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題目(和文)

気象予報モデルへの詳細な都市地表面パラメーターの導入が無降雨日の気象に及ぼす影響

Title(English)

The Meteorological Effects of Detailed Urban Parameter Inclusion to
Weather Research and Forecasting Model on Dry Days

著者(和文)

VarquezAlvin Christopher Galang

Author(English)

Alvin Christopher Varquez

出典(和文)

学位:博士（工学）,学位授与機関:東京工業大学,
報告番号:甲第9637号,
授与年月日:2014年9月25日,
学位の種別:課程博士,
審査員:神田 学,花岡 伸也,高木

泰士,木内

豪,中村

Citation(English)

Degree:,Conferring organization: Tokyo Institute of Technology,
Report number:甲第9637号,
Conferred date:2014/9/25,
Degree Type:Course doctor,
Examiner:,,,,

学位種別(和文)

博士論文

Type(English)

Doctoral Thesis

Powered by T2R2 (Tokyo Institute Research Repository)

恭志

2014
Doctoral Dissertation

Title:The Meteorological Effectsof Detailed Urban Parameter
Inclusion to Weather Researchand Forecasting Model on
Dry Days
Alvin Christopher Galang Varquez

Supervisor: Professor Manabu Kanda
Department of International Development Engineering
Tokyo Institute of Technology
Tokyo, Japan(page intentionally left blank)iiAbstract
The importance of new aerodynamic roughness parameter inclusion into weather models
was investigated. Previous models, such as the Weather Research and Forecasting Model(WRF), have improved representativeness of urban areas due to urban canopy coupling.
Success of previous models to simulate temperature and heat has been reported since then.
However, accuracy of wind speed still needs improvement. Thus, the aim of the study is tofurther improve wind speed simulation in WRF using a recently proposed roughness
parameterisation.
A new aerodynamic roughness parameterisation for zero-plane displacement andaerodynamic roughness length was recently derived from large-eddy simulation using real
urban morphology. A nationwide database of urban parameters – derived from a
combination of recently-derived parameterisation, processed urban building geometry data,and open source satellite images – were incorporated into the urban canopy model of WRF.
Three major studies were conducted to validate and apply the model to real and ideal
scenarios.
The first study was to test the performance of the model on a sea-breeze simulation in
Kanto region. Before focusing on a specific sea-breeze event, a 2-month simulation was
conducted to compare the new WRF model from the default and purely vegetation WRF
model version. Statistical analysis of wind speeds reveals better accuracy in the new model
than those using conventional roughness parameters. Slight overestimation in temperature
was simulated in the new model due in part to the increase level of urbanisation assumed
and possible misrepresentation by observation gauges. However, over-all the wind speediii

improvement was found to be very significant. Focusing on the sea breeze distribution,
surface drag was more apparent in the new case than in the default and vegetation cases.
This large reduction of sea breeze intensity at densely built-up areas was also confirmed bya wind speed measurement network in Kanto region. Because of the surface drag frombuildings, sea-breeze delay was simulated; and the new case showed better accuracy in thefronts when compared to real cloud images taken from a satellite. The sea breeze delay also
reduces advection of heat above urban areas resulting to stronger mixing above urban
canopies. Thus, a higher boundary layer height was simulated in the new case. This also
resulted to horizontal wind speed reduction at several hundreds of meters above ground andwithin the boundary layer.
The second study was conducted to apply the new WRF model at other cities such as
Osaka and Nagoya. Furthermore, the study also focuses on the comparison between thesignificance of anthropogenic heating and aerodynamic roughness. In this study, anationwide distribution of roughness parameters and anthropogenic heat was included. The
new, default, and vegetated models were used to simulate 3-days of August 2013. Nearground wind speeds were also simulated well in the new case when compared with groundobservations. Differences in accuracy between the new and default model were
proportional to the degree of roughness assumed in the evaluated grid. In other words,
larger roughness shows better accuracy in wind speed. Further comparisons between newmodel and default model were conducted using simulated near ground wind speed andconvergence. In Osaka, the sea breeze front becomes distorted due to the effect of buildings.
Similar to the previous study, horizontal wind speed reductions were also found ativ

significant hundreds of meters above ground and within the boundary layer. Finally,
aerodynamic roughness has a more dominant influence to near ground wind speed than
aerodynamic heating. In this study, it was also found that Nagoya has a more complex
atmospheric environment compared to Osaka and Tokyo because only slight improvement
was found in the new case.
Lastly, an idealization of WRF was introduced to test the model’s sensitivity to urban
parameters. The domain was set up having a linear boundary between the sea and land. The
land was assumed urban with uniform surface boundaries. Sea breeze was triggered byintroducing higher temperature over land than sea. The same meteorological backgroundwith no winds as the sea breeze date and a periodic lateral boundary was applied.
Sensitivity cases were conducted by varying the zero-plane displacement height, d,roughness length for momentum, zo, and sky-view factor, SVF. From the trend of heat flux,
moisture, and wind speed; it was found that zo has the largest influence to wind speed andheat flux. The near ground wind speed is largely reduced and heat is emitted less at higher
zo. As a result, large difference in boundary layer evolution was also found. The delay insea breeze at larger zo resulted to a larger boundary layer height later in the day. Decreasing
SVF at urban areas results to lower heat flux and lower wind speed similar but not assignificant as increasing zo. d has a similar effect to a hill and was found to have the least
impact to the meteorological parameters.

v

Table of Contents

List of Figures and Tables ................................................................................................. x
1

Introduction .............................................................................................................. 1
1.1 Urbanization and Weather ......................................................................................... 1
1.2 Weather Modelling at Urban Areas ........................................................................... 2
1.3 Sea Breeze and Urban Area Interaction .................................................................... 3
1.4 Research Problem ...................................................................................................... 5
1.5 Objectives and Originality of the Study .................................................................... 6
1.6 Dissertation Outline ................................................................................................... 7

2

Theoretical Background ........................................................................................... 9
2.1 Dynamical Downscaling ........................................................................................... 9
2.2 Single-layer Urban Canopy Model .......................................................................... 10
2.3 Aerodynamic Roughness Parameterisation ............................................................. 14
2.3.1 Definition .................................................................................................. 14
2.3.2 Building Database Construction ............................................................... 16
2.3.3 Roughness Parameter Derivation ............................................................. 17
2.4 Other Urban Parameters .......................................................................................... 20
2.5 Chapter Summary .................................................................................................... 21vi

3

Model Description and Improvement..................................................................... 22
3.1 Weather Research and Forecasting Model .............................................................. 22
3.2 Incorporating New Aerodynamic Roughness Parameters ....................................... 24
3.3 Additional Modifications in the Single-Layer Urban Canopy Model ..................... 26
3.3.1 Bulk Transfer Coefficient Revision .......................................................... 26
3.3.2 Modified Urban Fraction .......................................................................... 27
3.3.3 Consideration of 3-D Urban Surfaces ...................................................... 29
3.3.4 Vegetation Effect on Transfer Coefficients .............................................. 32
3.4 Chapter Summary .................................................................................................... 34

4

Impact of A Distribution of Improved Roughness Parameterisation on a SeaBreeze Simulation .................................................................................................. 35
4.1 Numerical Settings .................................................................................................. 35
4.2 Model Validation ..................................................................................................... 38
4.2.1 Near-ground wind speed ........................................................................... 42
4.2.2 Near-surface Temperature ........................................................................ 43
4.3 Results and Discussion ............................................................................................ 47
4.3.1 Sea Breeze Analysis from Clouds ............................................................ 47
4.3.2 Near-ground Winds .................................................................................. 51
4.3.3 Sea Breeze Convergence Inland of Tokyo ............................................... 55

vii

4.3.4 Atmospheric Boundary Layer Structure Above Convergence Region 2 . 58
4.4 Chapter Summary .................................................................................................... 60
5

Improved WRF Model Application at Major Cities in Japan ................................ 62
5.1 Numerical Settings .................................................................................................. 63
5.2 Model Validation ..................................................................................................... 66
5.2.1 Near-ground Wind Speed ......................................................................... 66
5.2.2 Near-surface Temperature ........................................................................ 69
5.3 Results and Discussions .......................................................................................... 70
5.3.1 Vertical Profile of Horizontal Winds ........................................................ 70
5.3.2 Surface Drag from Roughness .................................................................. 72
5.3.3 Atmospheric Boundary Layer .................................................................. 74
5.4 Chapter Summary ................................................................................................... 75

6

Sensitivity of WRF Model to Roughness Parameters and Sky-view Factor using a2-dimensional Idealised Case ................................................................................. 77
6.1 Numerical Settings ................................................................................................. 78
6.1.2 Geographical set-up .................................................................................. 78
6.1.3 Meteorological set-up ............................................................................... 80
6.2 Results and Discussion ............................................................................................ 83
6.2.1 Effect of Roughness Parameters d and z0m.............................................. 83viii

6.2.2 Effect of Sky-view .................................................................................... 88
6.2.3 Sensitivity of Boundary Layer Height ...................................................... 90
6.3 Chapter Summary .................................................................................................... 91
7

Conclusion .............................................................................................................. 93

Appendix .......................................................................................................................... 97
A. Individual influence of geometrical parameters to d and zo ................................... 97
B. Default vs. proposed estimation of sky-view factor ............................................... 99
C. The Single-layer Urban Canopy Model by Kusaka et al. (2001) ......................... 100
Acknowledgments ......................................................................................................... 103
References ...................................................................................................................... 103

ix

List of Figures and Tables

Figure 2-1 Scales of dynamical downscaling ......................................................................... 9
Figure 2-2 A schematic of the single-layer urban canopy model (represented by SLUCM)
and mutli-layer urban canopy model (represented by BEP) acquired from Chen et al.
(2011) ............................................................................................................................... 13
Figure 2-3 Comparison of zero-plane displacement height used in this study to previously
used distribution ............................................................................................................... 20
Figure 3-1 Flowchart of WRF simulation ............................................................................ 23
Figure 3-2 Estimated displacement height, d (Left), and roughness length for momentum, zo(Right). Water bodies and areas with missing information are filled in white. ............... 24
Figure 3-3 The modified urban fraction, urb_frc (Left). The difference between the default
urban fraction (0.90) at urban dominant grids and urb_frc (Right). Grey represents nonurban dominant grids. ...................................................................................................... 29
Figure 3-4 1-km gridded sky-view factor derived for Kanto region following Kanda et al.
(2005). Blue areas have missing or zero values............................................................... 30
Figure 3-5 Sky-view factor difference between proposed WRF model and default WRF
model. Red shows underestimation of default WRF. Blue shows overestimation ofdefault WRF. Grey-fill shows non-urban dominant grids with urban fraction greater than
0 (i.e. no urban effect calculated in the default WRF). .................................................... 32
Figure 3-6 Input vegetation fraction for August (left) and September (right). Blue areas
have zero or missing values. ............................................................................................ 33x

Figure 4-1 WRF Simulation domains and geographical boundaries for September 2011
simulation focused on Kanto Region. .............................................................................. 36
Table 4-1 WRF Numerical Parameters ................................................................................ 38
Figure 4-2 JMA observation points overlaid on model ground topography (shown in light
contour lines with units = m). Topography contour starts from 100 m at 200 m intervals.
......................................................................................................................................... 40
Table 4-2 Observation gauges information (refer to Figure 4-2 for the location) ................ 41
Table 4-3 Statistical analysis of height-adjusted wind speed at selected AMeDAS stations(D = Day; N = Night) ....................................................................................................... 43
Table 4-4 Statistical analysis of the 2-m temperature at selected AMeDAS stations (D =Day; N = Night) ............................................................................................................... 45
Figure 4-3 Five-day trend of hourly height-adjusted wind speed at U1 (left) and R2 (right)......................................................................................................................................... 46
Figure 4-4 Five-day trend of hourly 2-m temperature at U1 (left) and R2 (right) ............... 46
Figure 4-5 September 14, 2011, 1600 LST (UTC = LST – 9 hours) Rapidscan geostationary
satellite image. White patches correspond to clouds. Sea-breeze fronts are shown with
red lines. Dashed blue green lines indicate densely built-up regions with d > 10 m. Solidblue green line indicates location of a common cloud street. .......................................... 47
Figure 4-6 September 14, 2011, total column cloud mixing ratio from NEW (left), CTL(centre), VEG (right). Red lines represent Rapidscan geostationary satellite cloud
outlines drawn from Figure 4-5. Blue lines represent the sea breeze front from the firstatmospheric-level convergence. Dashed black lines indicate regions with d > 10 m. Red

xidots mark the location of U1 and R3 (labelled in green). Dashed light blue lines indicate
the location of a common cloud street. ............................................................................ 48
Figure 4-7 September 14, 2011, WRF hourly-averaged horizontal wind-speed verticalprofiles above U1 on 1200 LST (a) and 1500 LST (b); and above R1 on 1200 LST (c)and 1500 LST (d) ............................................................................................................. 51
Figure 4-8 September 14, 2011, wind observed at 25 m (left), NEW 10-m wind (centre),
NEW first atmospheric-level wind (right). Darker wind vectors have wind speed >= 5
m/s. Dashed red lines indicate regions with d > 10 m. Green dots represent AMeDAS
stations. Dashed light blue lines indicate the location of a common cloud street. .......... 52
Figure 4-9 September 14, 2011, wind simulated by WRF at 10-m height (darker windvectors >= 4 m/s) and first atmospheric-level convergence (blue lines) for NEW (left),
CTL (centre), and VEG (right). Dashed red lines indicate regions with d > 10 m. Greendots represent AMeDAS stations. .................................................................................... 53
Figure 4-10 Continuation of Figure 4-9 ............................................................................... 54
Figure 4-11 September 14, 2011, 1600 LST divergence of first atmospheric winds for NEW(left), CTL (centre), and VEG (right) at 1600 LST. Regions bounded by dashed lines
represent the analysed convergence zones. The dashed line represents the location of thevertical slice used in Figure 4-12 ..................................................................................... 56
Figure 4-12 Vertical cross-section for September 14, 2011, 1600 LST (slice location shownin Figure 4-11) vertical wind (contour), wind velocity (vectors) calculated from west-toeast wind component and vertical wind component (enhanced by 2.5), and planetary
boundary-layer height (solid lines) .................................................................................. 58

xii

Figure 5-1 Domain coverage of WRF simulation over major cities in Honshu, Japan. Red
boxes cover child domains. .............................................................................................. 62
Figure 5-2 Distribution of d, zo, and 1200 LST AHE for Tokyo domain (Top), Osaka
domain (Middle), and Nagoya domain (Bottom) domains. The selected AMeDAS
stations for comparison are encircled in the d distribution. ............................................. 64
Table 5-1 Domain Settings ................................................................................................... 65
Table 5-2 Simulation Cases .................................................................................................. 65
Figure 5-3 RMSE values for wind speed (Above) and 2 m temperature (Below) for eachcase and AMeDAS station. Corresponding d and z0m plotted in filled triangles. .......... 67
Figure 5-4 3-day observed and simulated wind speed trend from August 10 – 13, 2013 .... 68
Figure 5-5 3-day observed and simulated T2 trend from August 10 – 13, 2013.................. 69
Figure 5-6 CTL and NEW horizontal wind speed difference averaged from 58 hourly data
covering August 10 – 13, 2013 (dots); CTL and NEWnAH difference (lines) ............... 71
Figure 5-7 August 12, 2013 hourly snapshot of 10 m sea breeze flow (dark wind vectors >3 m/s), and 1st atmospheric level divergence (contours) towards Osaka for NEW and
CTL .................................................................................................................................. 73
Figure 5-8 August 11, 2013 hourly snapshot of 10 m sea breeze flow (dark wind vectors >3 m/s), PBLH (solid black lines) and 1st atmospheric level divergence (colored lines) for
NEW and CTL ................................................................................................................. 74
Figure 5-9 August 11, 2013 trend of PBLH, sensible heat flux (HFX), and 1st atmosphericlevel divergence in Tokyo AMeDAS station ................................................................... 75

xiii

Figure 6-1 General domain configuration. (Left) Cases with no varying displacement
heights. (Right) Cases with varying displacement height. ............................................... 79
Figure 6-2 Input pressure. Time is in UTC (LST minus 9 hours) ........................................ 81
Figure 6-3 Input relative humidity. Time is in UTC (LST minus 9 hours) .......................... 82
Figure 6-4 Input air temperature. Time is in UTC (LST minus 9 hours) ............................. 82
Figure 6-5 Input sea surface temperature (SST) and land skin temperature (SKINTEMP)
from 6 AM of September 12, 2011 to 6 PM of September 14, 2011 .............................. 83
Figure 6-6 Simulated meteorological parameters for each case and location, where:(Leftmost) first atmospheric level vapor mixing ratio; (Middle) first atmospheric levelwest-east wind component; (Rightmost) sensible heatflux. Top and bottom figures arehourly signals for pt. A and pt. B, respectively. .............................................................. 85
Figure 6-7 Simulated meteorological parameters for each case and location, where:(Leftmost) first atmospheric level vapor mixing ratio; (Middle) first atmospheric levelwest-east wind component; (Rightmost) sensible heatflux. Top and bottom figures arehourly signals for pt. A and B, respectively. ................................................................... 86
Figure 6-8 Simulated first atmospheric winds (colored vectors) and total colulmn cloud
mixing ratio (shaded) for various cases at 1300 LST. ..................................................... 87
Figure 6-9 Simulated meteorological parameters spatially averaged over land, where:(Leftmost) first atmospheric level vapor mixing ratio; (Middle) first atmospheric levelwest-east wind component; (Rightmost) sensible heatflux. ............................................ 88
Figure 6-10 Simulated meteorological parameters for each case and location, where:(Leftmost) first atmospheric level vapor mixing ratio; (Middle) first atmospheric levelxiv

west-east wind component; (Rightmost) sensible heatflux. Top and bottom figures arehourly signals for pt. A and B, respectively. ................................................................... 89
Figure 6-11 1000 LST, 1200 LST, and 1700 LST snapshots of the distribution of boundary
layer height for cases: displow (A), disphigh (B), zlow (C), zhigh (D), svmid (E), andsvfhigh (F)........................................................................................................................ 92
Figure A-1 Combination of possible values of d and zo for uniform arrays using MD1998
and KA2013 ..................................................................................................................... 97
Figure A-2 Combination of possible values of d and zo using MD1998 and KA2013 withvarying Hmax and σH for fixed Have, λp and λf. Red asterisk represents uniform arraycondition. ......................................................................................................................... 98
Figure B-1 Comparison of sky-view factor derived from the default UCM and the proposed
update from section 3.3.3 ................................................................................................. 99xv

