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

Academic Article

Making Climatic Zoning Maps in Yokohama-Comparison among different resolution calculationsMakoto Yokoyama*1

Takahiro Tanaka*1

Toru Sugiyama*2

Satoru Sadohara*3*1 Graduate School of Engineering, Hiroshima University*

2 Center for Earth Information Science and Technology, Japan Agency for Marine-Earth Science Technology*3 Graduate School of Urban Innovation, Yokohama National University
Corresponding author: Makoto Yokoyama, yokomako.3918@gmail.com
ABSTRACT
Although countermeasures against urban warming and its effects have been studied in recent years, thesecountermeasures must be introduced into a suitable place for effective urban environmental planning. Therefore,
classification of urban areas in zoning maps must be done in terms of climatic environments to find necessary andeffective countermeasures for respective zones.
Numerical calculations using a meso-scale meteorological model are often used to produce such maps. This
study calculated the climatic environment in Yokohama at 500 m resolution and at 100 m resolution, respectively,
using a WRF (Weather Research and Forecasting) model and an MSSG (Multi-Scale Simulator for the
Geoenvironment) model. These calculation results were then compared with observations. Calculation accuracy ofrelative magnitude correlation of these calculations were compared to analyze whether high-resolution calculation
is effective or not for making climatic zoning maps. Furthermore, climatic zoning maps were produced from thesecalculation results and cluster analyses. Results show that urban areas in Yokohama are classifiable into five zones
in terms of patterns of air temperature change. Additionally, more detailed effects can be incorporated into climaticzoning maps using high-resolution calculations with an MSSG model.
Key Words : Climatic zoning map, Air temperature, Numerical calculation, Cluster analysis, Sea breeze

such maps necessitates classification of target areas into some

1. Introduction

zones in terms of urban climate. Using results of numerical
In recent years, the thermal environment in urbanized areas iscalculation is apparently effective for such classification.

becoming increasingly inhospitable for residents because of

Meso-scale meteorological models are often used to elucidate

urban heat island effect and global warming. These phenomena

spatial characteristics of urban climatic environments (2)(3).

areSuch models are regarded as useful to produce climatic zoning

collectively

regarded

as

urban

warming.

Manycountermeasures against urban warming such as improving windmaps.

ventilation and increasing the green ratio in urban areas and its

As previous research related to making climatic zoning map

effects have been studied. However, these countermeasures

based on result of meso-scale meteorological model, Kitao etshould be introduced into suitable places for effective urbanal.(4) calculated climatic environment of Osaka region with 1km

environmental planning.

resolution by using Weather Research and Forecalting (WRF)(1) aremodel and made regional climate atlas in Osaka region forproposed as effective tools to inform urban planningsuggesting the pro-environmental urban plannning. Oba et al.(5)stakeholders such as urban planners, architects, and residents

analysed the spatial distribution of heat budget inside Sendai andabout the urban environment. As one layer of urban

made “heat balance map” with 500m resolution. In addition,

environmental climate maps, zoning maps classifying urban

climatic zoning to select appropriate countermeasures against

areas in terms of urban climate are necessary for presenting andheat island effects based on this map was proposed. Matsuo andconsidering effective countermeasures for each zone. Making

Tanaka(6) analysed the effect of sea breeze on summer diurnal

From this background, urban environmental climate maps

- 107 -

temperature in Hiroshima plain for mapping the sea breeze

target area. Yokohama, a hilly city, has many small valleys

effect for mitigating urban warming. In this study, definite

called “yato” throughout the whole city area. Urban areas extend

zoning is not defined but spatial distribution of sea breeze effectthroughout the whole city area; urban heat island effect has been

is calculated with 1km resolution. As shown in these studies, thereported there (7). However, Yokohama faces Tokyo Bay and isspatial resolution of climatic zoning based on numericallocated near Sagami Bay. Figure 2 shows wind roses ofcalculation is rough because of constraints of computation time,

AMeDAS Tsujido and the Yokohama Local Meteorologicaland other restrictions.

