京 都 大 学 防 災 研 究 所 年 報 第48号 C 平成17年3月
Annuals of Disas. Prev. Res. Inst., Kyoto Univ., No. 48C, 2005

Estimation for Effects of Existence of Urban on Development of Cumulonimbus Clouds
Using Atmosphere-Land Coupled Model of CReSiBUC
Qoosaku MOTEKI*, Yotaro ITO, Kazuaki YOROZU, Kazuyoshi SOUMA,
Atsushi SAKAKIBARA**, Kazuhisa TSUBOKI***, Teruyuki KATO****,
Kenji TANAKA, and Shuichi IKEBUCHI*

COE Researcher, DPRI, Kyoto University**
ChudenCTI Co, Ltd***
Hydrospheric Atmospheric Research Center, Nagoya University****
Meteorological Research Institute, Japan Meteorological Agency
Synopsis
In this study, a model of CReSiBUC coupled with a cloud resolving model CReSS and aland surface processes model SiBUC was developed, and effects of existence of urban ondevelopment of cumulonimbus clouds were investigated. From a simulation for a super cell
thunderstorm on 24 September 1999, rainfall amounts simulated with the CReSiBUC wereconfirmed to be significantly different from that with the normal CReSS. From a simulation for
Nerima heavy rainfall on 21 July 1999, it was found that changes of distribution of urban andanthropogenic heat amount greatly affected the positions and amounts of rainfall.
Keywords: land atmosphere interaction, heat island, heavy rainfall
1.

Introduction

Recently, in Japan, the approach on operational
forecast of local weather phenomena of the horizontal
scale less than 100 km has been activated. Mesoscale
Spectral Model (called MSM: 10 km-resolutionhydrostatic model) by Japan Meteorological Agency was
begun from 2002, and such kinds of activities by private
enterprise were also activated.
Meanwhile, other than the problem of a horizontal
resolution of forecast models, the land surface processes
are important for improvement of forecast accuracy for
local weather phenomena. Destabilization and the
low-level convergence with a local circulation due to thesurface heating are significant triggers for generation of
cumulonimbus clouds. Therefore, forecast accuracy forthe part of the land surface processes relating to thesurface heating is an important subject on forecast of
precipitation with cumulonimbus clouds.
This study especially focuses on the cumulonimbus

clouds that are generated over a plain field with big cities.
Over such kind of regions, the “heat island” is considered
to affect generation and development of the
cumulonimbus clouds. Kobayashi (2003) investigatedgeneration and development processes of cumulonimbus
clouds in an urban area by using weather radar data.
They pointed out that the processes of the cumulonimbus
clouds in the urban area were different from those inmountainous regions. Fujibe (2003) suggested that the“heat island” affected generation frequency of strong
rainfall events in the Tokyo metropolitan area. Thesesuggestions with observational studies were encouraged
by some numerical studies (e.g. Ohashi and Kida, 2002;
Kusaka and Kimura, 2004; Rozoff et al. 2003). However,the urban effects on rainfall events have never been
quantitatively discussed because of calculation accuracyof the land surface processes.
For estimation of the land effects on rainfall events,
the present study develops an atmosphere-land coupledmodel of CReSiBUC. In the CReSiBUC, CReSS

sfcphy4cresibuc|-- soildrv||-- radiat||-- forcesfc||-- sibucdrv|-- radiat4cresibuc|-- passvar2sibuc|-- gim4cress|-- retvar2cressFig. 1 Structure of subroutines relating to the land
surface processes calculations in the CReSiBUC.
(Cloud Resolving Storm Simulator: Tsuboki andSakaibara, 2001) is adopted for atmosphere and SiBUC(Simple Biosphere model including Urban Canopy:
Tanaka, 2004) is adopted for land surface processes. An
objective of this study is to estimate the urban effects ondevelopment of cumulonimbus clouds. For the objective,
two cases of local rainfall events associated withcumulonimbus clouds are selected: the 24 September
1999 super cell thunderstorm at Toyohashi city in Aichprefecture and the 21 July 1999 Nerima heavy rainfall inTokyo.
2.