1 Introduction

1.1 Urbanization and Weather
The ratio of population at urban and rural areas has risen in the last 5 decades due to theshift from agriculture-based economy to mass industry, technology and service. By 2050,
the population at urban areas is projected to double that of 2009. Approximately,urbanization has been increasing by 3% annually since 1950 (Global Health Observatory,
World Health Organization). Along with increasing population, globalization promotes
economic growth and overseas migration. The rise in economy corresponds to increase inwork spaces like industrial and commercial buildings. The increase and shift in population
also results to an increase in urban settlements such as high-rise buildings, low-rise andmiddle-rise residential areas. The implication of rapid urbanization to weather istremendous.
Initially, a noticeable warming at urban areas has been observed for decades (Arakawa,
1937; Oke, 1973; Yamashita, 1994). When plotting isothermal at the urban environment, an
isolated region could be form above the city referring to it as the “urban heat island”. This
causes various socio-economic and environmental problems. According to reports from theIntergovernmental Panel on Climate Change, heat stroke is a common occurrence during
summer. Indirectly, temperature differences affect the wind and moisture circulation aroundand above urban areas. This leads to unexpected thunderstorms in the suburban areas,
flooding, and sea breeze delays. Disease-carrying organisms could also thrive in an urban
environment where moisture is stagnated due to poor ventilation. The vulnerability is high1but to mitigate it is difficult because of the limited understanding and resources. Various
approaches to its understanding include observation, theoretical approaches, and modelling.
The approach used in this research is modelling.
1.2 Weather Modelling at Urban Areas
As an alternative to observations which can be cost-intensive and impractical in thelong-term especially at developing cities, numerical weather models (NWP) are very usefulin mitigation and planning. It is the mathematical approach to forecasting (Abbe, 1901). In
fact as a tool of atmospheric research, modelling goes hand-in-hand with observation andtheoretical studies (Warner, 2011). NWPs, with its long history, have evolved to adequately
represent real environmental weather conditions (Lynch, 2008) owing to the developmentof sophisticated land surface models such as the Noah land surface model (Ek et al., 2003)
and the urban canopy models. Along with the advancement in computational capacity andmeteorological knowledge, models have been upgraded to improve performance at urban
areas where vulnerability is highest (Chen et al., 2011). The motivation for doing this isbecause urbanization is phenomenal to modify the existing and surrounding environment(Duh et al., 2008; Changnon, Jr., 1973; Shafir and Alpert, 1989). The Weather Researchand Forecasting Model (WRF) by Skamarock et al. (2005), among highly sophisticatedmesoscale NWP system collaboratively designed to meet operational forecasting andatmospheric needs, provides urban physics option such as the single-layer Urban Canopy
Model (UCM) of Kusaka et al. (2001) and Kusaka and Kimura (2004). The UCM is to-date
the most widely used model for urban representation due to its sophisticatedparameterisations without sacrificing calculation efficiency. Prior to the UCM, slab models,2

(Liu et al., 2006) which assume bulk values for urban parameters, were used. Details of theurban canopy model and its parameters are mentioned in the later chapters.
1.3 Sea Breeze and Urban Area Interaction
Sea breeze is generally caused by the local pressure gradient prevalent during daytime
between the land and the sea. Land warms faster than the sea during daytime. The warmingof land will cause air mass directly above it to mix further above. This mixing, called freeconvection, directly affects the height of the boundary layer, the region in the atmospheredirectly influenced by the surface. The rising of air leads to a pressure drop on land.
Because the temperature above the sea varies less throughout the day, the sea level pressure
will remain constant. The difference in pressure between land and sea triggers the sea
breeze flow. Nowadays, increased urbanization causes a non-uniform distribution ofheating on land. The warmer urban areas have a tremendous effect to the sea breeze flow.
Sea breeze circulation is directly affected by urban areas since most urban areas lie very
close to the land and sea boundary. Urban areas tend to affect sea breeze,
thermodynamically and aerodynamically. Likewise, sea breeze also has a significant role tothe atmospheric condition of urban areas. Specifically, studies have already been conducted
regarding Kanto’s urban heat island and their influence on its sea breeze flow.
Sea breeze advancement can be delayed by additional heating from urban areas(Yoshikado et al., 1989; Chuan-Yao et al., 2008) and reduced nocturnal cooling. Kimura
and Takahashi (1991) investigated the effects of land cover and anthropogenic heating on
the surface temperature in Tokyo. Kanda et al. (2001) applied the idea further byinvestigating the formation of cloud lines above a major street in the Tokyo metropolitan
3area as a function of sea breeze convergence and urban heating. From numericalexperiments, the position of cloud lines during the day was also found to be a factor in sea
breeze behaviour (Inoue and Kimura, 2007). Prior to penetrating to inland regions of Kanto(e.g., Saitama and its surroundings (Fig. 1) and coastal cities such as Tokyo), the distancefrom the shore line acts as a heat sink, weakening sea breeze capacity, thus maintaining
high temperatures inland during summer (Sato et al., 2008). This was also observed in astudy conducted by Chen et al. (2011a) in the Greater Houston area of the USA. Scalaremission from urban areas also affects sea breeze flow. According to Hosoi et al. (2011),
two thermal lows tend to occur in western and northern Kanto on sunny days, promoting asoutherly sea breeze and transporting photochemical oxidants. This thermal low is largely
influenced by urban areas triggering sea breeze penetration. Tokyo acting as an upwind
area during summer influences the heat advected inland (Aoyagi). All the mentioned pastmesoscale modelling in Kanto, idealized or real, were not able to fully replicate the sea
breeze due to the misrepresentation of actual roughness distribution at highly urbanized
cities.
Some observational studies and idealised simulations from foreign researchers havementioned specifically the aerodynamic effect of buildings to sea breeze (Martilli, 2003;
Prtenjak, 2002). Investigations in New York (Childs and Raman, 2005) and Athens(Dandou et al., 2009) sea breeze simulations using weather models obtained similarfindings as above but the roughness parameters used were coarsely (or uniformly)
distributed and underestimated.

4

1.4 Research Problem
The coarseness and underrepresentation of urban areas can be problematic in simulationof real weather. Thus, improvements which are discussed in the later chapters are stillnecessary. The focus of this study is on how weather models, such as WRF, can be
improved especially in the aerodynamic aspect (e.g. wind field, surface drag).
Although coupling WRF and UCM improves the accuracy of simulated temperature,
recent evaluation studies remain to detect overestimation of surface wind speeds at
urbanized areas. Kondo et al. (2008) confirmed that urban canopy model performance
depend on wind direction and consistently overestimates canopy wind speeds at an area
within and almost down-wind of a highly dense area within Tokyo (values of roughness
length for momentum, zo, range from 0.5-1.0 m). Consistent overestimation were also
found in a WRF/Urban model simulation over greater Paris, using single set of parameters
for the whole area, regardless of planetary boundary layer schemes used (Kim et al., 2013).
Although sensitive to surface type, low roughness parameter assumptions such as 0.7(1.4) –1.6(11) m for zo(d) still overestimate wind speed based from sensitivity tests in a USCanada border (Flagg and Taylor, 2011). Using the simple rule-of-thumb method
prescribed by Burian et al. (2002), WRF coupled with the Noah land surface model andsingle-layer urban canopy model improves reproducibility of urban heat island on asummer day in Beijing but still slightly overestimated urban canopy wind speed comparedwith observed during daytime (Miao et al., 2009). Other researchers suggest other factors
affecting modelled wind inaccuracies. Lee et al. (2010) noticed overestimation of windspeed and underestimation of vertical gradient of wind speed due to excessive turbulent

5

mixing when applying WRF with urban canopy model to Houston-Galveston Metropolitan
Area. Despite this, urban canopy model is still detrimental to the simulated wind speed atsurface level above urban areas.
Some researches would resort to recommending more complex models such as themulti-layer urban canopy models (Martilli et al., 2002; Kondo et al., 2005; Salamanca et al.,2011). However, multi-layer urban canopy models are resource-demanding and its
parameters, which are not readily available, may vary highly per location. Multi-layer
schemes, when not properly set-up, may even perform worse in wind speed simulations
compared to single-layer (Holt and Pullen, 2006). According to comprehensive review byChen et al. (2011b) regarding WRF and urban canopy model coupling, initialization of statevariables is still left with little attention by the urban modelling community. Further, if
mesoscale models are to be used anywhere globally, simple but highly accurate
parameterisation in urban canopy models is necessary. The wind speed overestimation issue
above urban areas need to be addressed, first, prior to creating more complex models andapplying it to highly urbanised locations.
1.5 Objectives and Originality of the Study
The main objective of this study is to improve urban representation in weather models tofurther understand impacts of urbanization to weather. The objectives can be subdivided to
as follows:
1. To incorporate distribution of new aerodynamic roughness parameters into a
numerical weather model (chapter 3);

6

2. To further improve parameterisation within single-layer urban canopy model(chapter 3);
3. To evaluate the performance of updated model (chapter 4);
4. To improve and understand urban roughness effects to sea breeze and wind field at
major cities in Japan (chapter 5);
5. To understand how each roughness parameter and another urban parameter affect thesimulation results of the numerical weather prediction model (chapter 6).
In line with this, this research has achieved the following points of originality:
1. FIRST-EVER application of new aerodynamic roughness parameters into a
numerical weather prediction model coupled by the single-layer urban canopy model(chapter 4 and 5);
2. Simulation of sea breeze using nation-wide urban parameter distribution, preparedby Makabe et al. (2014), derived from real building morphology (chapter 5);
3. Modification of single-layer urban canopy model in WRF to read distributed
parameters (chapter 3);
4. Better understanding of sea breeze/wind field changes at urban areas in terms ofurban heating and roughness (chapter 4);
5. Comparisons of relative influence of zero-plane displacement height, aerodynamic
roughness length for momentum, and sky-view factor (chapter 6).
1.6 Dissertation Outline
This chapter is succeeded by the theoretical background, followed by the discussion on
the model improvements, the model validation, and application. Chapter 1 is about the7introduction and problem statement. The application of weather models to urban areas andthe introduction of urban-sea breeze interaction are included. Chapter 2 deals about thetheoretical background relevant to the study. Dynamical downscaling, urban canopy
modelling, and the aerodynamic roughness parameters, and other urban parameters are
discussed. Chapter 3 is about the model building from incorporating urban geometricparameters to physics improvements. Chapter 4 discusses the testing and application of the
improved WRF model on a sea breeze case in Kanto region. Chapter 5 is the expanded
application of the improved WRF in a nationwide scale with focus on major cities of Tokyo,
Osaka, and Nagoya. Aside from the application, chapter 6 discusses how each improvement
influences the model outputs using an ideal world scenario in WRF.

8

2 Theoretical Background

2.1 Dynamical Downscaling

Figure 2-1 Scales of dynamical downscaling

The study of urban meteorology is conducted via numerical or observational approaches.
This research involves a numerical approach specifically called regional climate
downscaling. Before going deeper into this study, it is necessary for the general readers tounderstand briefly the concept behind downscaling. Various scales in meteorology havebeen introduced in the past by Orlanski (1975). Downscaling is the forecasting of smallscale features of a region, estimated from large-scale structures. The large-scale structures,
often fed by temporally and spatially coarse global datasets, are used as lateral boundaries.
Thus, the accuracy of regional climate models is highly dependent on the lateral boundaries.
These lateral boundaries are usually in the scale of 30 arc-seconds to 1.0 degrees. Among

9

leading source agencies of lateral boundaries are National Center for Environmental
Prediction (NCEP), European Centre for Medium-Range Weather Forecasts (ECMWF),
and the Japan Meteorological Agency (JMA). The large-scale structures are not necessarily
simulated solely by a model but in fact derived, using assimilation methods, from external
or observation sources. From the lateral boundaries, mathematical models allow the
reproduction of finer scale structures. A sequence of downscaling from global to local scaleis shown in Figure 2-1. The most common application of downscaling is long-term
forecasting from general circulation models. The concern of urban meteorology, however,is on the understanding of mesoscale processes such as extreme rainfall and the urban heatisland. The downscaling model used in this research is a well-known NWP called theWeather Research and Forecasting Model. Instead of forecasting future weather, the focus
is on the improved simulation of past events, called hind-casting.
2.2 Single-layer Urban Canopy Model
As the resolution of NWP increases, the influence of the surface to the simulation results
increases. In WRF, land surface schemes and urban schemes are necessary settings toparameterize the surface. For non-urban surfaces, the Noah land surface model (Tewari etal., 2008) is widely used. To consider urban surfaces, various options are available (Chen etal., 2011). Among them is the single-layer urban canopy model, henceforth called UCM,
proposed by Kusaka et al. (2001) and Kusaka and Kimura (2004). This model assumes aninfinitely-long street canyon (for each calculation grid) parameterized to represent urban
geometry with a three-dimensional nature. The heat fluxes from the roof, wall, and groundare calculated using the widely used Monin-Obukhov similarity theory and the Jurges
10formula. Currently available as a default option in WRF, it was known to improve accuracyof simulated temperature at urban areas especially when coupled with the Noah land
surface model. Although the UCM prescribes the condition within the urban canopy layer,
its main purpose in WRF is to provide a realistic simulation of the urban boundary layer,
the region above the urban canopy layer.
UCM has the following features:
1) 2-D street canyon parameterisation to represent effects of urban geometry on urban
canyon heat distribution.
2) Shadowing from buildings and reflection of radiation in the canopy layer
3) Canyon orientation and diurnal cycle of solar azimuth angle
4) Man-made surface consisting of eight canyons with different orientation
5) Inoue’s model for canopy flows
6) The multi-layer heat equation for the roof, wall, and road interior temperatures
7) Anthropogenic heating associated with energy consumption by human activities
8) Very thin bucket model for evaporation and runoff from road surface
Below is a list of urban parameters used in WRF (Ching et al., 2009),
Urban fraction

Building height

Roughness for momentum above the urban canopy layerRoughness for heat above the urban canopy layer

Zero-displacement height above the urban canopy layerPercentage of urban canopy11Sky-view factor

Building coverage ratio

Normalized building heightDrag coefficient by buildings

Building volumetric parameterAnthropogenic heat



Heat capacity of the roof, wall, and road

Heat conductivity of the roof, wall, and roadAlbedo of the roof, wall, and roadEmissivity of the roof, wall, and roadRoughness length for momentum of the roof, wall, and roadRoughness length for heat of the roof, wall, and road

The current UCM model, however, provides very limited assumptions for aerodynamic
roughness length, displacement heights, and anthropogenic heat emission. In other words
the parameters were assumed from uniform arrays, and its application unjustifiably lessrepresentative of real urban environment. The urban parameters, listed above, are read froma table in the model. In the table, parameters vary based on three urban classifications: low
intensity residential, high intensity residential, and commercial districts. The default
parameters in the table, which can immediately be edited by the user to suit the region of
study, were based on reports of Burian and Han (2003) and Burian et al. (2003). The report
was created from National Land Cover Data for the United States (NLCD) and U.S.
Geological Survey (USGS).
12

Other versions of the urban canopy model, beyond the scope of this study, have beendeveloped as well. One of them are the mutli-layer urban canopy models (Martilli et al.,
2002; Kondo et al., 2005; Salamanca et al., 2011; Chen et al., 2011). With a moresophisticated modelling, it allows direct interaction with the planetary boundary layer
schemes unlike the UCM. It considers the three-dimensional nature of urban surfaces andthe vertical distribution of heat/moisture sources and sinks. Another is an improvement of
the UCM developed in Princeton University, called “PUCM”, which also includes theeffect of vegetated surfaces in the surface energy budget (Wang et al., 2013). A
comparative diagram from the UCM and multi-layer urban canopy models are shown in
Figure 2-2.

Figure 2-2 A schematic of the single-layer urban canopy model (represented by SLUCM)
and mutli-layer urban canopy model (represented by BEP) acquired from Chen et al. (2011)

A sensitivity study of using a distribution of the parameters above for Houston, Texas
was conducted by Salamanca et al. (2011) using the multi-layer urban canopy models.
Distributed (nationwide) urban parameter testing using the single-layer urban canopy model
is yet to be conducted with up-to-date aerodynamic roughness paremeterisations.

