the conceptual model for disaster risk

relationship between the WBGT and incidence rate in Tokyo.

evaluation(9). In the field of disaster prevention, the risk of each

We followed these and used meteorological data from the same

disaster is largest for locations where hazard, vulnerability, andobservatory. Using this data, the relationship between the daily

exposure intersect. Hazard represents dangerous factors thatmaximum WBGT and daily incidence rate of outdoor heatstroke

cause natural disasters. Vulnerability represents potential

patients transported by ambulance per 1,000,000 people per day

weaknesses that increase the disaster risk for a region. Exposure(IR day [persons/(1,000,000 populationday)]) was obtained.

represents the number of people or amount of time spent①Hazardexposed to the hazard, and is generally quantified by the
population in the region.
In this study, risk was defined as the number of outdoor
heatstroke patients transported by ambulance for a given

Risk

location and time period. The three indices of hazard,③Exposurevulnerability, and exposure were defined as the daily maximum②Vulnerability

WBGT, the relationship between the daily maximum WBGT
and the daily incidence rate, and the daytime population density,
Fig. 1: Concept for disaster risk(9)respectively.
(10)

The outdoor WBGT is generally calculated from Eq. (1)
WBGT  0.1Ta  0.7 Tw  0.2 Tg:(1)Daily incidence rate (IRday)[persons/(1,000,000 population・day)]

5

where Ta is the dry-bulb temperature [°C], Tw is the wet-bulb
temperature [°C], and Tg is the globe temperature [°C]. In thisstudy, the distributions of the hourly WBGT were estimatedusing several meteorological factors simulated by WRF. The
details of the WRF simulations are described in Chapter 3.
Although Ta was equal to the air temperatures obtained directlyfrom the WRF simulations, Tw and Tg were estimated fromseveral WRF outputs, respectively. Tw was estimated using air
temperature, absolute humidity, and atmospheric pressure(11)-(12).
Tg was estimated from Eq. (2) (13):IRday = -1.9 10-6θn6 + 2.9 10-4θn5 - 1.8 10-2θn4+ 5.5 10-1θn3 - 9.6θn2 + 8.6 10θn - 3.1 102

4

R2 = 0.72
3

2

1

0
19 20 21 22 23 24 25 26 27 28 29 30 31 32 33Ta  12.1  0.0067 S  2.40 v1 / 2 S  400 W m 2(2)
Tg  
1/ 2
S  400 W m 2
Ta  0.3  0.0256 S  0.18 v
where S is the global solar radiation [W/m2] and v is the wind

Daily maximum WBGT (Wmax) [℃]
Fig. 2: Relationship between the daily maximum WBGT and IRday[persons/km2]

velocity [m/s]. The solar radiation to the ground surface andhorizontal wind velocitiy at 10-m were obtained from the WRF
Saitamasimulations and used for S and v in Eq. (2), respectively. Finally,

Ibaraki

200,000

the daily maximum WBGT was obtained from the hourly
estimates and used as the hazard index for heatstroke riskTokyoevaluation.
To define the vulnerability index, the relationship between theobserved daily maximum WBGT value and the actual recorded

Kanagawa

number of heatstroke patients transported by ambulance in

ChibaTokyo’s 23 wards was analyzed. The emergency transport data0

included the transport dates and places where heatstroke
occurred for each heatstroke patient recorded from May toFig. 3: Daytime population density in the Tokyo metropolitan area

- 62 -

Figure 2 illustrates the IRday for Tokyo as a function of the daily

simulations in the 2000s and 2030s based on the monthly

maximum WBGT. Figure 3 illustrates the spatial distribution of

average air temperature in August by MIROC4h. During thethe daytime population density per 1 km2 in the Tokyo

period from 2006 to 2010, specifically in 2007, the monthly(14)

average temperature in the Tokyo metropolitan area was closest

and the Economic Census of 2009(15), which was used as theto the regression line of the observed value from 1981 to 2010.

exposure index of the risk evaluation. To analyze exclusively theThus, we selected the summer of 2007 as the representative

impact of climate change on heatstroke risk in the future, it wassummer in the current situation. Further, we selected the summer

metropolitan area, based on the Population Census of 2010

assumed that the population density would remain the same.

of 2031, in which the monthly average temperature wasUsing the three indices representing hazard, vulnerability, andapproximately 1 °C higher than that in 2007 as the

exposure, heatstroke risk is estimated as shown below:

representative summer of the near future situation. We ran WRF

IRday   1.9  10 6  Wmax  2.9  10 4  Wmax6

 1.8  102

 Wmax  5.5  10
4

1

5

 Wmaxsimulations for both summers from July 15 to September 1. The(3)

3

first two weeks were used for spin-up.