Model description

simulations for the broader domain with the higher
resolution are possible.
2.2 Land surface processes model of SiBUC
The SiBUC is a land surface processes model
developed in Water Resources Research Center,
Disaster Prevention Research Institute, Kyoto
University (Tanaka, 2004). The SiBUC calculates
meteorological variables at the surface considering
detailed processes. In the normal CReSS, the land
surface processes are calculated with empirical constantvalues of albedo, evaporation efficiency, and roughness
for simplifying calculations. To the contrary, the
SiBUC calculates budgets of radiation, heat, water, andmomentum while changing parameters for the surface
condition. For example, for an urban area in the SiBUC,
irregularity associated with buildings is considered on
the basis of the urban canyon concept.
Additionally, the SiBUC adopts a “mosaic”
approach. In case that a horizontal grid of the CReSS is
including a number of various land use categories, values
on each category are calculated and a value averaged forthese is returned to the CReSS. This mosaic scheme is aneffective facility especially for the domain wheremultiple artificial landuse categories of urban, paddy, etc.
are mixed as like Japan.

2.1 Cloud resolving atmosphere model of CReSS
2.3 Development of CReSiBUC
The CReSS is a non-hydrostatic meteorologicalmodel developed in Hydrospheric AtmosphericResearch Center, Nagoya University (Tsuboki andSakaibara, 2001). Its source code written by the
FORTRAN 90 is open to the public in the following
web site.
http://www.tokyo.rist.or.jp/CReSS_Fujin/
At the present, the latest version is 2.2. All versions of
the CReSS have source codes for a single CPU andmultiple CPUs parallel computing. The CReSS has two
significant
advantages
comparingtoothermeteorological models of MRI/NPD-NHM (Saito et al.
2001), MM5 (Dudhia et al., 1993), ARPS (Xue et al.,1995), etc. One is a high readability of the source code
so that the CReSS is easy to couple with models forother processes. The other is a high calculation
efficiency that satisfies a condition to use the “Earth
Simulator,” which is one of the fastest super computersin the world. Using the CReSS, atmospheric

The CReSiBUC developed in this study is a
coupled model of the CReSS and the SiBUC describedin sections 2.1 and 2.2, respectively. The CReSiBUC
can be used for estimating effects of urban ondevelopment of cumulonimbus clouds. The source codeof the CReSS version 2.0 for a single CPU computing isused in the CReSiBUC.
Structure of subroutines relating to calculations of
the land surface processes in the CReSiBUC and theirfeatures are shown in Fig. 1 and Table 1, respectively. In
the normal CReSS, the land surface processes arecalculated in several subroutines in the “sfcphy.” On thisdevelopment of the CReSiBUC, the “sfcphy4cresibuc”
is made so that both “soildrv” and “sibucdrv” can becalled. The “soildrv” is a subroutine for calculations of
the land surface processes used in the normal CReSS.
The “sibucdrv” is a subroutine including subroutines of
SiBUC. Using “NAMELIST” of a standard facility ofTable 1 List of subroutines relating to the land surface processes calculations in CReSiBUC.
Subroutine

Remarkssfcphy4cresibuc Switching the normal scheme for surface physics in CReSS (soildrv) and SiBUC (sibucdrv).
soildrv
Controlling subroutines of the normal scheme of CReSS.
radiatCalculating short and long radiations.
forcesfc
Calculating surface fluxes with a simple method.
sibucdrv
Controlling subroutines including SiBUC (gim4cress).
radiat4cresibuc The same as “radiat,” but some output parameters are changed for inputting SiBUC.
passvar2sibuc Changing array format of surface meteorological parameters for inputting SiBUC.
gim4cress
Calculating surface fluxes with SiBUC.
retvar2cress Changing array format of surface meteorological parameters output from SiBUC.

Fig. 2 Accumulated rainfall distributions simulated with (a) the normal CReSS, (b) the CReSiBUC, and (c) difference
from the CReSiBUC to the CReSS.

the FORTRAN, switching of the “soildrv” and the“sibucdrv” (i.e. the normal CReSS and the CReSiBUC)
can be executed without a re-compiling.
When the “sibucdrv” is called in the“sfcphy4cresibuc,” the surface fluxes is calculated by the“gim4cress” of a main routine of the SiBUC. As
atmospheric boundary conditions for the land surface
processes, temperature, vapor pressure, wind speed at
the first layer of the atmosphere, and short and longwave radiations, and rainfall intensity are input from thepart of the CReSS to the part of the SiBUC. The“gim4cress” calculates fluxes of sensible heat, latent heat,and momentum from the land surface to atmosphere.
The sensible heat flux affects a source term in the timedependent equation of potential temperature at the firstlayer of the atmosphere. The latent heat flux affects asource term in that of vapor at the first layer of theatmosphere. The momentum flux affects a diffusion
term in that of horizontal wind at the first layer of theatmosphere.
3.