13

2.3 Aerodynamic Roughness Parameterisation
2.3.1 Definition
To appreciate the concept of aerodynamic roughness, its background importance isdiscussed. Near the surface, the atmosphere is subjected to various forcings. The lowest
layer of the atmosphere directly affected by the surface is commonly referred to as theplanetary boundary layer (or atmospheric boundary layer). This layer can be visible on aclear day and characterized by strong mixing (e.g. pollutants, cloud formations) above
cities. Occasionally, the layer can be judged visually from the clear separation of pollutantsa few hundreds to a kilometre near the surface. On cloudy days, the height is commonly
estimated from the bottom of the clouds. The evolution of the boundary layer is affected by
two types of convection: free and forced. Free convection is the rise of heated air from thesurface. Forced convection, two-dimensional in nature, is the result of the drag effect of
surfaces to winds. The drag is brought about by the shape of roughness elements above theground. Examples of these roughness elements near the surface are gravity waves, grass,trees, and man-made structures. The rougher the surface, the more turbulent the urban
boundary layer becomes thus contributing to the whole boundary layer.
The drag can be visualized using the wind profile. The profile, logarithmic in form, isrelated to the transport of momentum and heat from the surroundings. In neighbourhood
scale, the drag can be measured directly when realistic urban geometry is used. However inmesoscale simulations where urban geometry cannot be resolved explicitly, aerodynamic
roughness parameterisation is needed. In urban boundary layer, aerodynamic roughness
length for momentum, zo, and zero-plane displacement height, d, are the necessary
14

roughness parameters. The logarithmic wind profile applicable to the urban boundary layer
can be generalized by the equation,uu*   z  d     ,lnk   z o (1)where u is the horizontal wind speed, u* is the friction velocity, k is the Von Karman
constant, z is the elevation from ground, and  is the stability parameter function.  iszero under neutral conditions.
The definition of d and zo can be understood from eqn. 1. The displacement height, d,determines the contributed effect of roughness elements to displace the surface of thelogarithmic wind upward within a grid. For example, trees packed close enough will appear
like a displaced surface from above. Similarly, building density influences the height of thedisplaced wind profile. This is the reason why d is subtracted to z. On the other hand, zo,
smaller in magnitude than d and actual roughness elements, is the parameter that affects theshape of the wind profile. Traditionally, it is determined by extrapolating the measured
logarithmic wind profile to zero when plotted on a semi-log graph.
It is to be noted that the large d and zo in some locations do not necessarily mean zero
wind values inside the canopy. The actual winds within the canopy layer (close to averagebuilding heights) are much more complicated and would require higher resolution model toresolve the values realistically. For the purpose of this study, the d and zo are used in the
UCM to prescribe the winds above the urban canopy layer (or within the urban boundary
layer).

15

WRF is capable in simulating wind and temperature values within the urban canopy
layer diagnostically using the Monin-Obukhov similarity theory. These diagnostic
parameters are representative of the assumed urban canopy layer configuration in the gridand useful for practical applications.
2.3.2 Building Database Construction
The building parameters used to derive the roughness parameters are initially discussed.
Makabe et al. (2014) created a 1-km spatial resolution database of building height from a
Geographic Information System (GIS) dataset. The dataset provided by the Environmental
Systems Research Institute, Inc. (ESRI) contains building floor information together withthe area occupied by the building in vector format in 0.05 arc seconds (approximately 1.5m). Assuming 4 m floor height, the heights were estimated. The data was grouped into
meshes (approximately 1 km per side) and building statistics were derived which is definedlater in the chapter 3. The following information were also provided in the database:a. average building height (units in meters), Haveb. maximum building height, Hmaxc. standard deviation of building heights, σHd. plane area index, λpe. frontal area index, λfHave is the average height of all buildings within the grid. Hmax is the height of the tallest
building within the grid. σH is the standard deviation of all heights of buildings within thegrid. λp is a dimensionless parameters defined as the fraction of area occupied by buildings
within a grid. To put it simply, it determines how much of the area is covered by the16buildings. On the other hand, λf is calculated from the frontal area of all the buildings
divided by the total grid area. The frontal area here is assumed to be oriented along thedirection wind. From this definition, λf contributes to how much of an incoming wind isaffected by the buildings. Ideally, this value varies with wind direction but current WRF
only considers one value, averaged for 8 directions.
2.3.3 Roughness Parameter Derivation
The aerodynamic roughness parameters, roughness length for momentum anddisplacement height are essential canopy parameters for accurate wind field distribution.
The method discussed here only applies to buildings. Various methodologies for estimating
the roughness parameters used in urban canopy models (for other urban parameters refer top. 10) have been extensively reviewed by Grimmond and Oke (1999). Derived fromexperiments using simple building arrays, previous parameterisations were proven toaccount for larger heat storage of cities but do not physically estimate the drag due tocomplex urban morphology. In terms of urban canopy modelling in standard WRF,
assumed zo and d is still estimated using the conventional parameterisations (Burian et al.,
2002; Burian and Ching, 2009; Macdonald et al., 1998, henceforth denoted as MD1998)
leading to the issue mentioned earlier. A new aerodynamic roughness parameterisation
recently introduced by Kanda et al. (2013), henceforth denoted as KA2013, is proposed for
integration with the urban canopy model. The highlight of this new parameterisation is in
the sophistication and straightforwardness of estimating d and zo. The formulated equation
was derived from a large eddy simulation using real building configurations in Tokyo and17Nagoya. The recent method provides more representative and pronounced feedback from dand zo values.
The difference between KA2013 and MD1998 lies in the consideration of two gridded
parameters, Hmax and σH, for the calculation of d and zo. In the default single-layer urban
canopy model, the equations for d and zo are calculated as follows according to MD1998;d mac  1  A p  p  1,
H ave
0.5
 
zo mac  
Clb 
d 
d    exp  0.5 2 1 
 f   , 1 
H ave
H

H

ave 
ave 


 
 

(2)

(3)

where A and β are experimental constant with values of 4.43 and 1.0, respectively, Clb= 1.2 is the drag coefficient of an obstacle, and κ = 0.4 is the von Karman constant, forbuilding arrays with uniform height.
KA2013 expands the parameterisation further to accommodate variability in buildingheights throughdb c0 X 2  ( a0 P 0  c0 ) X ,
H max

(4a)

where d is the proposed displacement height, X as
   H ave  , 0  X  1.0 ,X   H
 H max (4b)

and a 0 , b0 , and c 0 are the dimensionless regressed constant parameters, i.e., 1.29, 0.36,
and -0.17, respectively. Eqn. 4a also provides displacement height for regularly arrayedcubes at the upper limit of X  1 as,

18

d
d
b

 a0  P 0 ,H max H ave(4c)

The roughness length for momentum, zo, is estimated usingzo b1Y 2  c1Y  a1 ,zo ( mac )(5)

where a1 , b1 and c1 are the dimensionless regressed constant parameters, i.e., 0.7076,
20.2067, and -0.7711, respectively. Y is estimated by  
Y   P H  , 0  Y , H ave (6)which also provides zo for regularly arrayed cubes at lower limit of Y = 0 as,zo a1zo ( mac )(7)

Based from the findings of the large eddy simulations conducted by KA2013 method,the value for their proposed d is slightly larger than MD1998 when height variability within
grids is not considered. Furthermore, neglecting height variability results to a smaller zo (bya factor of a1) in the KA2013 method. Most often, KA2013 results to larger roughness
parameter values when height variability, represented by Hmax and σH, is considered.
Influence of each geometrical parameter can be found in Appendix A.

19

SagamiBay

Pacific Region
KA2013-derived d vs d used in Shimoju et al. (2010)

Figure 2-3 Comparison of zero-plane displacement height used in this study to previously
used distribution

Shimoju et al. (2010) was able to incorporate a distribution of d using the MD1998 into
the WRF model. Comparing with their previously derived distribution, the d used in thisstudy is much larger than previously used, as shown in Figure 2-3. Further, no distributionof zo was considered in WRF simulations in Japan prior to this study.
2.4 Other Urban Parameters
Necessary urban parameters, also mentioned later, are defined briefly below:Anthropogenic heat emission

The additional heat emitted by urban areas due to anthropogenic activity. This can comefrom many sources such as car combustion, air conditioning, heating, and so on. In thispaper, it was estimated from the hourly energy consumption.


Roughness length for heat

20

The aerodynamic heat transport parameter, proportional to roughness length formomentum. This parameter directly influences the heat transfer coefficient used in theurban canopy model.
Sky-view factor

The visible fraction of the sky viewed from a reference point from the ground. Thisparameter influences the amount of radiation trapped within the canopy. Actual skyview factor is difficult to be calculated in WRF since building configurations are notexplicitly resolved. This can be empirically estimated from the λp and λf.
Urban fraction

The fraction of the urban areas within a grid. Unlike the λp where only the buildings are
considered, this parameter considers all anthropogenic elements (e.g. roads andbuildings). The urban fraction was depends on the input land use classification. Within agrid, various land use information (e.g. water bodies, urban, croplands, grasslands,
mixed forests, savanna) are available. The urban fraction can be calculated by dividing
the area assigned urban over the total area of the grid. This parameter determines thepercent contribution of the urban areas to momentum and heat exchange within the grid.
2.5 Chapter Summary
In this chapter, dynamical downscaling and single-layer urban canopy model are
discussed. These background concepts are essential since this study revolves entirely on
their usage and modifications. The roughness parameters were then introduced along withits database construction. Finally, brief descriptions of other essential parameters were
provided.
21

3 Model Description and Improvement

3.1 Weather Research and Forecasting Model
The Weather Research and Forecasting Model (WRF) by Skamarock et al. (2005) is ahighly sophisticated mesoscale model collaboratively designed to meet operational
forecasting and atmospheric needs. The version of WRF used in the lab was initially
improved to accommodate distributed anthropogenic heat emission data. Originally, WRF
has two dynamical cores defining the governing equations formulation, prognostic
variables, horizontal and vertical gridding, terrain formulation, time integration method,and spatial discretization. They are the WRF Nonhydrostatic Mesoscale Model and theAdvanced Weather Research and Forecasting Model (ARW). The difference between two
dynamical cores is not of concern for this study. Due to its wide usage and popularity,
ARW (version 3.3.1) is equivalent to the WRF used in this dissertation. Similar to allmesoscale models, below are some limitations of WRF and the reason why
parameterisations are necessary:

Weather features smaller than the grid size, such as spatial heterogeneity of
meteorological fields, cannot be resolved. Usual grid size is usually larger than 1 km.



The resolution of topography is also dependent on the grid spacing. In other words,
if a grid is selected much larger than Mt. Fuji, the grid will not be able to accurately
resolve the height of Mt. Fuji. This condition also applies to roughness.

Turbulence and individual cumulus clouds cannot be explicitly resolved.

22

WRF Pre-processing System

WRF System

Figure 3-1 Flowchart of WRF simulation

WRF was improved further to accommodate distributed anthropogenic heat emission
which was directly added into the planetary boundary layer scheme. This method had been
employed by in the studies of Shimoju et al. (2010) and Nakano et al. (2013). This versionof WRF, where distributed anthropogenic heat emission is included, will be referred to as
the default version compared in this study.
A basic understanding of the relevant aspects of the WRF model is necessary. Running
WRF has two essential components: the WRF pre-processing software (WPS) system andthe WRF system (Figure 3-1). They are called “systems” because within them are uniquely
sourced executable files interacting with each other. Inside the WPS system, geogrid.exe
processes the static input boundaries (e.g. land use, vegetation fraction, topography) byinterpolating them to the desired resolution and size of domain. The urban fraction is also
calculated in the geogrid.exe. The metgrid.exe combines the output of the geogrid.exe and23

processes the time-dependent global meteorological inputs (e.g. pressure distribution, sea
surface temperature) suitable to the desired resolution and size of domain. The outputs ofthe metgrid.exe, at low time-resolution, will be processed by real.exe as a final step prior torunning the wrf.exe. The individual metgrid.exe outputs will be combined as a time-varying
lateral boundary (only for the parent domain) and surface boundaries for all domains.
Finally, the wrf.exe reads the result from real.exe and simulates the weather based on thedesired physics, dynamics, and boundaries.
Most of the improvements in this study are on the static geographical data and the UCM
physics contained in the WRF system. Section 3.2 talks about the aerodynamic roughness
parameters inclusion as a static input. Section 3.3 talks about necessary changes in the
UCM to optimize the usage of the aerodynamic roughness parameters.
3.2 Incorporating New Aerodynamic Roughness Parameters

Figure 3-2 Estimated displacement height, d (Left), and roughness length for momentum, zo(Right). Water bodies and areas with missing information are filled in white.

24

The following gridded urban parameters with 1 km resolution, which are recommended
for our purpose, can easily be estimated for cities with readily available high-resolution
building morphology data prepared by Makabe et al. (2014).
The parameters listed in section 2.3.2 were used to derive the distribution of d and zo
from eqn. 4c and 5. Static d and zo were estimated for urban areas Central Japan, as shownin Figure 3-2. Compared with the commonly used estimation from MD1998, the updated d
was approximately three times larger at densely built-up areas. The maximum d (74.20 m)
and zo (17.34 m) were found solely in Shinjuku district, which has the most number ofskyscrapers within the 23 wards. Table 4-1 summarises the substituted fixed parameters
used in the urban canopy model for the default WRF model. The derived parameters fromthe were read directly into the urban canopy model, except for d. When applying WRF tourban meteorology, the first atmospheric level as close as possible to the surface ispreferred. Approximately 30 m first atmospheric level height above ground was suitable tojustify a 1.2 km horizontal grid spacing for the second domain. This level is also highenough not to fall within most buildings heights of the selected domain. However, the d,derived from the new aerodynamic parameterisation, for some highly urbanised grids tend
to be larger than 50 m, which may potentially cause some issues when a very low firstatmospheric level used. To avoid this problem, d was added directly to the model
topography. To avoid redundancy of d, the displacement height considered in the urban
canopy model was set to zero. This approach is based on the assumption that within theurban canyon, wind speed and ambient temperature is constant. The validity of thisassumption is discussed later.

25

The derived distribution were written in geogrid binary format (refer to section 3.1) as astatic input. The specific methodology can be found in the WRF user manual or through theonline tutorial,
http://www2.mmm.ucar.edu/wrf/users/docs/user_guide_V3/users_guide_chap3.htm#_W
riting_Static_Data

3.3 Additional Modifications in the Single-Layer Urban Canopy Model
The changes discussed in the succeeding sub-sections were also deemed necessary toaccommodate the bulk roughness parameters. The following improvements were based on
previous researches and have much less impact to the model than the aerodynamic
roughness parameter distribution.
3.3.1 Bulk Transfer Coefficient Revision
The default urban canopy model relies on two zo values, for the roof and the canyon.
Both are calculated from fixed building morphology using the method of Macdonald et al.
(1998). Because the urban parameters were fixed depending on the default buildingclassification, two fixed values were assigned for a homogeneous classification. For
example, in high-density residential areas, zo values of 0.13 m and 0.33 m were used for theroof and canopy, respectively. Consequently, the local bulk transfer coefficients for the roof
and canyon were also calculated separately (Chen et al., 1997).
New aerodynamic parameters were derived to represent the canopy as a whole,disregarding the individual effects of the roof and canyon. For consistency in applying the26

distributed zo, a top-down scheme (Kanda et al., 2005) was employed wherein the surface
layer bulk transfer was estimated and used to calculate, via weighted averaging, the transfer
coefficients for each individual surface (roof, wall, and ground). This scheme is valid for
applications generally focused on the surface layer at the mesoscale.
3.3.2 Modified Urban Fraction
The accuracy of urban fraction in a calculation grid is also critical for simulation
accuracy (Lo et al., 2007). The urban fraction (urb_frc) is related to the resolution of land
use category. By default, urban canopy model is employed for urban dominant grids. In thiscase, the grid will assume three possible urb_frc values, (0.5, 0.9 and 0.95, as specified in
the urban parameter table of WRF), depending on the assigned building classification for
the grid. Excess of the assigned urb_frc (i.e. 1.0 – urb_frc) is automatically assigned
cropland/grassland category and will be handled by the land use model. For non-urban
dominant calculation grids, only the dominant category was considered and other categories
which can be present are disregarded. Each land use category assumes various parametervalues when distribution is not available. A few examples of the parameters used in theland surface models available for each internationally recognised land use category areroughness length of momentum, green vegetation fraction, albedo, leaf area index, andemissivity – the accuracy of these assumptions is beyond the concern of this study.
The simulations conducted in this study use a 100 m resolution 15 category land-use
scheme from the National Land Numerical Information adjusted to match the standard U.S.
Geological Survey land use. Due to its high spatial resolution, determining a more precise
land use fraction, as well as urb_frc, was possible. Thus, calling the urban canopy model
27

subroutine no longer relies mainly on the dominant category but instead on the actual
calculated urb_frc. In other words, grids will use the actual fraction calculated from theland use category and, likewise, proportion the influence of both urban canopy model andland use model in the grid. Since 100 m is not enough to accommodate vegetation within
urban spaces (which is approximately equal to 0.10 according to local government
measurements in Tokyo), an urb_frc upper limit and vegetation fraction (veg_frc) of 0.9
and 0.1 were set, respectively.
In addition to using actual land use fractions, a second dominant land use category
scheme was introduced for grids with urb_frc > 0. The reason behind this was to ensurethat the energy and momentum flux contribution of other land use categories are considered
in a calculation grid. This was achieved considering only the urban category, mostdominant, and second dominant land use categories for each grid. First, the grid isclassified as urban dominant (urb_frc > other land use factors) or non-urban dominant(other land use factors > urb_frc). If a grid is urban dominant, only the area occupied byurban and the area occupied by the next dominant land use category were considered. For
non-urban dominant grids, only the area of the most dominant land-use category and theurban area were considered. The ratio of urb_frc to the sum of the urb_frc and the otherconsidered land use category affects how much the urban canopy model contributes to thesurface fluxes in the grid. The other land use category is handled by the land surface model
as mentioned earlier.