Observatory (YLMO). Locations of these observation points are

Generally, climate zoning with reflecting detailed effects such aspresented in Figure 1. As shown in Figure 2, a south wind blows

topography and land use. But, these effect is also important forat both points. An east wind blows at YLMO during the daytime.

making zoning maps in some citis having complicated

Therefore, it is considered that the sea breeze effect mitigates

topography.

urban warming in this area.

However, high-resolution calculations for wider areas can be

In addition, fine weather days are classified into three groups (i.e.

executed using supercomputers in recent years. A Multi-Scale

day without southwest wind, day with southwest wind andSimulator for the Geoenvironment (MSSG) model can be
developed for multi-scale calculation in the Earth Simulator.

Ü0This model can execute such high-resolution calculations for awider area. Therefore, more detailed climatic zoning maps can

20

40

60

80

μ

100 kmbe anticipated using this model.
For this study, calculations of two kinds were executed
respectively using WRF model and MSSG model. TheseÜμcalculation results are compared with observation results. Thencalculation accuracy of relative magnitude correlation of these
Elevation [m]calculations were compared to analyze whether high-resolution

3705

calculation is effective or not for making climatic zoning maps.

Yokohama local
meteorologicalobservatory

0

Additionally, climatic zoning maps are produced from theseYokohama
city area

calculation results. Urban areas in Yokohama are classified into
five zones in terms of air temperature change patterns using

Tokyo Bay

AMeDAS Tsujido

cluster analysis.

Elevation- 240 m2. Research outline

-0m
0

2.1

1

2

3

4

5 km

Sagami Bay

Target area

For this study, Yokohama was selected as the target area. Figure

Fig. 1 Yokohama location and topography. This area is located1 portrays locations and topographical characteristics of thenear Sagami Bay, facing Tokyo Bay.

August 7, 5:00

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August 7 5:00

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- 32.032.0 - 33.0
33.0 - 34.0
34.0 - 35.0
35.0 - 36.0
36.0 - 37.0
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Fig. 3 Hourly averaged air temperature distribution of August 7 (left) 5:00 JST, (right) 12:00 JST. Air temperatures in Yokohama
are higher around the eastern coastal area at 5:00 JST and higher around the northwestern part at 12:00.

- 108 -

day with both east and southwest wind) by sea breeze blowing

Yokohama local
meteorological observatory

AMeDAS Tsujido

patterns among this area(8). Especially, number of days with botheast and southwest wind is largest in these three patterns in

30%summer of 2015. Therefore, August 7 in 2015 is selected as a

NWtarget day in this study. This day is a typical sunny day and the

N
NW

NE

20%
10%

end of continuous fine weather days period. East wind blows inW

the morning and southwest wind blows in the afternoon

E

0%

W

25%
20%
15%
10%
5%
0%

N
NE

E

according to observatonal data at YLMO. Analysis on other days
and other sea breeze blowing patterns are next steps.

SW

Area: 437.4 [km2]Population: 3,728,124 [persons] (2015)

SW

SE

Basic information related to Yokohama is given as shown below.

SE

SE
SE

Daytime（6:00～18：00）

Nighttime（19:00～5：00）

Fig. 2 Wind roses of AMeDAS Tsujido and YLMO. These
2.2

figures were made using data of summer, 2011–2015.

Meteorological observations

For this study, 59 air temperature sensors were set in and aroundYokohama for the observation period of July 24 through August

Case B

Case A31 in 2015. Observations were recorded at 10 min intervals. All

Domain1 (4.5 km resolution)

sensors were set in Stevenson screens located at elementary
schools throughout Yokohama. Figure 3 shows the air

Domain1 (100 m resolution)

Domain2(1.5 km resolution)

temperature distribution at 5:00 JST and 12:00 JST on August 7.
In the daytime, the southern part of the city is cooler; thenorthern part is hotter. Furthermore, the maximum airtemperature in that time reaches to over 37 °C. Therefore, itDomain3(500 m resolution)

seems that the air temperature distribution during the daytime is0

affected by the air temperature reduction effect of the sea breeze,

50

100

150

200

250 kmespecially from Sagami Bay in this area. In the nighttime, theFig. 4 Calculation areas of Case A and Case B. Numbers ofeastern part is hotter; the inland area is cooler. This distribution

domains and whole covered areas are different among two

pattern is apparently affected by the seawater temperature, landcalculations.