Importance of considering detailed processes ofurban canopy

To confirm simulated rainfall difference between
the normal CReSS and the CReSiBUC, simulations
with the two models for a case of a super cell
thunderstorm on 24 September 1999 at Toyohashi city
in Aich prefecture were conducted. Tsuboki and
Sakakibara (2001) have already shown that the case iswell reproduced with a simple initial condition. In thissimulation, a landuse of urban was uniformly assumed
as the land surface in a calculation domain forverification of effects that detailed processes of urban isconsidered.
3.1 Simulation design
In the simulations with the CReSS and the
CReSiBUC, precipitation physics of mixed-phaseprocesses, predicting the mixing ratios of rainwater,
cloud water, cloud ice, snow, and graupel, are used. The
horizontal grid size is 0.5 km, and the vertical gridcontains 30 levels with variable grid intervals (⊿z =100 m near the surface and ⊿z = 500 m at the top
level). The horizontal domain has 145 × 300 grid
points, and big and small time steps of ⊿t = 3
seconds (for all terms except for those relating toacoustic wave) and ⊿t = 0.5 seconds (for the terms
relating to acoustic wave) are used.
The initial data was uniformly given for thehorizontal domain from an upper-air sounding at 09
JST on 24 September 1999 at Shionomisaki (located in
the southwest of Toyohashi city). For initiating
convective storm updraft, plus 4 K disturbance with asize of 20 km in diameter was given in the lowest layer
in the initial field. The integration time was 7200
seconds from 09 JST on 24 September 1999.
For the whole of the domain, a landuse of urban is
uniformly assumed as the land surface in both
simulations with the CReSS and the CReSiBUC. In the
CReSS simulation, empirical constant values of albedo,
evaporation efficiency, and roughness for urban are set
to 0.1, 0.1, and 0.8, respectively.

This result shows the importance of the fact that thedetailed land surface processes of urban is consideredin short-time simulations of strong rainfall events.
4.

Impact of urban effects on a heavy rainfall event

To study effects of urban on rainfall, sensitivity tests
for distribution of urban and urban anthropogenic heat
are conducted by the CReSiBUC. A case of the 21 July
1999 Nerima heavy rainfall is selected for the sensitivity
tests. The Nerima heavy rainfall was occurred from 15
JST to 18 JST and the maximum hourly rainfall amountwas 131 mm for 15-16 JST. This event was occurred ina local area of Nerima-ku in Tokyo where has a highurban centralization. The Nerima heavy rainfall is asuitable case for studying urban effects on rainfall.
4.1 Simulation design
Figure 3 shows the domains of simulations with
the RSM and the CReSiBUC. In the simulations with
the CReSiBUC, precipitation physics of mixed-phaseprocesses are used. The horizontal grid size is 5 km,
and the vertical grid contains 45 levels with variablegrid intervals (⊿z = 100 m near the surface and ⊿z =500 m at the top level). The horizontal domain has 100
× 100 grid points, and big and small time steps of ⊿t= 10 seconds and ⊿t = 5 seconds are used.
The initial and lateral boundary data were
provided from output produced by the Regional

3.2 Simulation results
Simulated total rainfall for 2 hours and total
rainfall difference between the CReSS and the
CReSiBUC simulations are shown in Fig. 2. As in the
CReSS simulation, the main features of super cell
thunderstorm are well reproduced in the CReSiBUC
simulation: the horizontal scale and the shape of arainfall area are not so different between the twosimulated results (Fig. 2a and b).
However, focusing on the center of the super cell(x = 10-30 km, and y = 40-70 km), the maximum valueof the total rainfall simulated with the CReSiBUC isabout 8 mm larger than that with the normal CReSS(Fig. 2c). Considering the fact that the total rainfall forthe part of the center of the super cell is about 25 mm,
the difference of 8 mm is a sufficiently large amount.