 9.6  Wmax  8.6  10  Wmax  3.1  10 2
2

max

3.2

ComparisonofresultsformeteorologicalIRmonth   IRday(4)

Risk  IRmonth  PD  10 6

(5)

From the WRF simulations, the meteorological factors needed

where Wmax is the daily maximum WBGT. Equation (3) gives

for calculating the WBGT could be obtained. Figure 5 illustrates

the polynomial approximations for the IRday of Tokyo as a

the spatial distributions for differences in the monthly averagesimulations in 2000s and 2030sday1function of daily maximum WBGT, as seen in Fig. 2. The
determination coefficient (R2 value) of Eq. (3) was 0.72. By

Urban (Commercial)
Irrigated Croplandsumming IRday during one month, the monthly incidence rate

Urban (High-rise)
Urban (Low-rise)
Dry Cropland
Grassland
Mixed Forest
Water bodies
Barren120 km(IRmonth [persons/(1,000,000 populationmonth)]) was calculated

Domain3

Domain1(Eq. (4)). Finally, by substituting IRmonth and the daytime
population density (PD [persons/km2]) in Eq. (5), the absolute

120 kmDomain2

number of heatstroke patients transported by ambulance (Risk[persons/(km2month)]) was estimated. The accuracy of theTokyo Sta.

method has been validated by comparing the predicted numberof heatstroke patients in Sendai of 2010 with the actual recorded
number of patients(8).
Fig. 4: Domains for WRF simulationsand land use map of Domain 3

３. Mesoscale meteorological simulations with WRF
Table 1: Calculation conditions for WRF simulations

3.1

Date

Outline of simulations

Mesoscalemeteorologicalsimulations

with

WRF

were

Grid Arrangement

performed to obtain distributions of the daily maximum WBGT

21:00 (JST) Jul. 15 ~ 21:00 (JST) Sept. 1
Dom. 1: 72  72
(Grid size: 25 km)
Dom. 2: 150  150 (Grid size: 5 km)
Dom. 3: 120  120 (Grid size: 1 km)

for use as the hazard index. The calculation results of a GCM

Time Interval

Dom. 1: 90 s, Dom. 2: 30 s, Dom. 3: 6 s

named MIROC4h(5), which followed the RCP4.5 scenario(16),

Number of Vertical Grids

34 (from the surface to the 50 hPa level)

Topographic Data

Doms. 1, 2: U.S. geological survey
Dom. 3: Japanese national land numericalinformation(17)Nesting

One-way nesting

was selected as the global scale meteorological data. Since thehorizontal resolution of MIROC4h data is approximately 60 km
 60 km, we could not analyze the details of the thermal
environment at the urban scale. Hence, we conductedsimulations with a finer grid resolution by WRF using

Table 2: Physics schemes for WRF simulations

MIROC4h data for the initial and boundary conditions, toMicrophysics

WRF single-moment 6-class scheme

predict climate conditions at the urban scale. Figure 4 shows the

Shortwave Radiation

Dudhia scheme

Longwave Radiation

Rapid radiative transfer model scheme
Noah land surface model+ Single-layer urban canopy modelcomputational domains for WRF and the land use distribution in
the smallest domain, namely Domain 3. We focused on theLand Surface

Tokyo metropolitan area. The calculation conditions and physics
schemes used in the WRF simulations are summarized in Tables
1 and 2, respectively. The horizontal resolution of Domain 3 was
1 km  1 km. We selected representative summers for WRF- 63 -

Planetary Boundary Layer

Yonsei University scheme

Cumulus Parameterization

Kain-fritsch (new Eta) scheme

(1) air temperature and (2) absolute humidity at a height of 2-m

average sea surface temperature used as the boundary condition

and time of 12:00 p.m. between 2007 and 2031. The monthlyof WRF near Tokyo in 2031 increased 1.6 °C compared withaverage inland air temperatures at noon in the future were higherthat in 2007, while the increase in monthly average land surface

than 31 °C even though that of the coastal area was relatively

temperature was only 0.3 °C. This increase in sea surface

low, owing to the cooling effect of the sea breeze. However, thetemperature led to the increase in air temperature on the sea.

increase of temperature from 2007 to 2031 in the coastal area

The humidity along the coastal areas was higher than that of thewas larger than that of inland area as seen in Fig. 5 (1). Since

mountainous areas in the western part of the area studied.

there were a few more cloudy days in 2031 than in 2007, theMoreover, the discrepancy in humidity between the coastal area

solar radiation to the ground surface in 2031 was less than that in

and inland area further widened in the future (2031). This was

2007. Therefore, the air temperature in 2031 in the northwest

probably caused by the increase of evaporation from sea surfacepart of the center of Tokyo changed little compared with that inand the advection of humidity resulting from the sea breeze.

2007. According to the results of MIROC4h, the monthly

Because of the difficulty to set the detailed distribution of the

[℃]
1.4

[kg/kg’]
0.00140
0-0.4

Tokyo Sta.