cover, and anthropogenic heat from factory areas in the coastal
area of Tokyo Bay.
2.3

Ü
μ

Calculation conditions

Case A (500 m resolution)ÜμCase B (100 m resolution)

In this study, two resolution cases were calculated. Case A wascalculated using a Weather Research & Forecasting (WRF)model with 500 m resolution. The WRF model is a popularmeso-scale meteorological model used worldwide (9); WRF ver.
3.7.1 was used for this study. Case B was calculated using an
MSSG model with 100 m resolution. An MSSG model is0

multiscale atmospheric-ocean coupled model developed by theLand use category (USGS)
Urban & Built-Up Water Bodies
Mixed Forest
GrasslandCenter for Earth Information Science and Technology, Japan

2

4

6 km

Dryland cropland
Irrigated cropland0Land use category (MSSG)
Building
Road
Building lot
Grassland

2

4

6 km

ForestWater bodies

Agency for Marine–Earth Science and Technology (JAMSTEC).

Fig. 5 Land use map of Case A and Case B. Land uses in both

An MSSG model can execute global, meso, and urban scalecases are classified into six categories.

numerical calculations(10). For this study, we used meso-scale

components of this model.

A. The input dataset is mainly land use, elevation, and the green

The calculation period of both cases was from 0:00 JST on

fraction (only in the WRF model). Figure 5 presents the land use

August 4 to 0:00 JST August 8. Numbers of domains differ

maps of the two cases. Land use categories are classified based

between the two calculations. Both calculation areas are shown

on USGS classification in Case A, and are classified into six

in Figure 4. Case A has three domains with one-way nesting andcategories in Case B, as shown in Figure 5. As an original landhorizontal resolutions of 4500 m, 1500 m and 500 m. Case B has

use dataset, National Land Numerical Information is used inonly one domain: the whole calculation area is smaller than Case

both cases. Furthermore, results of Basic Survey of City

- 109 -

Case-A (500m resolution)ÜμCase-A (500m resolution)ÜμAir temperature[°C] (2m height)
August 7 5:00
Air temperature[°C] (2m height)
August 7 12:00

0

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5

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10 km

0Case-B (100m resolution)

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10 km

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Case-B (100m resolution)ÜμAir temperature[°C] (2m height)
August 7 5:00

Air temperature[°C] (2m height)
August 7 12:00

0

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Fig. 6 Horizontal air temperature distribution at 12:00 JST,

Fig. 7 Horizontal air temperature distribution at 05:00 JST,

August 7 (2 m above ground). The distribution tendency is

August 7 (2 m above ground). Absolute values are different

similar to observation results. However, the shapes of high air

between Case A and Case B. In Case B, air temperature is lower

temperatures differ between the two cases. This difference seems

in inland area and distribution patterns are defined along

to result from differences of the wind direction of sea breezes

topographical charateristics.

from Sagami Bay.
air temperatures are highest around the northwestern part ofPlanning in Yokohama are used to identify building meshes in

Yokohama, reaching over 36°C. These distribution patterns are(11) ismainly the same as those of observation results presented in theCase B. In addition, a single-layer urban canopy model

introduced for Case A and Case B. Therefore, building effects

previous section.

such as anthropogenic heat and reduction of wind speed in the

From comparison among two cases, air temperature was mostly

urban area are considered. In Case A, anthropogenic heat ishigher in Case B than in Case A. Furthermore, the shapes of highemitted equally from urban meshes. However, anthropogenic

air temperature areas differed among these cases. In Case A, airheat is defined for every urban meshes including from housing,temperatures gradually became higher not only from Sagami androads, and factories in Yokohama in Case B. Therefore, we

but also from Tokyo Bay. However, in Case B, part of the highshould take care when analyzing results outside of Yokohama in

air temperature area extends near the coastline of Tokyo Bay.

Case B.