Fig. 3 Model domains of a 20 km-resolution hydrostatic
model (marked as JMA-RSM), and a 5 km-resolution
non-hydrostatic model (marked as CReSiBUC).

Fig. 4 Landuse/landcover distribution given to the CTL simulation (only for an analysis domain). Rate of (a) urban, (b)paddy, and (c) farmland, and accumulated rate of natural landcover (water body, grassland, bare soil, and forest) for
each 5 km-grid of the CReSiBUC are shaded. Dashed thick and thin lines indicate terrain heights of 500 m and 1500m, respectively. The “urban area” where the rate of urban is over 30 % is surrounded by the solid line.

Fig. 5 The same as Fig. 4, but given to the PDY simulation.

Fig. 6 The same as Fig. 4, but given to the UBN simulation.

Spectral Model (RSM: a hydrostatic model used
operationally in Japan Meteorological Agency, referredin Segami et al., 1987). The horizontal resolution of the
RSM is about 20 km and the horizontal domain has
129 × 129 grid points. The CReSiBUC simulation
was one-way nested within the RSM started at 09 JST
on 21 July 1999. The integration time of the
CReSiBUC simulation was 12 hours from 09 JST on
21 July 1999, and lateral boundary data was given
every 1 hour.
To study effects of existence of urban on thegeneration of the Nerima heavy rainfall, simulations
called CTL, PDY, and UBN (abbreviations of control,
paddy, and urban, respectively) are conducted using thefollowing three patterns of the landuse/landcoverFig. 7 (a) Averaged surface temperature for 12-14 JSTwith the AMeDAS, and (b) accumulated rainfall for
15-18 JST with the Radar-AMeDAS on 21 July 1999.
Dashed thick and thin lines indicate terrain heights of
500 m and 1500 m, respectively. The “urban area”
where the rate of urban is over 30 % is surrounded by
the solid line.

30 % is expediently defined as “urban area”(surrounded by the solid line in all figures after Fig. 4).
In the distribution data for the UBN, alllanduse/landcover categories except for water body are
replaced to urban only in the “urban area.”
In addition, to consider effects of anthropogenic
heat in urban, a simulation that the anthropogenic heatof 400 W/m-2 is given (hereafter called AH400) is
conducted. The anthropogenic heat is an artificial
heating the lower atmosphere due to coolingequipments, cars, etc. According to distribution data ofurban anthropogenic heat in Tokyo (Ichinose et al.,1994), a heat amount of about 300-400 W/m-2 isdischarged in Tokyo during a summer season. Although
the realistic anthropogenic heat amount is changed intime, the constant amount of 400 W/m-2 is assumed in
the AH400.
4.2 Characteristics of landuse/landcover distributionof model domain

Fig. 8 Simulated results with the CTL of (a)accumulated rainfall for 15-18 JST, (b) averaged
temperature at 0.1 km for 12-14 JST, and (c) averaged
convergence accumulated in the 0.1-1 km-layer for
16-18 JST. Dashed thick and thin lines indicate terrain
heights of 500 m and 1500 m, respectively. The “urbanarea” where the rate of urban is over 30 % issurrounded by solid line. Vectors of winds at a heightof 0.1 km are shown in all panels.
distributions. One is the actual distribution as shown in
Fig. 4. The second one is the distribution in whichurban is replaced to paddy as shown in Fig. 5. The third
one is the distribution in which urban near Tokyo is
increased as shown in Fig. 6. In this study, the area
where the rate of urban per 5 km square unit is over

The calculation domain with the CReSiBUC has asignificant
characteristicoflanduse/landcover
distribution, that is, the area of artificial landuse
categories of urban, paddy, and farmland are relatively
broad (Fig. 4a, b, and c). Generally, the natural landcoverof water body (river, lake, and sea), grassland, bare soil,
and forest is predominantly distributed over the Japanislands. In the calculation area, the area where the rate of
the natural landcover exceeds 60% is predominantly
distributed (Fig. 4d). However, around the Bay of Tokyo,
the area where the rate of the urban exceeds 30%(defined as the “urban area” in section 4.1) has the widthof about 100 km, and the area where the accumulated
rate of paddy and farmland is about 20-30 % is expandedin the northern and eastern sides of the “urban area.” The
Kanto district is a region that has a remarkable contrastof landuse/landcover distribution in the west-east
direction. Therefore, the surface heating difference
between the western and eastern areas could tend to besignificant, and local circulations could be easy todevelop.
Under this condition, the “urban area” where thesurface heating is quite large due to the “heat island”
effect is located in the center of the Kanto plain. It isconsidered that the low-level winds could be easy toconverge at the “urban area” where tends to be the local
area of high temperature and low pressure.