(1) Air temperature(2) Absolute humidity

Fig. 5: Differences in monthly average meteorological factors at 12:00 (2031－2007)

[℃]
1.25

[℃]
30

0

-0.50

22

(1) 2031(2) Difference (2031－2007)
Fig. 6: Monthly average daily maximum WBGT

- 64 -

[persons/(1,000,000 populationmonth)][persons/(1,000,000 populationmonth)]

45

13

0

-9

0

29

13

0

13

-3

(1) 2031(2) Difference (2031－2007)
Fig. 7: Monthly incidence rate (IRmonth)[persons/(km2month)][persons/(km2month)]

4.5

0.5

0

-0.1

0

4.0

0.4

0
0

-0.1

(1) 2031(2) Difference (2031－2007)
Fig. 8: The number of heatstroke patients (Risk)- 65 -

river surface temperature in the Tokyo metropolitan area from

Although we estimated the outdoor heatstroke risk in the future,

the results of GCM, the river surface temperature for the WRF

the distributions of IRmonth and Risk qualitatively corresponded

simulations was given by assuming the same as the sea surface

with the study by Ohashi et al(6).

temperature near the river in this study. Therefore, increasing of

The results of this study show that the increased rate in Risk of

air temperature and humidity along the rivers appeared similar toeach prefecture in the Tokyo metropolitan area from 2007 tothat on the sea. Figure 6 shows the spatial distributions for (1)2031 was proportional to the length of the coastline (Chiba:

the monthly average daily maximum WBGT in 2031 and (2) the

23.8%, Kanagawa: 21.8%, Tokyo: 5.2%, Saitama: -1.6%)

difference between 2031 and 2007 (2031 － 2007) calculated
５．Conclusions

using the method outlined in Chapter 2. Since the weight
coefficient of the wet-bulb temperature is largest in the equation
for calculating WBGT (Eq. (1)), the WBGT increased by

Outdoor incidence rate and risk of heatstroke in the 2000s andapproximately 0.5 °C for the future at the area with the largest

2030s for the Tokyo metropolitan area were estimated using theincrease in humidity. Hence, the hazard for the heatstroke risk

results from mesoscale meteorological simulations with WRF.

will increase significantly in coastal areas including Tokyo and

The risk evaluation was based on the concept that disaster can

Yokohama, which are some of the most densely populated

occur when hazard, vulnerability, and exposure intersect. The

regions in Japan.

results of this study suggest that the hazard for heatstroke risk,
the daily maximum WBGT, will increase mainly along coastal

４ .

Estimationofoutdoorincidence

rate

and

areas in the future, owing to a large increase in humidity withheatstroke risk

global warming. The IRmonth at northern inland area was found to
be twice that of the coastal area, including the central part of

Applying the proposed method mentioned in Chapter 2 to theTokyo in 2030s. This means that the regional potential risk was

WBGT distributions obtained from the WRF simulations, thevery high in the inland area. However, the tendency for theoutdoor incidence rate per month (IRmonth) and the number ofincreased IRmonth was more significant along coastal areas in theheatstroke patients (Risk) in the Tokyo metropolitan area can be

future (2030s). Finally, the Risk increased by more than 20 at

estimated. Figure 7 illustrates (1) the IRmonth in 2031, and (2) the

coastal regions in the 2030s.

difference in IRmonth between 2031 and 2007 (2031－2007). The
Acknowledgment

IRmonth at the northern inland area was approximately twice ashigh as that of the coastal area. However, the tendency of theincreased IRmonth was significant at the waterfront because of the

The authors are deeply grateful to Professor M. Kimoto at thehigh-WBGT change from 2007 to 2031 caused by the increase

Atmosphere and Ocean Research Institute, the University ofin humidity. This indicated that risk would become obvious inTokyo, who provided MIROC4h data. The authors would like tothe future at coastal areas where the regional potential risk is not

thank Tokyo Fire Department for providing emergency transport

as high under the current climate conditions.

data. The authors also thank to the members of the working

Considering the population density as the exposure index, Risk

group on future standard weather prediction using global climate

can be estimated. Figure 8 illustrates (1) the Risk in 2031 and (2)model (project general manager: Ryozo Ooka) for discussion

difference in Risk between 2031 and 2007 (2031－2007). The

about future standard climate data. This study was supported bytotal predicted number of heatstroke patients in Tokyo was

the JSPS Grant-in-Aid for Scientific Research (B) (Grant

approximately 370 people in August 2031. Figure 8 (2) shows

Number 26289200, Project General Manager: Akashi Mochida),that the expansion of the Risk was concentrated at the coastal

and by the joint research project of the Wind Engineering Joint

areas, such as the central part of Tokyo. This resulted from theUsage/Research Center, Tokyo Polytechnic University.

synergistic effects of an increase in humidity with global
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