The cooler area along Tokyo bay is smaller than that in Case A.
This difference seems to derive from differences of sea breeze

3. Results and discussion

effects from Tokyo Bay between the two calculations. Actually,

3.1 Air temperature distribution

the wind direction from Tokyo Bay in Case B is not east, but

Here, horizontal air temperature distributions from two

almost south, which seems to result from the single domain

calculation

their

setting in Case B. This case cannot reflect the local wind

distributions. Figure 6 shows horizontal air temperatureenvironment adequately. Consequently, increasing the number of

distributions at 12:00 JST on August 7 (2 m above the ground).

domains and improving calculations using MSSG model is a

In both cases, the air temperature is lower in the coastal area,

subject for future work.

becoming gradually higher toward inland areas. Additionally,

Figure 7 shows horizontal air temperature distributions at 5:00

resultsarepresented.

Wecompared- 110 -

8/7 15:00 JST

36
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1.106529

1.78630.823828

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Case B (100 m resolution)
8/7 15:00 JST

36
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0.367929

21.436

0.068638
Air temperature (Observation) [°C]37

Case A (500 m resolution)
Air temperature (Observation) [°C]

Air temperature (Observation) [°C]38

Air temperature (Case A) [°C]

8/7 15:00 JST

36
35
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0.975529

0.924

0.395828

28
28 29 30 31 32 33 34 35 36 37 38

37Case B focal (5×5) (100 m resolution)

28 29 30 31 32 33 34 35 36 37 38
Air temperature (Case B) [°C]28 29 30 31 32 33 34 35 36 37 38
Air temperature (Case B focal (5×5)) [°C]Fig. 9 Scatter plots of air temperatures between calculation and observation results at 15:00 JST on August 7. Plot of Case B focal(5×5) is distributed along the dotted line without some observation points.

differences in urbanised areas are about 1.5°C-2.0°C and smaller
than that of daytime in both cases. However, absolute values aredifferent between Case A and Case B. Air temparature of Case A
is about 5°C higher than that of Case B. Case A overestimate
about 2°C and Case B underestimate about 2°C compared toobservation result (figure 3). Calculation accuracy of absolute

Correlation coefficient

JST on August 7 (2 m above the ground). Air temperature

values are need to be improved in the next challenge. With

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1.0
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are lower in Case A; higher in Cases B. In addition, air

Hour [h], JST

Case A
Case B focal (4×4)

temperatures in inland area are lower and air temperature
differences are shown along valley lines. This result seems that

Case B
Case B focal (5×5)

Case B focal (3×3)

high resolution calculation can consider topographical effectFig. 8 Hourly correlation coefficients of cases of August 7.

more clearly. As a result, effect of sky radiation cooling on air

Correlation coefficients are higher during daytime and lower

temperature distribution in inland area is more remarkable in thisduring nighttime. During nighttime, correlation coefficients ofcase.

high-resolution cases are higher than those of Case A.

3.2

focal (4×4) and Case B focal (5×5) respectively use values

Comparison with observation results

Here, calculation results are compared with observation results.

averaged by the surrounding 16 and 25 meshes. As shown figure

In the next section, we classify urban meshes using cluster

7, correlation coefficients are higher during daytime thananalysis. Cluster analysis classify all samples into some groups

nighttime in all cases. In addition, correlation coefficients are

based on relative magnitude correlation. So, important point oflower in Case B than in Case A during daytime. Air temperatures

calculation accuracy is to what extent these calculations canare sparsely distributed because of detailed temperature

express relative magnitude correlation among each observation

reduction effects by sea breezes and land use effects in Case B.

points. Therefore, we mainly calculated correlation coefficients

In three smoothed cases, correlation coefficients are generallyof respective cases with observation results to analyze suchhigher than in Case B during daytime. However, an increase of

relative magnitude correlation in each calculation. For thiscorrelation coefficients is independent of the number of meshes

comparison, only 45 observation points located in Yokohama

used for smoothing. Correlation coefficients are smaller during

and calculation results of 45 meshes located at respective

nighttime according in figure 7. Especially, correlation

observation points were used.

coefficients are negative in Case A from midnight to morning.