Fig. 9 15-18 JST accumulated rainfall simulated with
the PDY. Vectors of winds at a height of 0.1 km areshown.

Fig. 11 (a) 16-18 JST averaged convergence in the 0.1-1
km layer simulated with the PDY, and (b) convergence
difference between the PDY and the CTL. Vectors in (a)indicate winds, and in (b) indicate wind difference
between the PDY and the CTL at a height of 0.1 km.

Fig. 10 (a) 12-14 JST averaged temperature at a heightof 0.1 km simulated with the PDY, and (b) temperature
difference between the PDY and the CTL. Vectors in(a) indicate winds, and in (b) indicate wind difference
between the PDY and the CTL at a height of 0.1 km.

4.3 Observational characteristics over the Kanto
Plain on 21 July 1999

Averaged surface temperature and wind with the
AMeDAS (Automated Meteorological Data Acquisition
System) for 12-14 JST and accumulated rainfall with the
Radar-AMeDAS for 15-18 JST are shown in Fig. 7.
Before the generation of the Nerima heavy rainfall, theKanto district was clear sky, and the feature of the “heatisland” was clearly shown in the distribution of thesurface temperature. The temperature over 33 degree C
is observed in the “urban area,” and that less than 31
degree C is outside of the “urban area” (Fig. 7a). The
surface winds in and around the “urban area” tend toconverge in the center of the “urban area.”
The strong rainfall exceeding 45 mm associatedwith the Nerima heavy rainfall is locally induced in thearea of about 50 km square (Fig. 7b). The position of thestrong rainfall corresponds to that of the area of hightemperature and convergence of winds. From theseobservational evidences, it is suggested that the NerimaFig. 12 15-18 JST accumulated rainfall simulated with
the UBN. Vectors of winds at a height of 0.1 km areshown.

Fig. 14 (a) 16-18 JST averaged convergence in the
0.1-1 km layer simulated with the UBN, and (b)convergence difference between the UBN and theCTL. Vectors in (a) indicate winds, and in (b) indicate
wind difference between the UBN and the CTL at aheight of 0.1 km.

Fig. 13 (a) 12-14 JST averaged temperature at a heightof 0.1 km simulated with the UBN, and (b)temperature difference between the UBN and theCTL. Vectors in (a) indicate winds, and in (b) indicate
wind difference between the UBN and the CTL at aheight of 0.1 km.
heavy rainfall is strongly affected by the urban.
4.4 Effects of distribution of urban on rainfall
Comparing to the observation results in Fig. 7, theCTL simulation well reproduced the main features of
the Nerima heavy rainfall as in Fig. 8. A local strong
rainfall over 35 mm is located in the “urban area,”
although its position is slightly shifted in the southwest
from that of the actual the Nerima heavy rainfall. This
shift could result from accuracy of the initial and lateralboundary data from the RSM. A high temperature area(> 30 degree C) is seen in the distribution oftemperature at a height of 0.1 km as shown in the actual
observation. The position of convergence in the 0.1-1
km layer exceeding 10 × 10-4 1/s is wellcorresponded to the high temperature area.
Rainfall distribution with the PDY simulation isshown in Fig. 9. Comparing to the CTL, a strong
rainfall area is shifted further west. Figure 10 shows
fields of temperature and temperature difference
between the PDY and the CTL. In the PDY, a hightemperature area in the “urban area” is not seen, that is,
the “heat island” phenomenon is not occurred (Fig.
10a). Around the area where is the temperature
decrease exceeding 2 degree C, winds are changed tothe direction to the outside of the “urban area” (Fig.
10b). Figure 11 shows fields of convergence andconvergence difference. A remarkable convergenceFig. 15 15-18 JST accumulated rainfall simulated with
the AH400. Vectors of winds at a height of 0.1 km areshown.