28

0.

0.25

0.5

0.75

1.0

-0.5

-0.25

0.

0.25

0.5

default minus urb_frc (dimensionless)urb_frc (dimensionless)Figure 3-3 The modified urban fraction, urb_frc (Left). The difference between the default
urban fraction (0.90) at urban dominant grids and urb_frc (Right). Grey represents nonurban dominant grids.

There were two advantages to this procedure compared with default WRF. First, theeffect of other land use category in urban dominant grids was considered (Figure 3-3);
likewise, the urban contribution to fluxes from the urb_frc in non-urban dominant grids
was also considered.
3.3.3 Consideration of 3-D Urban Surfaces
To a certain extent, 2-D urban canopy features is inconsistent with the new 3-D surfacederived feedback parameters. As a supplement to using 3-D-generated roughness
parameters, a gridded sky-view factor was also introduced and calculated based on Have, λp,
and λf, using an equation regressed from a highly accurate 3-D scheme (Kanda et al., 2005a).

29

Because the equation assumed infinitely long street canyons, the parameterisation of skyview factor was consistent with the default model’s sky-view factor definition. The skyview factor, from the floor to the sky, determined the amount of total solar radiation
reaching the wall and ground within the canopy. λp is also essential because the direct solar
radiation was evaluated from a weighted average according to the relative area of different
canyons. The diffuse solar and downward longwave radiation values were assumed to be
isotropic.
The algorithm to calculate the sky-view factor was included in the urban module in the
wrf.exe (refer to p. 22). Because of this, λp and λf are necessary static inputs similar to the dand zo. The distributed sky-view factor in Kanto region is shown in Figure 3-4. Large
values, signifying areas with more radiation exchange between atmosphere and ground, areat the suburbs and at reclaimed areas on Tokyo bay. Consequently, small values are foundat the Tokyo metropolitan area, Chiba, and Kanagawa.

Pacific
Ocean
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Figure 3-4 1-km gridded sky-view factor derived for Kanto region following Kanda et al.
(2005). Blue areas have missing or zero values.

30

The differences of the assumed sky-view factors between the proposed WRF model andthe default WRF model for major cities throughout Japan are shown in Figure 3-5. Default
setting tends to overestimate the sky-view factor at the city centers but underestimate it at
the outskirts (Figure 3-5). In spite the widening of urban area being considered at nonurban dominant grids, the over-all effect of sky-view factor improvements is not assignificant to roughness – where a totally new aerodynamic roughness parameterisation is
introduced. The large sky-view factors (> 0.9) shown at non-urban dominant grids suggest
the urban area’s capacity (at non-urban dominant areas) to trap radiation is similar to rural
grids (i.e. has an almost negligible effect). However, differences can be seen at urban
dominant grids. Default assumes a uniform value of 0.48 as a sky-view factor at urban
dominant grids. It results in the underestimation of radiation trapping within the city centerand overestimation at the suburbs. An idealized case is presented in chapter 6 to assess themodel’s sensitivity to the sky-view factor.
A direct comparison between the 2-dimensional assumption and 3-dimensional correctedsky-view factor is available in appendix B (not used in this study).

31

Sendai

Tokyo

Osaka

Nagoya
-0.5

-0.25

0

0.25

0.5

Figure 3-5 Sky-view factor difference between proposed WRF model and default WRF
model. Red shows underestimation of default WRF. Blue shows overestimation of default
WRF. Grey-fill shows non-urban dominant grids with urban fraction greater than 0 (i.e. nourban effect calculated in the default WRF).

3.3.4 Vegetation Effect on Transfer Coefficients
The roughness length for heat is parameterised by default as a function of the roughness
length for momentum. This function utilizes on the Reynolds number, zo. and roughness
length for heat to estimate the transfer coefficient. Kawai et al. (2009) introduced an
updated parameterisation of roughness length for heat, which considers the advection effect,or the enhancement of the transfer coefficients due to vegetation. This updated

32

parameterisation was included into the urban canopy model along with the inclusion of ahigh spatial resolution vegetation fraction.

0

10

20

30

40

50

60

70

80

90

100

Figure 3-6 Input vegetation fraction for August (left) and September (right). Blue areas
have zero or missing values.

A 1 km distribution of vegetation fraction (or greenness fraction in other literatures) was
derived for the year 2011 from the NASA MODIS global vegetation indices data productusing the method of Carlson and Ripley (1997). The vegetation index used was theNormalized Difference Vegetation Index (NDVI) which provided the monthly input ofvegetation fraction. This is an improvement from the low-resolution vegetation fraction
provided in the earlier versions of WRF. The updated vegetation fractions for August and
September are shown. A minimum value of 10% at urban areas was set to accommodate
urban greenery. This value is common for Tokyo urban vegetation not captured in MODIS.

33

3.4 Chapter Summary
This chapter contains the details of the WRF, its flow, limitations, and improvement.
The incorporation of the distribution of the new aerodynamic roughness parameters,
introduced in chapter 2, to the WRF is discussed. The displacement height was added to theground topography while the roughness length for momentum was read directly in theurban canopy model. Additional improvements in the single-layer urban canopy model
were also conducted. These improvements consider other updates to parameters such as
roughness length for heat and sky-view factor. Finally, these improvements were necessary
for successful implementation of distributed parameters.

34

4 Impact of A Distribution of Improved Roughness Parameterisation on a SeaBreeze Simulation

Most of the bulk of this research was on the application of modified WRF model into a
sea breeze circulation on September 14, 2011. A highest spatial resolution of 1.2 km
allowed us to resolve the roughness distribution in detail. Further, the analysis was
conducted for every 5 min interval.
4.1 Numerical Settings
Except for the large domain simulation in the last chapter, all simulations wereconducted using 64 cores of the TSUBAME 2.0 parallelized supercomputing system(Matsuoka, 2007).
Two types of simulations were conducted with three cases for each. The first type covers
a two-month simulation for validation purposed; and the second type is a simulation mainly
focusing on analysing the target sea breeze. The two-month simulation was used to validate
the model before focusing on the sea breeze day. The two-month simulation covers August
1 to September 30, 2011. Outputs were provided at hourly intervals. This was enough forcomparison with observation. The selected months, when the target sea breeze occurred,
represent peak of summer in Central Japan. Summer of 2011 was comprised of squalls,
strong winds, and extreme temperature conditions, occasionally exceeding 38° C,
throughout the country.
For the second simulation, September 14, 2011 was set as the target date with a one day
spin-up period. The spin-up period is typical for WRF applications. Variables were35

simulated at 5 min intervals to understand the sea breeze behaviour in detail. During thetarget day, a sea breeze front was clearly observed from ground stations and satellite cloud
images in Tokyo at around 1100 LST before it converged inland of Tokyo later in theafternoon. This mesoscale wind circulation is common in Central Japan during summer. A
detailed classification of the weather patterns in Central Japan is presented in Khiem et al.
(2010). The topography in Central Japan is relatively flat, with increasing altitude to thenorth and west.

Figure 4-1 WRF Simulation domains and geographical boundaries for September 2011
simulation focused on Kanto Region.

Figure 4-1 and Table 4-1 highlight the domains and the relevant WRF simulation casesettings, respectively. These settings were used by the two types of simulation mentioned

36

earlier. The distribution of roughness parameters prepared was interpolated to match themodel grid locations via nearest neighbour scheme. Three simulation cases were conductedin this study. All cases use two-way nesting, the same physics settings and meteorologicalboundaries. In the control case, the default WRF Version 3.3.1, was used. This case isnamed CTL. The case using newly modified WRF, which considers all improvements
mentioned in p. 26, is named NEW. Both the CTL and NEW also included diurnal
anthropogenic heat and vapour emissions based on the method of Moriwaki et al. (2008) tolimit urban effect to aerodynamic roughness. The anthropogenic emissions were released at
the first atmospheric level without being involved in the urban canopy model. The
supplementary case refers to the vegetation case in which the default WRF was used withall urban grids revised to the grassland category. A vegetation case, named VEG, wasnecessary to clarify the urban effects with respect to both the default and modified WRF
version.
All cases use the same meteorological boundaries and physics options. Japan
Meteorological Agency’s 3-hr Nonhydrostatic Mesoscale Model with 0.1 by 0.125 degrees
spatial resolution for the atmospheric level and 0.05 by 0.635 degrees resolution for thesurface level was used. Lack of input skin temperature, soil temperature and moisture wascompensated by the 6-hr National Centre for Environmental Prediction FNL Operational
Model Global Tropospheric Analyses. 1-km resolution MODIS vegetation fraction and
MODIS Sea Surface Temperature was used. New Thompson et al. Scheme was used as
microphysics. Rapid Radiative Transfer Model was used for the longwave radiation physics.

37

Table 4-1 WRF Numerical Parameters
Parameters
Case IDWRV Version
Roughness Lengths
Displacement Height
Urban Fraction
Plane Area Index

CASES
CTL
NEW
Default WRF Version 3.3.1 Modified WRF Version 3.3.1
Fixed (0.33 m) UCM* default Distributed zo
Fixed (5.71 m) UCM default Distributed d
Fixed (0.90)
Modified Urban Fraction
Fixed (0.5) UCM default
Distributed PAI

Frontal Area Index
Sky View Factor
ParametersID
spatial resolution
east-west grid no.
south-north grid no.
metgrid vertical levels
wrfinput vertical levels

Fixed (0.4) UCM default
Distributed FAI
Fixed (from PAI and FAI)
Distributed (from PAI and FAI)
DOMAINS
Domain 1
Domain 2
4.8 km
1.2 km
171 grids
201 grids
171 grids
181 grids
17 levs17 levs28 levs28 levs
1, 0.993, 0.983, 0.970, 0.954, 0.934, 0.909, 0.880, 0.837, 0.795,
Full eta levels
0.752, 0.709, 0.633, 0.563, 0.499, 0.439, 0.384, 0.334, 0.288,
0.246, 0.207, 0.172, 0.139, 0.110, 0.083, 0.059, 0.037, 0.018, 0.
* UCM stands for the single-layer urban canopy model incorporated in WRF

VEG
Default WRF withall urban land use
category replaced
to grassland(no UCM)

Goddard shortwave was used for the shortwave radiation. MM5 similarity was used forthe surface layer. Noah Land Surface Model was used for the land surface. Single-layerurban canopy model was used for the urban surface. Mellor-Yamada Nakanishi and Niino
Level 2.5 was used for the planetary boundary layer, and; Kain-Fritsch scheme was used
for the cumulus parameterisation.
4.2 Model Validation
The focused meteorological parameters are wind speed, wind direction, and temperature.
When discussing wind direction, show the differences in the development of the ambient
winds, sea breeze wind direction in Sagami bay and Tokyo bay.

38

All cases were compared with observations from JMA Automated Meteorological Data
Acquisition System (AMeDAS) stations using a separate 2-month simulation covering thesummer period of August 1 to September 30, 2011. Hourly outputs were generated. The
observation points selected for validation are shown in Figure 4-2. The selection of theobservation gauges was based on the data availability and the path of the sea breeze comingfrom Sagami bay and Tokyo bay. To understand the effect of the aerodynamic roughness
parameters, most of the points were selected in highly built up environments. Indices of U
represents urbanised areas with urban fraction = 0.90. R1, R2, R3, and R4, corresponding toslightly urbanised grids, correspond to urban fraction of 0.24, 0.73, 0.22, and 0.55,
respectively. U1 was located in a densely built-up district of Tokyo, and was directly along
the path of the sea breeze from Tokyo Bay. U2 was located in a residential area within
Tokyo. U3 and U4 were located in a moderately built-up area and a residential area,
respectively, with both locations being away from the direct path of the target sea breeze.
R1 was southwest of U1 and moderately urbanized, and it was located on the coastline
directly along the path of the sea breeze from Sagami Bay. R2 was also a residential area a

39

R4

R3

U2
U1

R1

U3

R2

SagamiBay

U4

Pacific
Ocean

Figure 4-2 JMA observation points overlaid on model ground topography (shown in light
contour lines with units = m). Topography contour starts from 100 m at 200 m intervals.

few kilometres inland from the coastline. R3 and R4 were both located downwind ofTokyo, and were characterized by vegetation, residential houses, and a few low-risebuildings. The relevant information is summarized in Table 4-2.
The meteorological parameters investigated were near-ground wind speed and nearsurface air temperature. Simulated temperatures used for comparison were the firstatmospheric-level temperature (Ta) and the temperature at 2 m (T2). The height of the firstatmospheric level was around 30 m above ground level (a.g.l.). AMeDAS surface
temperature observations were conducted at 1.5–2 m a.g.l., allowing direct comparison withthe simulated T2. The height of wind-speed observations differed40

Table 4-2 Observation gauges information (refer to Figure 4-2 for the location)
ID
U1
U2
U3
U4
R1
R2
R3
R4

urb_frc
0.90
0.90
0.90
0.90
0.24
0.73
0.22
0.55

zo
7.01
1.01
1.24
0.58
0.70
0.66
0.58
0.58

Land Use
URBAN
URBAN
URBAN
URBAN
CROPLANDURBANCROPLANDURBAN

Height
35.1
11.3
47.9
21.8
6.5
19.8
10
16.5

from the observation height for temperature and varied with location. Furthermore, theavailable simulated wind-speed outputs were diagnostic of the 10-m wind speed and firstatmospheric wind. The first atmospheric wind was compared with a height-adjusted
observation.
For consistency, a simple height adjustment for the observed wind-speed values was
conducted based on the logarithmic law, except for U1 and U2. Assuming Hmax as the
canopy layer height, the observation height and the first atmospheric level of U1 and U2 lie
within the urban canopy layer. KA2013 indicated that wind speeds were homogeneous
within the canopy layer, and therefore, the observed wind speed can be assumed to be thewind speed at the first atmospheric level (i.e., no height adjustment necessary) for U1 andU2.
A statistical summary showing the accuracy of the cases can be seen in Table 4-3 andTable 4-4. Root mean square errors (RMSE) and biases were calculated for each selected
point, comprising 705 hourly simulations and sets of observed data per station. Separate
analyses were also conducted for two diurnal periods: day (D), 0500 to 1700 LST; and41

night (N), 1800 to 0400 LST. Observation and simulation days without precipitation wereused for analysis.
4.2.1 Near-ground wind speed
As shown in Table 4-3, the over-all performance of the modified WRF case insimulating wind speed was better than that of default and vegetation cases. The lowest
RMSE was observed for the NEW in most cases, mainly due to the daytime overestimationof wind speed by both the CTL and VEG (-0.91–3.17 m s-1 daytime and 0.15–3.80 m s-1night time bias). Although slightly better performance was found for the CTL for R1 during
daytime, with all cases showing negative daytime biases, the differences in RMSE were notvery significant compared with other selected points. For less urbanised grids such as U4,
the difference in RMSE between the NEW and CTL was reduced, with both still
performing better than the VEG. U4 was the southernmost urban grid. Although most of
the errors in wind speed occurred during night time due to the influence of other input
conditions and model parameters not covered in this study, the NEW still produced better
results for wind speed. The reason behind the improved mechanism for wind-speed
simulation is discussed later.