Figure 7 presents hourly correlation coefficients for August 7. In

This is because that air temperature differences in nighttime arethis figure, three additional results were obtained using

smaller in calculation results as shown figure 7. Besides,

smoothed values of Case B are also shown. Here, Case B focal

beginning of blowing sea breeze in the morning is earlier in(3×3) represents the averaged values by surrounding nine

calculation result of Case A. So, air temperature distribution

meshes using results of Case B are used. In addition, Case B

tendency become converse between observation and calculation- 111 -

results in Case A in the morning. However, correlation

first cluster and other cluster or sample is calculated again andcoefficients are higher in Case B than in Case A at that time.

number of clusters is reduced in order. Finally, tree diagram

During nighttime, effects of land use and topography on air

showing clusters classified based on resemblance of all samples

temperatures are generally more remarkable than during daytime.

are provided. In this study, squared Euclidean distance is used as

Therefore, Case B seems to reflect nighttime air temperatures

measuring scale of resemblance between each sample. As

more correctly than Case A.

constructing method of cluster, Ward method constructingThis result shows that using high-resolution calculation using

clusters based on dispersion in or among groups is used.

MSSG result model is apparently improve calculation accuracy

Samples are urban meshes (i.e. Urban & Built-up in Case A andof relative magnitude correlation and effective to calculate the

Building, building lot and Road in Case B) located in Yokohama.

urban climatic environment during nighttime and to produce

Actually, 1,657 meshes are used for Case A; 46,615 meshes are

climatic zoning maps that include nighttime effects.

used for Case B focal (3×3). For these analyses, we used data of

For analyzing decrease of correlation coefficients in case A

Case B focal (3×3) as the 100 m resolution case because ofduring the daytime, we made scatter plot among calculations andhigher correlation coefficients than those in Case B in previous

observation results. Figure 8 depicts a scatter plot of air

section. Cluster analysis used variables of 24 hourly air

temperatures at 15:00 JST on August 7 made using calculations

temperatures of August 7, 2015. We considered spatial

and observation results at 45 points. From this Figure, thedistributions of hourly air temperature change characteristics arecoefficient of determination was found to be higher in Case A

classified clearly by this setting. Number of cluster can bethan in Case B and Case B focal (5×5) at 15:00 JST on August 7.

defined freely from analysis result in hierarchical cluster

The plots are sparsely distributed in Case B compared with Case

analysis. Here, we defined five clusters in this study reffering to

A. However, most of plots are concentrated along the dotted line

YOKOHAMA CLIMAATLAS 2010(12).

in Case B focal (5×5). Furthermore, some plots is located far

Figure 9 shows a horizontal distribution of each zone. Figure 10

from the dotted line in Case B focal (5×5). These observation

presents the hourly average air temperature of each zone. From

points are located downwind factories in a coastal area of Tokyo

Figure 9, the distribution of each zone is classified mainly based

Bay and seems to be affected from anthropogenic heat. As

on the distance from coastal line of Sagami and Tokyo Bay inmentioned above, wind directions of the sea breeze from Tokyo

both cases. The urban climate in Yokohama is affected strongly

Bay in Case B is incorrect and this seems to generateby sea breezes during daytime. Additionally, distribution

underestimation in some observation points in high-resolution

patterns of the respective zones are similar between Case A andcalculation. This result shows that the air temperature calculatedCase B. Nevertheless, the shapes of respective zones are partly

using the high-resolution case has some dispersion, but relative

defined according to topography and natural areas such as

magnitude correlation is calculable correctly during daytime

valleys and downward slopes of green areas in Case B focal

using average values with surrounding meshes.

(3×3). Therefore, more detailed climatic zoning will be possible

From these results, high-resolution calculation seems to beusing high resolution for wide-area numerical calculations with apossible and effective for making more detailed climatic zoning

MSSG model.

maps. In the next chapter, we produce climatic zoning maps

Figure 10 shows that the maximum air temperature is highest in

using two calculation results are different on calculation

Zone 5 an inland area in both cases. The maximum air

accuracy of relative magnitude correlation.

temperature is the lowest in Zone 3, a coastal area of Tokyo Bay.
As shown in Figure 10, the times of maximum air temperature

3.3 Climatic Zoning in Yokohama

differ among zones in Case A. This time is defined as that when

Earlier sections describe that the calculate accuracy of relative

a sea breeze from Sagami Bay arrives at each zone. Therefore,

magnitude correlation of two calculations are analyzed throughsuch times of Zone 1 and Zone 2 locating near Sagami Bay arecomparison with observation results. Furthermore, utilization

earlier than those other zones. In addition, air temperatures in thepossibilities of high-resolution calculations for making climatic

morning are lower than those in other areas in Zone 3 and Zone

zoning maps were found.