Fig. 17 (a) 16-18 JST averaged convergence in the
0.1-1 km layer simulated with the AH400, and (b)convergence difference between the AH400 and theCTL. Vectors in (a) indicate winds, and in (b) indicate
wind difference between the AH400 and the CTL at aheight of 0.1 km.

Fig. 16 (a) 12-14 JST averaged temperature at a heightof 0.1 km simulated with the AH400, and (b)temperature difference between the AH400 and theCTL. Vectors in (a) indicate winds, and in (b) indicate
wind difference between the AH400 and the CTL at aheight of 0.1 km.
zone is not seen in the “urban area” (Fig. 11a).
Comparing to the CTL, the convergence in the “urbanarea” is decreased by 5 × 10-4 1/s, meanwhile theconvergence in a mountainous region in the west isfound to be increased.

Rainfall distribution with the UBN simulation isshown in Fig. 12. Comparing to the CTL, a strong
rainfall area is shifted further east. Figure 13 shows
fields of temperature and temperature difference
between the UBN and the CTL. In the UBN, a hightemperature area (> 30 degree C) in the “urban area” isextended, that is, the feature of the “heat island” in the
UBN is more significant than that in the CTL (Fig. 13a).
Around the area where is the temperature increase
exceeding 1 degree C, the winds are changed to thedirection toward the “urban area” (Fig. 13b). Figure 14
shows fields of convergence and convergence
difference. A remarkable convergence zone is seen in
the “urban area” as in the CTL (Fig. 14a). Comparing
to the CTL, the convergence in the “urban area” isincreased by over 5 × 10-4 1/s (Fig. 14b).
It can be explained that the position of thegeneration of precipitation clouds is shifted resulting
from the fact that the surface heating with the “heatisland” is changed due to with or without of urban, andconcentration of urban. From these results of the PDY
and the UBN, it is considered that the existence ofurban changes the low-level convergence through theeffects of the “heat island,” and significantly affects theposition of the generation of a heavy rainfall.

Acknowledgements

4.5 Effects of anthropogenic heat on rainfall

The authors are grateful to all members at Water
Resources Research Center for their support andconstructive comments on the improvement of the
CReSiBUC. We use the FUJITSU VPP5000 at
Information Technology Center, Nagoya University. We
are also supported for software by CTI Co., Ltd. The
authors express their appreciation to all of them.

Rainfall distribution with the AH400 simulation isshown in Fig. 15. Comparing to the CTL, a strong
rainfall area is extended in the west-east direction. An
increase of rainfall amount of 10 mm in the maximum
was seen in the “urban area.” Figure 16 shows fields oftemperature and temperature difference between the
AH400 and the CTL. In the AH400, a high temperaturearea (> 32 degree C) in the “urban area” is seen, and thefeature of the “heat island” in the AH400 is moresignificant than that in the UBN (Fig. 16a). Around thearea where is the temperature increase exceeding 2
degree C, winds are changed to the direction toward the“urban area” (Fig. 16b). Figure 17 shows fields ofconvergence and convergence difference. A remarkable
convergence zone is seen in the “urban area” as in the
CTL (Fig. 17a). Comparing to the UBN, the area of theincrease of 1 × 10-4 1/s from the CTL is extended(Fig. 17b).
This result cautions about the following scenario.
Buildings will become higher and higher, andanthropogenic heat per unit area associated withcooling equipments will increase more and more. It issuggested that growing of such kind of human-induced
effects affects intensity and frequency of heavy rainfall
disasters in urban areas.
5.

Conclusions

In this study, a model of CReSiBUC coupled with acloud resolving model CReSS and a land surface
processes model SiBUC was developed. In a simulation
for a super cell thunderstorm on 24 September 1999,
rainfall amounts simulated with the normal CReSS andthe CReSiBUC were significantly different. From thisresult, the fact that the detailed land surface processes is
considered is confirmed to be very important for rainfall
simulations with a mesoscale model. In a simulation for
Nerima heavy rainfall on 21 Jul. 1999, it was found thatchanges of distribution of urban affected the position of arainfall area in association with the “heat island” effect.
In addition, it was clarified that anthropogenic heatamount greatly affected the horizontal scale of a rainfall
area and the quantitative rainfall amount.