42

Table 4-3 Statistical analysis of height-adjusted wind speed at selected AMeDAS stations(D = Day; N = Night)
Station
Simulated Observed
Simulated Observed RMSE Bias Simulated Observed RMSE Bias
Case
RMSE Bias
ID
Mean
Mean
Mean (D) Mean(D) (D) Mean (N) Mean(N) (N)
NEW
2.30
2.95
1.32 -0.65
2.43
3.02
1.30 -0.59
2.15
2.87
1.34 -0.72
U1
CNTL
4.66
2.95
2.45 1.71
4.79
3.02
2.54 1.77
4.50
2.87
2.34 1.63
VEGE
4.22
2.95
2.21 1.27
4.42
3.02
2.39 1.40
3.97
2.87
1.98 1.10
NEW
3.19
0.66
3.05 2.53
3.14
0.82
2.97 2.32
3.26
0.47
3.14 2.78
U2
CNTL
4.12
0.66
4.04 3.46
3.98
0.82
3.89 3.17
4.27
0.47
4.22 3.80
VEGE
3.81
0.66
3.75 3.15
3.90
0.82
3.82 3.08
3.71
0.47
3.67 3.24
NEW
3.54
3.15
1.95 0.38
3.66
3.17
1.93 0.49
3.39
3.13
1.97 0.26
U3
CNTL
4.40
3.15
2.47 1.24
4.40
3.17
2.40 1.23
4.40
3.13
2.54 1.27
VEGE
4.12
3.15
2.37 0.96
4.20
3.17
2.40 1.02
4.03
3.13
2.34 0.90
NEW
4.24
3.43
2.04 0.81
4.41
4.01
1.80 0.40
4.03
2.74
2.29 1.29
U4
CNTL
4.30
3.43
2.08 0.87
4.48
4.01
1.85 0.47
4.09
2.74
2.33 1.35
VEGE
4.33
3.43
2.14 0.90
4.47
4.01
1.89 0.46
4.15
2.74
2.41 1.41
NEW
3.93
4.93
2.41 -1.00
4.18
5.66
2.58 -1.48
3.63
4.07
2.20 -0.44
R1
CNTL
4.73
4.93
2.35 -0.20
4.89
5.66
2.29 -0.76
4.54
4.07
2.42 0.47
VEGE
4.51
4.93
2.45 -0.42
4.74
5.66
2.46 -0.91
4.22
4.07
2.43 0.15
NEW
4.11
3.83
1.78 0.28
4.34
3.97
1.82 0.37
3.83
3.66
1.74 0.17
R2
CNTL
5.02
3.83
2.32 1.19
5.09
3.97
2.28 1.13
4.93
3.66
2.35 1.26
VEGE
4.76
3.83
2.30 0.93
4.89
3.97
2.35 0.92
4.61
3.66
2.24 0.95
NEW
3.05
2.94
1.66 0.12
2.93
3.18
1.68 -0.25
3.20
2.64
1.62 0.55
R3
CNTL
3.80
2.94
2.09 0.86
3.55
3.18
1.91 0.37
4.09
2.64
2.30 1.45
VEGE
3.49
2.94
1.90 0.56
3.51
3.18
1.89 0.33
3.47
2.64
1.91 0.83
NEW
2.66
2.61
1.49 0.05
2.57
2.75
1.48 -0.18
2.76
2.44
1.50 0.32
R4
CNTL
3.14
2.61
1.72 0.53
2.92
2.75
1.55 0.17
3.39
2.44
1.91 0.95
VEGE
3.14
2.61
1.88 0.53
3.01
2.75
1.81 0.26
3.29
2.44
1.95 0.85

4.2.2 Near-surface Temperature
Although the modified model predicts wind speed well, a difficulty remains insimulating temperature. A direct statistical comparison between the simulated Ta and theAMeDAS observed T2 was conducted. The method was done to provide a quick-look forthe simulated and observed temperatures, and to minimise the uncertainties of thediagnostically calculated T2, explained in the succeeding statements. Direct comparison
shows that the NEW performs well compared with CTL in simulating temperature. Earlier
sensitivity tests indicated that an improvement in temperature was found whenanthropogenic heat emission was simulated with the default WRF. When CTL is simulated
without anthropogenic heating, Ta simulated from the NEW has the lowest RMSE

43

throughout. However, when the diagnostic T2 was used for comparison with the actual T2(Table 4-4), the NEW was found to perform worse than the CTL but better than VEG.
It is difficult to provide the exact reason for the NEW’s T2 mismatch with the observed
T2, but the most plausible reason lies in the differences between the measured local
condition from the AMeDAS station and the assumed urban condition by the urban canopy
model. The AMeDAS stations are situated in areas of green space within cities, such as thegauge for U1 (gauge surroundings shown in Fujibe, 2011). The canopy, as defined by the
new feedback parameters for urban areas, suggests limited vegetation and green space.
Thus, temperature measurements within the canopy do not represent the wider area
(Sugawara et al., 2004) or the WRF second-nest grid resolution and spatial variability (e.g.,
the urban and vegetation fraction, detailed building configuration). This results in variable
heat flux, and temperature complicates the determination of a reliable measurement
location. In an experiment with moving measurements conducted in one of the hottest cities
in Japan (Nakayoshi et al., in preparation), it was found that the surrounding T2 canfluctuate by approximately equal to 3°C higher than the stationary AMeDAS T2 dependingon the urban texture and morphology. Urban-wide T2 tends to exceed gauge-observed T2,
supporting the positive bias of NEW against the negative biases observed in CTL and VEG,
for both day and night. The NEW sometimes performed better during the evening (U1, U3,
R1, and R3) when radiative cooling caused the NEW T2 to fall closer to the observed values(Table 4-4).

44

Table 4-4 Statistical analysis of the 2-m temperature at selected AMeDAS stations (D =Day; N = Night)
Station
Simulated Observed
Simulated Observed RMSE Bias Simulated Observed RMSE Bias
Case
RMSE Bias
ID
Mean
Mean
Mean (D) Mean(D) (D) Mean (N) Mean(N) (N)
NEW
27.21
26.77
1.55 0.44
28.18
27.43
1.73 0.75
26.06
26.00
1.30 0.06
U1
CNTL
27.02
26.77
1.50 0.24
28.05
27.43
1.64 0.62
25.80
26.00
1.33 -0.20
VEGE
25.18
26.77
2.24 -1.59
26.46
27.43
1.87 -0.97
23.68
26.00
2.61 -2.32
NEW
27.14
26.72
1.78 0.42
28.49
27.81
1.88 0.67
25.54
25.43
1.66 0.11
U2
CNTL
26.73
26.72
1.55 0.01
27.89
27.81
1.59 0.08
25.35
25.43
1.50 -0.07
VEGE
24.73
26.72
2.62 -1.99
26.26
27.81
2.31 -1.55
22.91
25.43
2.94 -2.51
NEW
26.60
26.35
1.86 0.26
27.86
27.23
1.95 0.63
25.11
25.30
1.75 -0.19
U3
CNTL
25.82
26.35
1.74 -0.53 26.89
27.23
1.71 -0.34 24.55
25.30
1.78 -0.75
VEGE
24.63
26.35
2.45 -1.72
25.99
27.23
2.10 -1.25
23.02
25.30
2.81 -2.28
NEW
26.15
25.45
1.79 0.70
27.50
26.83
1.67 0.67
24.56
23.82
1.92 0.74
U4
CNTL
25.28
25.45
1.55 -0.17 26.36
26.83
1.48 -0.46 24.00
23.82
1.64 0.19
VEGE
24.62
25.45
1.75 -0.83
25.78
26.83
1.83 -1.05
23.24
23.82
1.66 -0.58
NEW
26.03
25.65
1.73 0.38
27.45
26.98
1.75 0.47
24.34
24.07
1.71 0.27
R1
CNTL
25.58
25.65
1.74 -0.07 26.90
26.98
1.76 -0.09 24.03
24.07
1.72 -0.04
VEGE
24.72
25.65
1.95 -0.92
26.06
26.98
1.98 -0.93
23.15
24.07
1.91 -0.92
NEW
26.98
26.36
1.71 0.63
28.19
27.32
1.84 0.87
25.55
25.22
1.55 0.33
R2
CNTL
26.48
26.36
1.43 0.12
27.40
27.32
1.48 0.08
25.39
25.22
1.36 0.17
VEGE
25.07
26.36
1.95 -1.29
26.14
27.32
1.97 -1.18
23.80
25.22
1.94 -1.42
NEW
26.51
26.17
1.82 0.34
28.10
27.36
1.94 0.74
24.62
24.76
1.65 -0.14
R3
CNTL
25.94
26.17
1.67 -0.23 27.43
27.36
1.64 0.07
24.17
24.76
1.70 -0.59
VEGE
24.95
26.17
2.10 -1.22
26.48
27.36
1.94 -0.88
23.14
24.76
2.29 -1.61
NEW
26.51
26.31
1.89 0.20
28.28
27.63
1.87 0.65
24.42
24.75
1.92 -0.33
R4
CNTL
26.22
26.31
1.72 -0.09 27.54
27.63
1.69 -0.10 24.66
24.75
1.75 -0.09
VEGE
24.70
26.31
2.47 -1.61
26.34
27.63
2.25 -1.29
22.76
24.75
2.71 -1.99

Previous studies have also mentioned spatial variability in temperature. Similardifficulties in assigning representative temperature values for urban grids were also foundin a study of an urban heat island in Seoul, South Korea (Kim et al., 2005). In that study,
appreciable temperature variations within Seoul were found, and the gauge with thewarmest temperature was selected because its location matched typical urban
characteristics. In a 1740 x 750-m area in Singapore, temperature was found to varyspatially by 37–41°C (Tso, 1996). Williams et al. (2000) also noted differences between
measurement points and suggested that 1° × 1° areal averaged measurements can be partly
ascribed to the difference between point measurements and spatially integrated values.
Thus, the representativeness of point measurements remains a challenge, especially for
urban climate studies.

45

SDLC
NEW

CNTL
CTL

0000 LST

09/13

0000 LST

09/140000 LST

09/15

0000 LST

10
9
8
7
6
5
4
3
2
1
0
09/17
09/12

JMA
JMA

VEGE
VEG

Wind Speed (m s^-1)

10
9
8
7
6
5
4
3
2
1
0
09/12

09/16

0000 LST

0000 LST 0000 LST

SDLC
NEW

09/13

0000 LST

CNTL
CTL

09/14

0000 LST

JMA
JMA

VEGE
VEG

09/15

09/16

0000 LST

0000 LST

09/170000 LST

Month/Day LST (=UTC+0900)

Temperature (deg C)

Figure 4-3 Five-day trend of hourly height-adjusted wind speed at U1 (left) and R2 (right)

35
33
31
29
27
25
23
21
19
17
15
09/12

0000 LST

NEW
SDLC
09/13

0000 LST

35
33
31
29
27
25
23
21
19
17
JMA
VEG
CTL
CNTL
VEGE
JMA
15
09/14
09/15
09/16
09/17 09/12
0000 LST

0000 LST

0000 LST

0000 LST

0000 LST

NEW
SDLC
09/13

0000 LST

VEG
VEGE

CTL
CNTL
09/14

0000 LST

09/15

0000 LST

09/16

0000 LST

JMA
JMA
09/17

0000 LST

Month/Day LST (=UTC+0900)

Figure 4-4 Five-day trend of hourly 2-m temperature at U1 (left) and R2 (right)

The succeeding section focuses on the sea breeze event that occurred on September 14,
2011. Figure 4-3 and Figure 4-4 show the trend of wind speed and T2 during September 12–
17, 2011, from the two-month simulation. The synoptic condition during the dates selected
reveals a prevailing high pressure system very close to the eastern side of Japan. A
stationary front exists at the northernmost part of Japan. These period marks the beginningof transition from summer to autumn. During this period, NEW was found to perform wellwhen simulating higher wind speed and temperatures than the other cases. Wind speed was46

largely overestimated by both CTL and VEGE with a slight underestimation from the NEW.
During night time, all cases underestimate the temperature in spite generally no little bias
was found during night-time from the statistics shown in Table 4-4. The intensity andcommonality of the large underestimations during night-time were probably brought about
by other factors aside from the roughness parameters. In spite this, NEW simulated
temperature approaches the observed temperature at night.
4.3 Results and Discussion
4.3.1 Sea Breeze Analysis from Clouds

Figure 4-5 September 14, 2011, 1600 LST (UTC = LST – 9 hours) Rapidscan geostationary
satellite image. White patches correspond to clouds. Sea-breeze fronts are shown with redlines. Dashed blue green lines indicate densely built-up regions with d > 10 m. Solid blue
green line indicates location of a common cloud street.

47

Figure 4-6 September 14, 2011, total column cloud mixing ratio from NEW (left), CTL(centre), VEG (right). Red lines represent Rapidscan geostationary satellite cloud outlines
drawn from Figure 4-5. Blue lines represent the sea breeze front from the first atmosphericlevel convergence. Dashed black lines indicate regions with d > 10 m. Red dots mark thelocation of U1 and R3 (labelled in green). Dashed light blue lines indicate the location of acommon cloud street.

48

An approaching sea breeze can sometimes be seen from the motion of clouds during
clear sunny days (Damato et al., 2003; Corpetti and Planchon, 2011; Inoue and Kimura,
2004; Kanda et al., 2001). Viewed from a high-resolution geostationary satellite(Rapidscan), the sea breeze front can be depicted as a boundary between cloudy and clear
regions, such as that shown in Figure 4-5. At 1100 LST on September 14, 2011, clouds
start progressing from the coastline in the direction inland, labelled R3. The wedge-shaped
cloud line suggests two sea breeze sources, Tokyo Bay and Sagami Bay. Locally known as
Kanpachi clouds (Kanda et al., 2001), a distinct cloud street occasionally forms andelongates at a distance from U1 to R3. The cloud street which was not generated on the dateof this simulation is commonly generated by the convergence of two sea breezes, shown inblue lines in Figure 4-6. Later in the afternoon, clouds converged above the region
surrounding R3 and dissipated before sunset. For convenience, the boundaries representing
the fronts were carefully hand-drawn in red for comparison with the WRF.
Clouds were resolved well in WRF when the JMA Nonhydrostatic Mesoscale Model(MSM) was used to determine the boundary (Figure 4-6), especially in the region
surrounding U1, U2, R3 and R4. When the spatial distribution of the total column cloudmixing ratios was compared with Rapidscan images, the observed clouds, which were
mostly concentrated at 2 km above mean sea level (AMSL), matched the simulation well,with slight differences depending on each case. Based on the overlain snapshots of theconvergence in the first atmospheric level, it can be deduced that the cloud distribution was
substantially influenced by the inflow of sea breeze. Of the three cases, the VEG simulated
the least cloud and predicted that the clouds would move faster than was observed.

49

Considering only the cloud motion and dissipation, the sea breeze front appeared earliest in
the VEG case. The NEW was similar to the CTL with regards to improving cloudformation. However, in the CTL, there was an earlier advancement by a few km along thepath of the sea breeze coming from Tokyo Bay and an earlier dissipation on 1600 LST. On
1500 LST, cloud formations at the south of R3 deviate a bit farther north of the red line for
the CTL. Further, clouds along the sea breeze of Sagami Bay are also slightly delayed andformed a bit south of the red line. Meanwhile on 1600 LST, the NEW also shows slightly
better representation of clouds west of R3 with a shorter gap existing between the clouds
north of R3. The improved roughness representation in Tokyo has the most dominant effectin delaying cloud advection by sea breezes. The current findings suggest that urban forcing
and parameterisation in mesoscale models are quite significant for determining theaccuracy of cloud distribution.

50

4.3.2 Near-ground Winds

Height AMSL (m)Height AMSL (m)

3000
3000
b
a
2500
2500
2000
2000
1500
1500
1000
1000
3000
500
500
U1
U1
0
0
2500
0d1 2 3 4 5 6 7 8 93000 0 c1 2 3 4 5 6 7 8 3000
9
2000
2500
2500
1500
2000
2000
1000
1500
1500
500
1000
1000
500
0
500
R2
R2
0
1
20 3 4 5 6 7 80
0 1 2 3 4 5 6 7 8 9
0 1 2 3 4 5 6 7 8 9Wind speed (m s-1)
SDLC
CNTL
NEW
CTL

VEGE
VEG

Figure 4-7 September 14, 2011, WRF hourly-averaged horizontal wind-speed verticalprofiles above U1 on 1200 LST (a) and 1500 LST (b); and above R1 on 1200 LST (c) and1500 LST (d)The new aerodynamic parameters dramatically reduced the simulated wind speed,
especially in highly urbanised areas. Around noontime, urban heat island increased thethermal low throughout Central Japan. Thus, faster wind speeds for the CTL can be seen in
Figure 4-7 for both U1 and R1 in Figure 4-8. However, the significant roughness in U1
reduces winds at heights of up to 500 m. Despite the faster trailing winds behind the front,
surface wind speed was slowest in the NEW at 1500 LST. At R1, with a lower d and zo
compared with U1, the consideration of the urban fraction at predominantly non-urban
grids caused the NEW to simulate faster near-surface wind speeds around noontime. At
1500 LST, the drag effect was apparent, as the CTL exceeded the wind speed in the NEW.
51

Wind speed differences which can also be seen at upper levels are caused not only due tothe aerodynamic effect of the roughness parameters but also possibly due to the differencesin the heat being released into the atmosphere by each case and to large scale effects (e.g.
two-way nesting). Even if only the surface roughness is varied between NEW and CTL, toachieve an equivalent wind speed simulation at the upper levels is impossible because real
WRF simulations involves various 3-dimensional processes and is non-linear.

Figure 4-8 September 14, 2011, wind observed at 25 m (left), NEW 10-m wind (centre),
NEW first atmospheric-level wind (right). Darker wind vectors have wind speed >= 5 m/s.
Dashed red lines indicate regions with d > 10 m. Green dots represent AMeDAS stations.
Dashed light blue lines indicate the location of a common cloud street.

Surface drag in urban areas was also captured by the Atmospheric Environmental
Regional Observation System (AEROS) of the National Institute for Environmental Studies,Japan (NIES), as shown in the 1600 LST snapshot shown in Figure 4-8. The threshold ofwind vector visibility was set based on the most convenient plot to analyse the sea breeze
front and differentiate the cases. The drag depicted in NEW matched the observations,despite the height differences.

52

Figure 4-9 September 14, 2011, wind simulated by WRF at 10-m height (darker windvectors >= 4 m/s) and first atmospheric-level convergence (blue lines) for NEW (left), CTL(centre), and VEG (right). Dashed red lines indicate regions with d > 10 m. Green dots
represent AMeDAS stations.