4 located in coastal areas of Tokyo Bay because sea breezes

Therefore, hierarchical cluster analysis is performed to classify

from Tokyo Bay blow in the morning. However, temporalurban area in terms of urban climate using two calculationdifferences of maximum air temperature times among zones doresults. Cluster analysis is the statistical method classifying

not exist in Case B focal (3×3). Apparently, the temporal change

many samples into some groups by resemblance of each samaple.

of sea breezes from Sagami Bay and Tokyo Bay are not reflected

In this analysis, resemblance between each samaple are definedin this case compared to Case A. Most of the southern part ofas distance. At first, most similar pair of samples are

Yokohama and part of the coastal area of Sagami Bay aresummarized and first cluster is generated. Resemblance between

included in same zone (Zone 1) in Case B focal (3×3).

- 112 -¯¯Case A(500 m resolution)

Case B focal (3×3)(100 m resolution)

μ
0

2

μ

4

6

8 km

0

2

4

6

Zone 1

Zone 1

Zone 2

Zone 2

Zone 3

Zone 3

Zone 4

Zone 4

Zone 5

Zone 5

Green area
Water area

Green area
Water area

8 km

Fig. 10 Horizontal distribution of each zone: (left) Case A (500 m resolution) (right) and Case B-focal (3×3) (100 m resolution).
Distributions of the five zones are similar, but each zone of Case B focal (3×3) is partly shaped along the topography and the green
Case A (500 m resolution)

Case B focal (3×3) (100 m resolution)

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Air temperature [°C]

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20:00
21:00
22:00
23:00

Air temperature [°C]area.

Hour [h], JST
Zone 1

Zone 2

Zone 3

Hour [h], JST
Zone 4

Zone 1

Zone 5

Zone 2

Zone 3

Zone 4

Zone 5

Fig. 11 Hourly average air temperature of each zone: (left) Case A (500 m resolution) and (right) Case B (100 m resolution). Timeof maximum air temperature differs in Case A, but it is almost identical in Case B-focal (3×3).
4. Summary

zones were partly distributed along topography and natural areas

For this study, calculations of two kinds were executed using a

in the high-resolution case. Therefore, using a high-resolution

WRF model with 500 m resolution and an MSSG model withcase is apparently effective to produce more detailed climatic

100 m resolution to show that climatic zoning maps made fromzoning maps.

high-resolution calculation are effective because of including

Future works will address the following objectives.

detailed information. Furthermore, each calculation result was

1) Increasing the number of domains and expanding thecompared with observation results to analyze calculation

calculation areas of high resolution calculations using the MSSG

accuracy of relative magnitude correlation. In addition, climatic

model are necessary to reflect local wind environments such as

zoning maps were produced from calculation results obtained

sea breezes from Tokyo Bay in the morning.

using cluster analysis. The major findings are presented below.

2) Expansion of the calculation period and use of the average of

1) High-resolution case can reflect detailed effects of topographysome sunny days are necessary for each analysis.

and land use and improve calculation accuracy of relative

3) Consideration of suitable countermeasures is necessary for

magnitude correlation during the nighttime.

each zone to mitigate urban warning effects.

2) High-resolution case has some dispersion, but relative
magnitude correlation is calculable correctly during daytime

Acknowledgements

using average values with surrounding meshes.

This study was supported by JSPS KAKENHI grant number

3) Distribution patterns of respective classified zones are mostly

JP16J03661. This study was also supported by a grant of thethe same among two climatic zoning maps. However, some

Asahi Glass Foundation. For numerical calculation, this study- 113 -

used Earth Simulator, with the support of JAMSTEC.

Goto and H. Fuchigami, Challenge toward the prediction of
typhoon behaviour and down pour, Journal of Physics:

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(Received Apr. 30, 2017, Accepted Jul. 7, 2017)

on