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大気・陸面結合モデルCReSiBUCを用いた積乱雲発達に対する都市の存在の影響評価
茂木耕作*・伊藤洋太郎・萬和明・相馬一義・榊原篤志**・坪木和久***・加藤輝之****・田中賢治・池淵周一*

京都大学防災研究所COE研究員**
中部電力・CTI***
名古屋大学地球水循環研究センター****
気象庁気象研究所
要旨
本研究では、雲解像モデルCReSSと陸面過程モデルSiBUCを結合したCReSiBUCを開発し、都市の存在が積乱雲発達に与える影響を調べた。1999年9月24日のスーパーセルの実験では、標準のCReSSとCReSiBUCでそれぞれ再現される降水量に顕著な差が生じることを示した。1999年7月21日の練馬豪雨の実験では、都市の分布と人口排熱量の変化が降水の位置や量に大きな影響を持つことが分かった。
キーワード: 大気・陸面相互作用、ヒートアイランド、豪雨C26

大気・陸面結合モデル CReSiBUC による積乱雲発達に対する都市の存在の影響評価○茂木耕作・伊藤洋太郎・萬和明・相馬一義・榊原篤志・坪木和久・田中賢治・池淵周一

１．はじめに
本研究では、名古屋大学で開発された雲解像
モデル CReSS と京都大学で開発された陸面過程モデルSiBUC を結合したCReSiBUC の開発を行った。この CReSiBUC を用いることによって、従来の数値モデルでは困難であった積乱雲の発生・発達における陸面過程の影響評価を行うことを本研究の目的とする。ここでは、陸面過程が発達に重要であると思われる積乱雲を
2 事例選出し、都市の存在が降水にもたらす影響に着目して解析を行った。
２．都市の陸面過程を詳細に扱うことによる効果1999 年 9 月 24 日の 11JST から 12JST にかけての愛知県豊橋市において、スーパーセル型の積乱雲が発達した。この対象事例を再現する
際に、簡便な陸面モデルが採用されている標準のCReSS とCReSiBUC でそれぞれ実験を行った。二つの実験結果を比較すると、CReSS に比べて CReSiBUC では、スーパーセルの中心部
において降水量が 1 時間積算値で最大 7 mm 増加していた(図示せず)。この結果は、短時間降
雨予測においても、都市での陸面過程を詳細に考慮することによる効果が小さくないことを示している。
３．都市の存在が積乱雲発達に与える影響
1999 年 7 月 21 日の東京都練馬区において練馬豪雨と呼ばれる豪雨災害があり、一時間最大降水量で 131 mm を記録した。この練馬豪雨を
水平解像度 5 km の CReSiBUC で再現する際に、実際の土地利用分布データを与えた実験(CTL)、都市を水田に置き換えた実験(PDY)、
都市をより過密にした実験(UBN)、人工排熱量
400W/m-2 を与えた実験(AHD、他では 0W/m-2としている)を行った。CTL(図 1a)では、レーダ
ーアメダス観測雨量(図示せず)と比較して、概ね練馬豪雨の特徴を再現した。
練馬豪雨がもたらされた 1999 年 7 月 21 日
15JST から 18JST までの 3 時間積算降雨量分
布を図１に示す。CTL に比べて PDY およびUBN の結果(図 1b と c)では、降水域の位置が西側、東側にそれぞれずれている。また、AHD(図 1d)では、CTL と比べて、降水域の水平規模が拡大し、定量的にも 10 mm 程度の雨量の増加が見られた。
４．結論
本研究では、雲解像モデル CReSS と陸面過程モデルSiBUC を結合したCReSiBUC の開発を行った。1999 年 9 月 24 日のスーパーセルの理想実験では、地表面を一様な草地、都市と仮定した場合、CReSS と CReSiBUC で再現される降水量に顕著な差が生じることを示した。この結果から都市の陸面過程を考慮することは、
短時間の降水予測においても高い重要性を持つ
ことが示唆された。1999 年 7 月 21 日の練馬豪
雨の再現実験では、都市の分布の変化が降水域の形成位置に対して影響を及ぼすことが明らか
にされた。
さらに、
都市における人工排熱量が、降水域の水平・定量規模を大きくする影響を持
つことが分かった。

図１：CReSiBUC で計算された (a)CTL、(b)PDY、(c)UBN、(d)AHD による３時間積算
降水量分布。赤線で囲まれた領域は、土地利用分布データにおいて感度実験の際にデータ操作を行った都市領域を示す。