53

Figure 4-10 Continuation of Figure 4-9

The time-varying wind-speed distribution in Figure 4-9 and Figure 4-10 shows thedelineated convergence lines denoting two sea breeze flows passing above the region
surrounding R1 and R2 and the region surrounding U1. One flow arrived from Sagami Bay,and the other was from Tokyo Bay. Near the surface, the front from Sagami Bay advanced
faster and was stronger than the front coming from the Tokyo Bay. The buildings of Tokyoand Yokohama, close to R2, obstructed the flow of the sea breeze from Tokyo Bay, making
it less visible from the wind-speed distribution map (NEW). Using default roughness
parameters, wind speeds were simulated to be faster, but noticeable drag was observed

54when no urban areas were assumed. At 1400 LST, the sea breeze front from Sagami Bay
advanced earlier for the NEW, followed by the CTL and VEG, due to the higher land–sea
pressure gradient. The Sagami Bay sea breeze appeared to weaken near the coastline in the
NEW due to the combined effect of the roughness at the coastline facing Sagami Bay andthe western slope. The sea breeze from Tokyo Bay easily penetrated inland at 1400 LST in
the VEG, showing the potential of the sea breeze when no urban obstructions wereconsidered. After 1 hour, the wind speeds passing above Tokyo remained to be weak in theNEW. With the passage of the front above the urban canopy layer at 1500 LST, faster
trailing winds could be seen at the CTL that were not observed in the NEW. By 1600 LST,
the winds had increased in intensity in between To1 and C1. The urban heat island
circulation in Tokyo, explained further in the next section, contributed to the horizontal
wind reduction in the Tokyo Bay’s sea breeze’s wake (1600 LST for the NEW). When theeasterly sea breeze from the Pacific Ocean assisted the convergence later in the afternoon,
surface winds remained weak in areas with large roughness parameters and at theconvergence line of the two sea breezes.
4.3.3 Sea Breeze Convergence Inland of Tokyo
Three distinct convergence/divergence regions were found to exist in the late afternoon
of September 14, 2011, as shown in Figure 4-11: Region 1 – a convergence/divergenceregion above the inland of Tokyo; Region 2 – an eastward moving convergence line above
Tokyo generated by sea breeze flows coming from Tokyo Bay and Sagami Bay; andRegion 3 – an elongated convergence region east of Tokyo. Regions 1 and 2 (bounded bydashed boxes) were substantially influenced by the sea breeze and the urban parameters.
55

Region 3 was generated by the convergence of sea breeze from Tokyo Bay and the easterly
sea breeze. Only the mechanism of regions 1 and 2 are discussed here due to their directinteraction with the urban areas.

Figure 4-11 September 14, 2011, 1600 LST divergence of first atmospheric winds for NEW(left), CTL (centre), and VEG (right) at 1600 LST. Regions bounded by dashed lines
represent the analysed convergence zones. The dashed line represents the location of thevertical slice used in Figure 4-12

The strong convergent/divergent region occurring inland of Tokyo later in the afternoon
was caused by the incoming sea breezes from Tokyo Bay and Sagami Bay, and the easterly
sea breeze (Region 1 in Figure 4-11). The convergence branches were brought about by the
combination of in-land penetration of sea breeze and turbulent mixing. Ahead of the fronts(NEW and CTL), the horizontal winds were weaker and slightly more dispersed (divergent
cells). Inland of Tokyo is also a region where advected heat is deposited, especially during
summer, when sea breezes could not extend farther inland due to the surface frictional
stress (Sato et al., 2008). The slight differences in CTL and VEG to the convergence inland

56

can maybe affect the understanding of common extreme events in that region (e.g. heatstroke and localized heavy rainfall). When new aerodynamic roughness parameters wereintroduced, differences in the surrounding convergence could be seen due to the intensity of
the approaching sea breeze front. The CTL had the strongest first-level convergence at thefront (notice the darker blue contours compared to other cases), with wider divergent cells
leeward. NEW simulated leeward divergent cells with a smaller diameter and slightly
weaker convergence. In a test run, during which distributed anthropogenic heat emission
was removed in the CTL, the region of convergence receded westward; thus, urban heating
contributed to the delay of sea breeze penetration to the inland.
Divergent cells may be generated possibly by two mechanisms. First, weak wind regionsthat are also influenced by the heat island circulation and ambient winds may form ahead of
the front due to the balance of vertical mixing and the horizontal pressure gradient (fromidealised test simulations of Ohashi et al., 2002). On September 14, 2011, the ambient windcondition in the late afternoon is a prevailing south-easterly wind at the rate of
approximately 2 m/s. The heat island circulation is described here as the strong verticalmotion found at the centre of the study domain due to strong surface heating similar to
Yoshikado et al. (1989) findings. The weak wind together with the displacement of airparcels above may cause subsidence and downward adiabatic transport enhancing
divergence near the surface. However, results show that surface temperatures were slightly
reduced close to the surface suggesting a different mechanism that idealised simulations
failed to uncover. The better and second mechanism is due to the clouds generated by thefront (refer to 1600 LST in Figure 4-6 for the cloud image corresponding to that of Figure57

4-11). At the front, strong updrafts lift moist air upward, continually generating clouds
above the boundary layer. These clouds are advected ahead of the front and above theweak-wind region due to the faster ambient wind speed above the boundary layer. As aresult, shortwave radiation levels fall, blowing surface winds apart. Divergence underneathclouds was also simulated in the study by Salamanca et al. (2011). The presence of cloudcontributed significantly to the simulated divergent patches found above the inland ofTokyo during this period.
4.3.4 Atmospheric Boundary Layer Structure Above Convergence Region 2

Figure 4-12 Vertical cross-section for September 14, 2011, 1600 LST (slice location shownin Figure 4-11) vertical wind (contour), wind velocity (vectors) calculated from west-toeast wind component and vertical wind component (enhanced by 2.5), and planetary
boundary-layer height (solid lines)

A convergence region was found (Region 2 in Figure 4-11) extending from theconvergence region 1. Although it was considerably weaker in intensity than inland ofTokyo, this region earlier extended all the way to the Miura Peninsula (see location on theleft-most image of Figure 4-11) and then later receded slowly in the direction of Tokyo.
Bounded by the smaller dashed box in the divergence snapshot in Figure 4-11, two distinct
58

blue elongated cells stretching from Tokyo to the inland represent the convergence line.
Above this region of convergence, the top of the boundary layer peaked (cross sections inFigure 4-12), with strong updrafts underneath. The mechanism of the elongatedconvergence region and its features varied for each case.
The NEW had the highest simulated planetary boundary-layer height (PBLH), followedby the CTL and VEG. The maximum PBLH simulated by NEW and CTL (> 1 km) are
typical during hot days with sea breeze (Yoshikado et al., 1989). The locations withobvious PBLH peaks were situated close to each other; the leftmost (higher) moving
eastward (hereinafter called Lpeak) and to the right (lower) remaining directly above Tokyo(hereinafter called Rpeak). Westward of the selected cross-section is characterised by lessurbanisation. While easternmost is located near the coastline explaining the low PBLH.
Within the two peaks, high vertical wind speeds (approximately equal to 0.2-0.5 m s-1)
were visible. The peaks varied depending on the urban parameters included. The VEG casesimulated only the Lpeak. This could mean that the Rpeak only represents the urban heatisland circulation, mentioned in p. 55, while the Lpeak represents the area where sea
breezes coming from Tokyo Bay and Sagami Bay converged. Roughness parameters play
an important role in intensifying turbulent mixing as manifested by the difference in peakheight and wind-speed intensities between the NEW and CTL. With the increasing d and zo
from the southwest region to Tokyo, a transition from rough to rougher surfaces occurred.
This strengthened horizontal convergence and upward motion. The Lpeak increased byapproximately equal to 100 m and the Rpeak increased by approximately equal to 200 m.

59This is the reason why the convergence line appears to be wider in the NEW than in the
CTL.
The investigation of horizontal and vertical winds, along with their convergence,
provides a clearer picture of how a sea breeze interacts with the highly urbanised area ofTokyo. The earlier development of the land-sea pressure gradient in the NEW, due toadditional heating near the surface, caused the sea breeze front to pass the Tokyo Bay
shoreline earlier. The densely built-up, very rough, buildings of Tokyo then tended to shift
the sea breeze wind upwards, increasing the boundary-layer height. Alongside this, theTokyo Bay’s sea breeze also appeared to move around the area with the aid of the Sagami
Bay’s sea breeze and the easterly sea breeze, which caused the extended convergenceregion leeward of Tokyo. With relatively rougher areas concentrated near the coastline, thesea breeze lost its momentum due to friction, resulting in the stagnation of a weakened sea
breeze inland of Tokyo.
4.4 Chapter Summary
In this chapter, the updated WRF model, introduced in chapter 3, are now evaluated andapplied. A comparison between the modified model and its default version, which uses
uniform roughness parameters within a conventional method, was conducted for a 2-month
simulation during summer. Results showed a significant improvement in the simulation of
surface wind speeds but more improvements were still necessary for temperature accuracy.
A sea-breeze event was then selected from the 2-month simulation. A separate simulation
was conducted for the selected event to understand the atmospheric effects of actual
building roughness of Tokyo. Sea-breeze penetration weakened and was more delayed at60

urbanised areas in the updated model but not in the default model. Furthermore, the slow
sea-breeze penetration also lessened heat advection downwind allowing stronger turbulent
mixing and deeper boundary layer above urban areas. Horizontal wind speed reduction, due
to the increased urban surface drag, reached several hundreds of metres above through thestrong convection.

61

5 Improved WRF Model Application at Major Cities in Japan

ParentDomain
40 N

Sea
of
Japan

38 N

Tokyo
36 N

NagoyaOsaka

Pacific
Ocean

34 N

32 N

134 E

136 E

138 E

140 E

142 E

144 E

146 E

Figure 5-1 Domain coverage of WRF simulation over major cities in Honshu, Japan. Red
boxes cover child domains.

After testing the model in Kanto for the summer of 2011 in the previous chapter, theapplicability of the updated model at other cities and different study period are addressed.
The impact of a nation-wide surface boundary distributed input prepared by Makabe et al.
(2014) was initially investigated. From the ESRI Japan’s 2012 detailed GIS, 1 km datasets
and Cad Center of Have, Hmax, σH, λp and λf were prepared for the whole Japan5). The
geometric parameters were then used to estimate a nationwide d and zo distribution.
Monthly diurnal 22.5 s x 15 s. AH datasets for the whole Japan were provided by the
National Institute of Advanced Industrial Science and Technology (AIST).

62

The target cities are Osaka, Nagoya, and Tokyo. The simulation results were comparedwith observation gauges. The findings focused mainly on the wind field and temperature, in
general, as affected by both roughness and anthropogenic heating from the surface.
5.1 Numerical Settings
Four domains were selected with the final domains (1.2x1.2 km grid spacing) coveringmajor cities such as Tokyo, Osaka, and Nagoya as shown in Figure 5-1. The distribution ofd, zo, and AH is shown in Figure 5-2. Japan Meteorological Agency’s 3-hr. Nonhydrostatic
Mesoscale Model (MSM) with 0.1x0.125 degrees spatial resolution for the atmosphericlevel and 0.05x0.625 degrees resolution for the surface level were used. Lacking skintemperature, soil temperature and moisture in the MSM were compensated with 6-hr.
NCEP FNL Operational Model Global Tropospheric Analyses. The size and resolution of
the domains used are shown in Table 5-1. Two-way nesting was used.

63

36 N

35 N

139 E

140 E

139 E

140 E

139 E

140 E

35 N

34 30’N

135 E
35 40’N

135 30’E

135 30’E

135 E

135 E

135 30’E

35 N

34 30’N
136 30E

137 E

136 30E

137 E

136 30E

137 E

Z0m (m.)d (m.)AHE (W/m2)

Figure 5-2 Distribution of d, zo, and 1200 LST AHE for Tokyo domain (Top), Osaka
domain (Middle), and Nagoya domain (Bottom) domains. The selected AMeDAS stations
for comparison are encircled in the d distribution.

64

Table 5-1 Domain Settings
DomainResolution
E-W grid size
S-N grids size
Vert levels
Time step% UrbanTokyo
1.2 km201
181
28
2.143 s50.19 %

Osaka
1.2 km121
117
28
2.143 s50.32 %

Nagoya
1.2 km109
173
28
2.143 s44.89 %

Table 5-2 Simulation Cases
Cases
NEW
CTL
NEWnAHd and z0m
GRIDDED
FIXED(5.71,0.33)
GRIDDEDAH
GRIDDED
None
None

The total simulation dates were from August 11 – 13, 2013 with a one day spin-up
period using the WRFv3.3.1. These days were selected due to its country-wide highpressure condition. A localized heavy rainfall incident was recorded on August 12 in Tokyo
but neglected in this study. Table 5-2 summarizes the cases. To emphasize the effect ofurban areas using the gridded surface parameters, a NEW case was simulated incomparison to the CTL. NEW is the updated WRF version considering all the
improvements. CTL is the default WRF version without AH. An additional NEWnAH run
was conducted to clarify the effect of AH relative to the roughness parameters.

65

5.2 Model Validation
The simulation results were compared with the JMA Automated Meteorology Data
Acquisition System (AMeDAS) stations from August 10 – 13, 2013. The selected stations
are highlighted in Figure 5-2.
5.2.1 Near-ground Wind Speed
The WRF simulation outputs the wind speed at 10 m diagnostically and at the firstvertical grid level, with heights approximately at 30 m. The values for comparison depend
on whichever is closer to the AMeDAS observation level. Root Mean Square Error(RMSE) was calculated using 58 hourly data for each station (Figure 5-3).
RMSE is least for the NEW and NEWnAH. The little difference of accuracy between
them suggests that AH has little influence in the wind speed. The large difference in RMSE
values between NEW and CTL means urban roughness has a larger influence in the windspeed accuracy. Moreover, stations with d greater than 10 m have large RMSE differences.
The top 3 locations with the largest CTL and NEW RMSE differences are Tokyo, Osaka,
and Chiba. Least improvements were seen at the selected points of Nagoya domain whereroughness parameters were found lower compared to other locations. Although the station
at Yokohama, Kanagawa shows low roughness parameters, the 2-dimensional effect fromits surroundings (e.g. Tokyo and its surroundings with significant roughness values) affectthe accuracy.
The main reason behind the larger RMSE values for CTL is that it overestimates thesurface wind speeds (Figure 5-4) at rough areas. NEW performs well in matching with theobserved wind speeds for both Osaka and Tokyo. Both NEW and CTL remain to66

overestimate the winds at Nagoya. In spite this, NEW approach closer to Nagoya station’sobservation. Nagoya domain, with lesser roughness parameters than Tokyo and Osaka,
simulated a more complicated wind circulation with winds alternating between the sea andthe surrounding mountains. This suggests the possibility that the selection of domain size,
target dates, and boundary conditions highly influenced the accuracy for these dates.

NEW
CTL
NEWnAH
Displacement Height

RMSE for Wind

2
1.5

45
40
35
30
25
20

1

15
10

0.5

Displacmeent Height (m.)

2.5

5
0

4
3.5

RMSE for T2

3
2.5

Osaka

Kobe

Tokyo

Yokohama

Chiba

TsukubaNagoyaToyotaNEW
CTL
NEWnAH
z0m

8
7
6
5

2

4

1.5

3

1

2

0.5

1

Roughness Length for Momentum (m.)

0

0

0
Osaka

Kobe

Tokyo

Yokohama

Chiba

TsukubaNagoyaToyotaFigure 5-3 RMSE values for wind speed (Above) and 2 m temperature (Below) for eachcase and AMeDAS station. Corresponding d and z0m plotted in filled triangles.
67

Osaka6
4
2

0
09 12 1510 Tokyo
8
6
4
2
0
12 15
8 09Nagoya

Wind Speed (m/s)

Wind Speed (m/s)

Wind Speed (m/s)

8

18

21

00

03

06

09

12

15

18

21

00

03

06

09

12

15

18

21

00

03

06

09

12

15

18

21

00

03

06

09

12

15

18

21

00

03

06

09

12

15

18

21

00

03

06

09

12

15

6
4
2
0
09

12

15

AMEDASNEWCTL

NEWnAH

Hour LST

Figure 5-4 3-day observed and simulated wind speed trend from August 10 – 13, 201368

Temperature ( C)

40
35
30
25
Osaka

Temperature ( C)

20
40 09

12 15 18 21 00 03 06 09 12 15 18 21 00 03 06 09 12 15

35
30
25
Tokyo

Temperature ( C)

20
40 09

12 15 18 21 00 03 06 09 12 15 18 21 00 03 06 09 12 15

35
30
25

Nagoya20
09 12 15 18 21 00 03 06 09 12 15 18 21 00 03 06 09 12 15
AMEDASNEWCTL

NEWnAH

Hour LST

Figure 5-5 3-day observed and simulated T2 trend from August 10 – 13, 2013

5.2.2 Near-surface Temperature
When it comes to temperature simulation, the effect of other improvements aside from dand z0 is also significant. It is interesting that NEW with the exception of Nagoya performs
well in simulating diagnostic 2 m temperature, T2. NEWnAH has the least accuracy for
Osaka, Kobe, Tokyo, and Nagoya. This means that NEW improves the wind distribution
but needs the AH to have a more accurate simulated temperature. Large differences in69

simulated temperature between 1500 LST and 1800 LST on August 12, 2013 in Tokyo(Figure 5-5) is brought about by the difference in localized heavy rainfall distribution, notcovered in this study. Over-all, T2 is underestimated by all cases in Osaka and Tokyothroughout the simulation period. Nagoya generally underestimates during nighttime (1800– 0400 LST) with NEW cases overestimating during daytime (0500 – 1700 LST). Bias wascalculated by averaging the difference between observed and simulated 2-m temperature(Observed T2 – Simulated T2). Osaka, Tokyo, and Nagoya has daytime (nighttime) biases of
0.35°C(1.40), 0.95°C(1.25), -0.42°C(0.92), respectively, for the NEW. On the other hand,
CTL has corresponding daytime (nighttime) biases of 1.03°C(1.43), 1.33°C(1.64),
0.62°C(0.72). Although all cases perform worse during nighttime than daytime, NEW
shows improvement throughout all selected stations except for Nagoya for the same reason
mentioned in the previous section. The nighttime bias is possibly due to the long-term
meteorological boundary bias or the selected physics option. The larger simulated
temperature at urban areas in NEW also corresponds to a larger horizontal pressure gradientdirected into rougher cities.
5.3 Results and Discussions
Similar to the sea breeze study, the surface wind speed, the wind profile, and theboundary layer height was investigated.
5.3.1 Vertical Profile of Horizontal Winds
Figure 5-6 shows the difference between CTL and NEW wind speed with height above
mean sea level (AMSL) averaged throughout the simulation period. The results were70

interpolated from 80 m to 3000 m AMSL using the Grid Analysis and Display System(GrADS). A few hundreds of meters from the ground, significant difference in wind speed
between NEW and CTL could be seen. Close to the surface, wind speed was reduced at an
average of 1.75 m/s, 1.28 m/s, 0.73 m/s for Tokyo, Osaka, and Nagoya, respectively.
Reduction reached heights of 400 m, 500 m, and 650 m for Nagoya, Osaka, and Tokyo,
respectively. Without the AH surface wind speeds at Tokyo, Osaka, and Nagoya are further
reduced by 1.89 m/s, 1.49 m/s, and 0.88 m/s, respectively. The result suggests that input
roughness parameters are more dominant than AH in influencing surface wind speed. The
larger the d and z0m values the higher the effect from the surface.

Tokyo
Osaka
NagoyaFigure 5-6 CTL and NEW horizontal wind speed difference averaged from 58 hourly data
covering August 10 – 13, 2013 (dots); CTL and NEWnAH difference (lines)

71

Slight increase in wind speed was simulated for the NEW at locations above thereduction region. AHE causes horizontal pressure gradients toward the city. Considering
only AHE effect (NEW minus NEWnAH), wind speeds were slightly faster for the NEW.
The factor directly influencing the speed reduction high above the boundary layer isdifficult to interpret at the current study due to the limited cases and observation capacity.
Future validation high above the boundary layer is still needed.
Occasionally, NEW wind speed increases slightly compared to the CTL very close to thesurface (80 m, not shown), especially when wind speeds fall below 1 m/s. The instability
caused by the warmer temperatures in the NEW causes increase in downward vertical windspeeds and momentum. A similar phenomenon was also observed in an experimentconducted in Poland from 1997-2002 (Fortuniak et al., 2006).
5.3.2 Surface Drag from Roughness
Looking at the horizontal wind speed distribution, drag was intensified in the NEW
throughout the simulation period. The surface drag, as shown in Figure 5-7, is significant at
locations with large roughness parameters. From noon of August 12, 2013, sea breeze from
Osaka Bay penetrated inland. Not depicted in CTL, the winds are reduced due to thepresence of buildings (circles in Figure 5-7). As the warm land continues to promote inflow,
winds leeward from the building once again increase in intensity while surface layer winds
at urban areas remain to be less intense throughout for the NEW. The effect of roughness
parameters to the front can be seen from the convergence line. The disturbance distorts thefront on urban areas in contrast with the CTL – convergence region perpendicular to theflow remains to be obvious.
72

Figure 5-7 August 12, 2013 hourly snapshot of 10 m sea breeze flow (dark wind vectors >3 m/s), and 1st atmospheric level divergence (contours) towards Osaka for NEW and CTL

The same phenomenon was observed in Tokyo and Nagoya. A convergence region (blueovals on Figure 5-8) prevails directly at the boundary between Tokyo and its bay which
was not observed in Osaka for two reasons behind this. First is because Tokyo has larger
and wider d and z0m than Osaka. Second is because large d and zo lies closer to thecoastline blocking incoming winds. Aside from sea breeze, northwesterly wind was also
reduced as it approached Nagoya city. The tall buildings at Nagoya affect the surrounding
wind circulation (not shown).

73

Figure 5-8 August 11, 2013 hourly snapshot of 10 m sea breeze flow (dark wind vectors >3 m/s), PBLH (solid black lines) and 1st atmospheric level divergence (colored lines) for
NEW and CTL

Fortuniak, K., Klysik, K., Wibig, J., 2006, Urban-rural contrasts of meteorological parameters in
Lodz, Theor. Appl. Climatol., 84, pp. 91 – 101

5.3.3 Atmospheric Boundary Layer
NEW resulted to a higher boundary layer height (PBLH) compared to CTL. The PBL
built-in physics scheme used was the Mellor-Yamada Nakanishi and Niino Level 2.5. In
Figure 5-9, the Tokyo metropolitan area shows ~100 m higher for the NEW at 1500 LST.
Because of the limited sensitivity cases, the individual contribution of each parameter to the
PBL evolution cannot be clarified at the current study stage. Nonetheless, the little changes
in heat flux and the increased 1st level convergence in NEW hints on the possibility that74

forced convection, directly affected by input roughness parameters, has significant
influence to the PBLH. Forced convection above the city also affects the PBLH as depictedin the PBLH difference between NEWnAH and NEW (Figure 5-9).
CTL_PBLH
CTL_HFXNEWnAHNEWnAH

1000

800

1500

600

1000

400

500

200

0
20

0

Divergence(1/0E-4/s)

PBLH (m.)

2000

NEW_PBLH
NEW_HFX

CTL

10

NEW

Heat Flux (W/m-2)

2500

NEWnAH

0

-10

-20
9

11

13

15

17

Hour (LST)

Figure 5-9 August 11, 2013 trend of PBLH, sensible heat flux (HFX), and 1st atmosphericlevel divergence in Tokyo AMeDAS station

5.4 Chapter Summary
After having satisfactory results in the reproducibility of near-ground winds in theprevious chapter, the model application was then extended at other dates and other major
cities. This was motivated by the recent development of a nation-wide distribution ofroughness and anthropogenic heat parameters by Makabe et al. (2014). Furthermore, it isthrough this chapter where the anthropogenic influence to winds is compared to the
aerodynamic roughness effects. Results show from a 3-day simulation of August 2013 thatthe updated WRF simulated a better near-wind compared to the default version of WRF.
The drag effects are proportional to the roughness parameter values. The delay and75

distortion of sea breeze was also confirmed in Osaka. Finally, anthropogenic heat canincrease slightly the wind speeds but was not as significant as the aerodynamic roughness.

76

6 Sensitivity of WRF Model to Roughness Parameters and Sky-view Factor using a2-dimensional Idealised Case

This chapter was finally added during the latter phase of the dissertation motivated by
the necessity to answer a few questions which arose from the previous chapters’ findings.
Although roughness was found to largely reduce the wind speed (for urbanized areas due tomomentum drag brought about by large roughness length for momentum) and slightly
increase temperature gradient (due to the reduced heat exchange brought about by lesser
roughness length for heat) in major cities in Japan, the proportions of influence of eachurban parameters, d, zo, and sky-view factor were not fully discussed.
There are three reasons why the contribution of each parameter was not clearly
established in the previous chapters. First and foremost, the focus was to showcase theimprovements of the modified WRF model proposed. The modified WRF showed better
results in terms of wind field accuracy as proven in the previous chapters. Second, the mostsignificant improvement was on the parameterisation of d and zo owing to the works of
Kanda et al. (2013). Third, the test simulations highlighted in the previous chapters werebased on real weather phenomena with a lot of factors affecting the results such as land use
distribution, initial and lateral boundary conditions, the influence of other physics, and soon. This non-linear nature of the model was the reason why moisture, energy balance, or
humidity, was not taken into account in the validation.
In this chapter, three sensitivity studies were conducted by idealizing the modified WRF
model. The meteorological parameters analysed are wind speed, mixing ratio, and heat and77momentum fluxes. It is to be carefully noted that this idealized WRF is not similar to theavailable ideal cases of WRF (e.g. em_b_wave, em_fire, em_heldsuarez, em_les,em_seabreeze2d_x) mainly because the provided WRF ideal cases do not allow flexibility
in setting up physics and the domains. The procedure for doing this is discussed in the
numerical settings.
6.1 Numerical Settings
The WRF version used was the modified WRF model represented by the NEW cases in
the previous chapters. The first step was to remove the coriolis and curvature effects in themodel to create a sea breeze front parallel to the coastline throughout. Doing this was done
through the adjustment of the map scale factor (responsible for the location and projections)to 1.0 and setting the earth’s radial influence and coriolis terms to zero.
After removing coriolis and curvature effects, static surface boundaries andmeteorological boundaries were adjusted to simulate desired weather condition. To control
the domain without adjusting the external datasets, output netcdfs from geogrid.exe andmetgrid.exe (refer to Figure 3-1) were revised directly using a programming language.
6.1.2 Geographical set-up
Relevant to this study is the land use and urban parameter distribution. To simplify thesurface domain, only two land use classifications were considered: homogeneous urban
surface for land, and water for the sea. The ratio between sea and land is 1:3 as shown in
Figure 6-1. The scale of the domain was retained such that the horizontal coordinates are indegrees longitude (for x) and degrees latitude (for y). With 150 grids in both x and y, the78

grid spacing was set to 1.0 km. The vertical resolution is also decreasing with height similar
to previous chapters (refer to Table 4-1 for eta levels). The geographic coordinates arenecessary to estimate the incoming shortwave radiation. Because of this, land was situatedat the eastern side of the domain to ensure that it is heated a few minutes earlier than the sea.
The urban area has a 0.90 urban fraction with 0.10 allotted for grassland. The groundsurface height above mean sea level is 1.0 m. The albedo set for urban land and sea were
15 % and 8 %, respectively. The emissivity was 0.88 for land and 0.98 for water.
To understand the influence of each parameter, sensitivity studies were conducted byvarying displacement height (4 cases), varying roughness length for momentum (3 cases),and varying sky-view factor (3 cases). In setting up the displacement height, the same
method introduced in chapter 3.2 was used. The default value of 5.731 m was added to theground topography for all cases except for the cases with varying displacement heights. A
default value of 0.33 m for the zo was used except for the cases with varying zo. A default
value of 0.48 m was used for the sky-view factor except for the cases with varying skyview factor.
112 km.

A

B

Water
z
y

Groundtopography addedwith input
displacement
heightA

B

Homogeneous
Urban Area

Figure 6-1 General domain configuration. (Left) Cases with no varying displacement
heights. (Right) Cases with varying displacement height.

79For the cases investigating the effect of d, the values of no displacement (nodisp), 5.71
m (displow), 25 m (dispmid), and 50 m (disphigh) were tested. For the cases investigating
the effect of zo, the values of 0.33 m (zlow), 5 m (zmid), and 10 m (zhigh) were tested.
Lastly, the values of 0.23 m (svflow), 0.48 m (svfmid), and 0.65 m (svfhigh) for the skyview factor were tested. Each effect is discussed in the next sections.
It must be carefully noted that in reality the roughness parameters and sky-view factor
are inseparable parameters; such that, large d corresponds to a larger zo but never exceeding
it; and, rough grids tend to have lower sky-view factor values. Since the aim of this chapter
is to determine how each parameter affects the model, their relationship is temporarily
neglected and extreme values are used. A careful consideration of this issue isrecommended, however, in the future analyses.
6.1.3 Meteorological set-up
Prior to running wrf.exe, 16 vertical levels of meteorological inputs were considered.
Meteorological parameters were acquired from the background meteorology of the sea
breeze simulation in chapter 4. In other words, the meteorology for September 13 and 14,
2011 were used as reference. Spatially uniform time-varying (three hour interval) values
were assigned for each level. Trends for pressure, relative humidity, and input potential
temperature (decreasing with height) are shown in80

Geopential Height (m)18000

2011/09/13 6:00

16000

2011/09/13 9:00

14000

2011/09/13 12:00
2011/09/13 15:00

12000

2011/09/13 18:00
10000

2011/09/13 21:00

8000

2011/09/14 0:00

6000

2011/09/14 3:00

2011/09/14 6:00

4000

2011/09/14 9:00
2000

2011/09/14 12:00

0

2011/09/14 15:00

200

20200

40200 60200 80200
Pressure (Pa)100200 120200

2011/09/14 18:00

Figure 6-2, Figure 6-3, and Figure 6-4, respectively. Inflow winds (UU and VV) were
set to 0 m/s. Only the sea surface temperature and skin temperature vary between land andsea (refer to Figure 6-5). The same meteorological inputs were used for all cases employedin this chapter. All simulations had a one day spin-up period.

Geopential Height (m)18000

2011/09/13 6:00

16000

2011/09/13 9:00

14000

2011/09/13 12:00
2011/09/13 15:00

12000

2011/09/13 18:00
10000

2011/09/13 21:00

8000

2011/09/14 0:00

6000

2011/09/14 3:00

2011/09/14 6:00

4000

2011/09/14 9:00
2000

2011/09/14 12:00

0

2011/09/14 15:00

200

20200

40200 60200 80200
Pressure (Pa)100200 120200

2011/09/14 18:00

Figure 6-2 Input pressure. Time is in UTC (LST minus 9 hours)

81

120002011/09/13 6:00
2011/09/13 9:00

Geopential Height (m)10000

2011/09/13 12:00
2011/09/13 15:00

8000

2011/09/13 18:00

2011/09/13 21:00
6000

2011/09/14 0:00
2011/09/14 3:00

4000

2011/09/14 6:00
2011/09/14 9:00

2000

2011/09/14 12:00
2011/09/14 15:00

0

2011/09/14 18:00
0

20

40
60
Relative Humidity (%)

80

100

Figure 6-3 Input relative humidity. Time is in UTC (LST minus 9 hours)

Geopential Height (m)18000

2011/09/13 6:00

16000

2011/09/13 9:00

14000

2011/09/13 12:00
2011/09/13 15:00

12000

2011/09/13 18:00
10000

2011/09/13 21:00

8000

2011/09/14 0:00

6000

2011/09/14 3:00
2011/09/14 6:00

4000

2011/09/14 9:00
2000

2011/09/14 12:00

0

2011/09/14 15:00
200

220

240
260
280
Air Temperature (K)

300

320

2011/09/14 18:00

Figure 6-4 Input air temperature. Time is in UTC (LST minus 9 hours)

82

305
304

Temperature (K)

303

302
301
300

299
298
297

SST

296

SKINTEMP

295
6:00

12:00

18:00

0:00
Time (LST)

6:00

12:00

18:00

Figure 6-5 Input sea surface temperature (SST) and land skin temperature (SKINTEMP)
from 6 AM of September 12, 2011 to 6 PM of September 14, 2011

6.2 Results and Discussion
6.2.1 Effect of Roughness Parameters d and z0m
Previous chapters mention the combined effect of d and zo because of in real conditions
they are indeed inseparable. Rough buildings, brought about by the height variability andthe average height, displaces the logarithmic wind profile from the surface at the same time
affect its structure by reducing the speed. Through the idealization of WRF, the separate
influence of d and zo can be assessed. Which between the two parameters is the model moresensitive to?
First, varying d was tested using the default zo (0.33 m). Refer to section 6.1.2 for thecase nomenclature. Since the effect of d becomes similar to topography and has little
purpose when added throughout urban domain, only a selected region was considered for d
to vary, as shown in Figure 6-1. The influence to the parameters above region of varying d83

(upper figures of Figure 6-6) is less significant as compared to the cases of varying zo andsky-view factor (shown later). Vapor mixing ratio drops from 7 in the morning to 0.015 kg/ kg, and remaining constant to this level from 10 in the morning similar for all cases at pt.
A. Wind steadily increases with slightly faster wind speeds at higher displacement heightvalues. A d of 50 m (disphigh) shows minimal differences compared with calses of small dvalues. Heat flux differences could also be seen during mid-day but still not very significant
due to the meagre height displaced compared to the height of the planetary boundary layer.
The differences are probably caused by the unstable boundary layer during daytime.
The d, however, affects significantly the surrounding areas from the displaced region,such as pt. B (bottom figures of Figure 6-6). For example, large differences in wind speed
and heat flux could be seen above pt. B due to the differences in convergence occurring
behind the displaced area. This is similar to a hill’s influence to an incoming breeze where
the impact is mostly observed behind the hill. This was the case for Tokyo and Saitama
where sea breeze convergence behind Tokyo was affected by the large d values.

840.017

0.016
0.015

0.014
nodispdispmid0.013

displow
disphigh

Signal at pt. A

6300

nodispdisplow
dispmiddisphigh

5
4
3

0.02

6

7

8

9

100
50
0
5

10 11 12 13 14 15 16 17
7

0.019
0.018
0.017
0.016
0.015

0.014
nodispdispmid0.013

displow
disphigh

W-E Wind Speed (m/s)

Signal at pt. B

6

6

7

8

9

5

6

7

8

9

10 11 12 13 14 15 16 17
Hour (LST)5

10 11 12 13 14 15 16 17

4

7

8

9

10 11 12 13 14 15 16 17 18

Signal at pt. B
350

nodispdisplow
dispmiddisphigh

5

6

400

Signal at pt. B

nodispdisplow
dispmiddisphigh

300

3

250

200
150

2

100

1

50
0

0

0.012

150

1

W m-2

5

nodispdisplow
dispmiddisphigh

200

2

0

0.012

Signal at pt. A

250

W m-2

0.018

Mixing Ratio (kg / kg)

350

7

Signal at pt. A
W-E Wind Speed (m/s)

Mixing Ratio (kg / kg)

0.02
0.019

5

6

7

8

9

10 11 12 13 14 15 16 17
Hour (LST)

5

6

7

8

9

10 11 12 13 14 15 16 17 18
Hour (LST)

Figure 6-6 Simulated meteorological parameters for each case and location, where:(Leftmost) first atmospheric level vapor mixing ratio; (Middle) first atmospheric levelwest-east wind component; (Rightmost) sensible heatflux. Top and bottom figures arehourly signals for pt. A and pt. B, respectively.

From the findings about d, it can be deduced that zo has larger influence to the windspeeds and momentum fluxes at pt. A. Refer to section 6.1.2 for the case nomenclature.
zhigh is typical value for Shinjuku, zmid is typical for Tokyo metropolitan area, and zlow istypical for rural areas and default WRF. Truly, the impact of zo is much more significant(see the case differences in Figure 6-7) to reduce the wind speed for both points A and B up
to as high as 3 m/s. Results to differences in wind speed less than 1.0 m/s. The large
reduction of wind speeds reflects to lesser heat and momentum exchange in the surface(smaller values of vapor mixing ratio and sensible heat flux). Sudden drops or breaks in theheat flux can be attributed to the cloud formation. It must be carefully noted that in spite thereduced heat flux, there is still a tendency for zhigh to have a larger thermal low than zlow
due to the larger skin temperature (and thus air temperature).

85

7

0.018
0.017
0.016

0.015
0.014

zlow

0.013

zmid

zhigh

350

Signal at pt. A

6

zlow
zmid
zhigh

5
4

W m-2

Signal at pt. A
W-E Wind Speed (m/s)

3

5
0.02

6

7

8

9

0.018

0.017
0.016
0.015
0.014
zlow

0.013

zmid

zhigh

0.012

50
0
5

6

7

8

9

10 11 12 13 14 15 16 17

Signal at pt. B

6

6

7

8

9

10 11 12 13 14 15 16 17
Hour (LST)

5
300

zlow

6

5

zhigh

4

7

8

9 10 11 12 13 14 15 16 17 18

Signal at pt. B

250

zlow

200

zmid

zmid

zhigh

150

3

100

2

50

1

0

0
5

zhigh

150
100

7

W-E Wind Speed (m/s)

Vapor Mixing Ratio (kg / kg)

0.019

zmid

200

1

10 11 12 13 14 15 16 17

Signal at pt. B

zlow

250

2

0

0.012

Signal at pt. A

300

W m-2

Vapor Mixing Ratio (kg / kg)

0.02
0.019

5

6

7

8

9

10 11 12 13 14 15 16 17
Hour (LST)

5

6

7

8

9 10 11 12 13 14 15 16 17 18
Hour (LST)

Figure 6-7 Simulated meteorological parameters for each case and location, where:(Leftmost) first atmospheric level vapor mixing ratio; (Middle) first atmospheric levelwest-east wind component; (Rightmost) sensible heatflux. Top and bottom figures arehourly signals for pt. A and B, respectively.

Obeying to the logarithmic wind assumption, effects brought about by zo lessen withlarger zo. For example, large differences in heat flux could be seen (around 50 W m-2)
between zlow and zmid but almost no differences (unless clouds form) between zmid andzhigh. In Figure 6-8, the dominant influence of zo over d could also be seen. Delays andreduction to the trailing winds could be seen for zmid and zhigh but not prominent for casesof varying displacement heights. The slower winds above the sea for the zmid and zhigh arebrought about by feedback from the previous day’s simulation. In zhigh, the reduction inheat flux from the surface reduces free convection which is why clouds, unless at the front,
are not formed compared to the other cases.
To confirm the findings in d’s minor influence to the intensity of meteorologicalparameters, spatial average of the simulated values at the first atmospheric level over land86was calculated. It can be seen in Figure 6-9 that the differences are not very significant
when the whole domain is considered for each case. Slight differences in the wind speed
can be distinguished due to the effect of d to the terrain.

displow

dispmiddispmid

zlow

zmid

zhigh

0

2

4

6

m/s

Figure 6-8 Simulated first atmospheric winds (colored vectors) and total colulmn cloud
mixing ratio (shaded) for various cases at 1300 LST.

87

300

nodispdisplow
dispmid

W-E Wind Speed (m/s)

6
5

250
200

W m-2

Mixing Ratio (kg / kg)

7

0.02
0.019
0.018
0.017
0.016
0.015
0.014
0.013
0.012

4

150

3

100

2

nodispdispmiddisplow
disphigh

50

1

0

0

5 6 7 8 9 10 11 12 13 14 15 16 17
Hour (LST)

nodispdisplow
dispmiddisphigh

5

6

7

8

9 10 11 12 13 14 15 16 17
Hour (LST)

5 6 7 8 9 10 11 12 13 14 15 16 17 18
Hour (LST)

Figure 6-9 Simulated meteorological parameters spatially averaged over land, where:(Leftmost) first atmospheric level vapor mixing ratio; (Middle) first atmospheric levelwest-east wind component; (Rightmost) sensible heatflux.

6.2.2 Effect of Sky-view
As mentioned in section 3.3.3, the sky-view factor is estimated in the UCM using the λp
and the λf. λp and λf are also invoked in eqn. 2, 3, 4 and 5 of section 2.3.3 which were used
to estimate d and zo. This analogy suggests the close relationship of aerodynamic roughness
parameters, d and zo, and sky-view factor. Using the similar analysis procedure from theprevious section, the sky-view factor effects can be assessed by fixing the aerodynamic
parameters. Refer to section 6.1.2 for the case nomenclature.
The sky-view factor has a slightly reverse effect to zo. The larger the sky-view factor, themore radiation released via heat flux (Figure 6-10). The higher the heat flux, by 50 W m-2,
being released also generates faster winds due to the enhance temperature gradient between
land and sea. With barely 0.40 difference between svflow and svfhigh, 1 m/s can beobserved at pt. A. However, there’s almost no difference in intensity between zmid andzhigh.

88

7

0.018
0.017
0.016
0.015
0.014
svflow

0.013

svfmidsvfhigh

0.012

5

svfmid4

svfhigh7

8

9

svfmid250

svfhigh

150

2

100

1

50
0

5
7

0.018
0.017
0.016
0.015
0.014
svflow

0.013

svfmidsvfhigh

0.012

6

7

8

400

Signal at pt. B

6350

svflow

5

svfmid4

svfhigh

6

7

8

9

10 11 12 13 14 15 16 17
Hour (LST)5

9 10 11 12 13 14 15 16 17

6

7

8

svflow
svfmid250

svfhigh

2

100

1

50

9 10 11 12 13 14 15 16 17 18

Signal at pt. B

300

3

200
150

0

0
5

svflow

200

3

10 11 12 13 14 15 16 17

Signal at pt. B

Signal at pt. A

300

W m-2

6

W-E Wind Speed (m/s)

Vapor Mixing Ratio (kg / kg)

0.019

350

svflow

0

5
0.02

400

Signal at pt. A

6

W m-2

Signal at pt. A
W-E Wind Speed (m/s)

Vapor Mixing Ratio (kg / kg)

0.02
0.019

5

6

7

8

9

10 11 12 13 14 15 16 17
Hour (LST)

5

6

7

8

9 10 11 12 13 14 15 16 17 18
Hour (LST)

Figure 6-10 Simulated meteorological parameters for each case and location, where:(Leftmost) first atmospheric level vapor mixing ratio; (Middle) first atmospheric levelwest-east wind component; (Rightmost) sensible heatflux. Top and bottom figures arehourly signals for pt. A and B, respectively.

Knowing that the sky-view factor has an influence in the aerodynamic field, the question
how significant the effect is in actual urban setting arises. To understand this requires themerging of the previous section’s findings regarding the variations of d and zo. It is
understood that the zo is more dominant over d. Consequently, the influence of zo in urban
areas is also more dominant than the sky-view factor. Why is this? At urban areas such as
Tokyo, the sky-view factor is low with higher zo. Further, there is little difference in thedefault sky-view factor and the revised one (Figure 3-5) than the default zo and its revised
value (Figure 3-2) at urban areas. Along with the statements in section 3.3.3 regarding the
reduced influence of larger and revised sky-view factor at non-urban dominant grids due tothe urb_frc, the finding in this section supports the bold assumption that the improvements
mentioned in section 3.3 have lesser impact to the simulation than the improved89aerodynamic roughness parameters, themselves. In spite this, it is still recommended to use
the revised version of sky-view factor with the realistic aerodynamic roughness parameters.
6.2.3 Sensitivity of Boundary Layer HeightThe sensitivity of the simulated boundary layer height to the parameters is discussed.
This section is important to determine up to which extent above ground each parameter
affects the atmosphere. The evolution of the boundary layer for selected cases can be seenin Figure 6-11. Depressions are brought about by the clouds. The terrain-like effect of d canbe seen in the development and decline of boundary layer height. An earlier jumpdownwind of the displaced region can be seen in the morning (see B at 10:00 LST). The
delayed penetration of sea breeze brought about by the blocking of the displaced area in theafternoon also tends to hinder the suppression of the boundary layer (see B at 17:00 LST).
The effect of zo again makes the largest impact to the boundary layer height. Due to thehigher transfer coefficient of heat, lower zo tends to have a faster boundary layer growth in
the morning (see C and D on 10:00 and 12:00 LST). The higher zo continues to supply heat
into the urban boundary layer at a steady rate until later in the afternoon even at lower heat
flux. At 17:00 LST, the lower zo allows a faster sea breeze penetration compared to thehigher zo. This phenomenon contributes to the finding that rough areas tend to have higher
boundary layer in spite lower heat flux late in the afternoon. The drag effect on sea breeze
and the steady supply of heat from the surface result to a higher boundary layer. Finally, thedifferences in the sky-view factor have some influence similar to zo but not as much. The
smaller the sky-view factor the shorter and slower the boundary layer height becomes.

906.3 Chapter Summary
To understand the individual effect of the urban parameters, and idealized simulation ofsea breeze was conducted. This idealized model utilizes the full features of WRF, with thesame domain resolution, and model physics options as the previous chapter. Until theprevious chapters, roughness was defined by the combined effect of inseparable parameters,
displacement height and roughness length for momentum. Furthermore, any sensitivity to
the sky-view factor was not considered. This chapter answers the question how the threeparameters (displacement height, roughness length for momentum, or sky-view factor)
influence WRF. It was found that the displacement height tends to create a terrain effect but
has negligible effects to the energy balance. Large roughness length for momentum is themost dominant parameter which influences the simulated wind field thereby also affecting
the boundary layer height evolution the most A lower sky-view factor at urban areas also
may reduce the wind speed compared to the default value due to the trapping of radiationnear the surface but not as significant as the roughness length for momentum (chapter 6).

91

A

B

A

B

A

B

C

D

C

D

C

D

E

F

E

F

E

F

10:00 LST

12:00 LST
Boundary Layer Height (m)17:00 LST

Figure 6-11 1000 LST, 1200 LST, and 1700 LST snapshots of the distribution of boundary layer height for cases: displow (A),disphigh (B), zlow (C), zhigh (D), svmid (E), and svfhigh (F)92

7 Conclusion

There remains limited understanding regarding the impact of buildings to weather at themesoscale level due to inadequate representation of weather models. This study addressed
the issue of wind field inaccuracy by weather models through the incorporation of new
aerodynamic roughness parameters and its advance parameterisations into the Weather
Research and Forecasting Model (WRF).
A distribution of aerodynamic roughness parameters, derived from new LES-derived
aerodynamic feedback parameterisation, was prepared and included into the single-layerurban canopy model. A 1-kilometre distribution of d and zo was prepared for Japan andtested on a sea breeze event using the WRF model (Objective 1 and Objective 2). The newvalues of d and zo that were calculated for Tokyo reached around three times larger than theconventional values used in WRF. The updated model, comprising the new aerodynamic
parameters, and the default WRF model (difference between default and updated model
shown in Table 4-1) were compared.
The updated model was evaluated (Objective 3) using a two-month simulation focusedon dry days. Simulated wind speeds were found to match ground observations wellespecially at urban areas. A difficulty remains in the calculation of the diagnostic 2-mtemperature, with the updated version overestimating observed values. The deviations werepossibly due to the representativeness of 2-m temperature observation gauges. The newparameters improved the simulation of clouds compared with images from a high-spatialresolution geostationary satellite.

93

The influence of the distribution of new aerodynamic roughness parameters are listed asfollows (No. 1– 6 achieved objective 4; No. 6–9 achieved objective 5) :1) Surface wind speeds behind the sea breeze front weaken in highly urbanised areas
like Tokyo and its neighbouring cities due to the significant drag from the surface(chapter 4 and chapter 5).
2) Wind speed can be affected up to significant heights within the boundary layer, also
affecting cloud formation (chapter 4 and chapter 5).
3) The strong convergence/divergence region to the north of Tokyo widens due to theweakened sea breeze (chapter 4).
4) A more intense elongated convergence region caused by increased roughness, sea
breeze, and heat island circulation was simulated and extended from Tokyo to theinland (chapter 4).
5) The findings in Tokyo were also confirmed in other major cities throughout Japan,such as Osaka and Nagoya (chapter 5).
6) The influence of anthropogenic heat emission was found to be subordinate to the
proposed aerodynamic roughness parameters in simulating the wind field (chapter
5).
7) The displacement height tends to create a terrain effect but has negligible effects tothe energy balance (chapter 6).
8) Large roughness length for momentum is the most dominant parameter which
influences the simulated wind field thereby also affecting the boundary layer height
evolution the most (chapter 6).

949) A lower sky-view factor at urban areas also may reduce the wind speed compared tothe default value due to the trapping of radiation near the surface but not assignificant as the roughness length for momentum (chapter 6).
Inaccuracies in the simulation of wind and cloud in some areas were generated byinsufficient roughness parameter data and model sensitivity to the assumed settings (e.g.,
meteorological and surface boundary conditions, physics options, domain size). Althoughbeyond the scope of this study, the possible causes of prevalent inaccuracies need to beaddressed and further testing on other parameters such as moisture and rainfall is necessary.
The contributions of this research to the research community and society are as follows:
1. Knowing that sea breeze penetration inland can be influenced largely by the urban
conditions upstream, conventional models might be underestimating the actual
impact of urban areas to extreme weather conditions like localized heavy rainfall andheat wave;
2. Due to the ability to vary the roughness distribution of the single-layer urban canopy
model, the updated model can be a wise alternative for developing countries whereobservations and research are limited;
3. Propose improvements to the widely used open-source WRF model;
4. In terms of urban planning, the improved model can allow testing of various building
configurations unlike the former single-layer urban canopy model where this option
is previously unavailable;955. With the improvements and findings contained in this study, the model usage will
also enable decision makers to create policies that will counter the negative effects ofurbanization.
6. The findings in this study could assist in the determination of appropriate
representative sites for observation.

96

Appendix

A. Individual influence of geometrical parameters to d and zo
Tests were conducted using the equation introduced in chapter 3. Figure A-1 shows thepossible combination of d and zo with increasing Have (5 m to 50 m at 5 m interval),increasing λp and λf (0.1 to 1.0 at 0.1 m interval). For KA2013, Hmax was set equal to Haveand σH set to zero to look only at uniform arrays. Increasing frontal area index increases themagnitude of the zo at a given d. Increasing the plane area index increases the d at a given zo.
Increasing the Have increases both d and zo.
25
MD1998

Increasing Plane Area Index

KA2013

Increasing Frontal Area Index

20

zo (m)

15
10
5
0
0

10

20

30

40

50

60

70

d (m)
Figure A-1 Combination of possible values of d and zo for uniform arrays using MD1998
and KA2013

Figure A-2 shows the possible combination of d and zo for increasing Hmax and σH while
fixing other geometrical properties. Have was set to 10 m while λp and λf were set to 0.5. The
difference between Hmax and Have increases the magnitude of d. However, the influence of a97

very tall Hmax decreases as it departs to far from the Have. On the other hand, large σH tends
to have a larger zo.
Increasing Hmax
Increasing Building σH

3
2.5

zo (m)

2

1.5
1

MD1998KA2013

0.5
0

0

5

10

d (m)

15

20

25

Figure A-2 Combination of possible values of d and zo using MD1998 and KA2013 withvarying Hmax and σH for fixed Have, λp and λf. Red asterisk represents uniform arraycondition.

98

B. Default vs. proposed estimation of sky-view factor
The default sky-view factor used in this study, 0.48, was based on a formulation by
Kusaka et al. (2001) for a 2-dimensional canopy. It was calculated from the normalizedheight (roof height divided by the sum of road width and roof width) and the normalized
roof width (roof width divided by the sum of road width and roof width). The assumed roof
height, road width, and roof width, are 7.5 m, 9.4 m, and 9.4 m, respectively.
Comparing with the proposed calculation of sky-view factor in section 3.3.3, the λp andλf can be used as substitute for the normalized roof width and normalized height,
respectively. Figure B-1 was created by varying values of λp and λf, ranging from 0.1 to 0.9
at 0.1 intervals, into the default and proposed equation. Slight overestimation in the 2dimensional assumption of sky-view factor can be seen when compared with the method of
Kanda et al., 2005a. Based on the correlation plot, a quadratic correction can be applied in
the future to the default sky-view factor in the UCM.

Default sky-view factor

1
0.9

y = -1.01x2 + 1.89x

0.8
0.7

0.6
0.5

0.4
0.3
0.2
0.1
0
0

0.2

0.4

0.6

0.8

1

Proposed sky-view factor
Figure B-1 Comparison of sky-view factor derived from the default UCM and the proposed
update from section 3.3.3

99

C. The Single-layer Urban Canopy Model by Kusaka et al. (2001)
The model description of the single-layer urban canopy model proposed by Kusaka et al.
(2001) is summarized herein. This section include excerpts of the following interactions
modelled within or directly above the urban canopy: sensible heat flux, wind speed in thecanyon, and surface temperatures. The model assumes a representative infinitely uniform
street canyon for a model grid. The parameterization of solar fluxes and longwave fluxes isleft for the readers to refer to Kusaka et al. (2001).
1. Sensible heat flux
Sensible heat flux from the building roof, building wall, and road was estimated at eachsurface separately. The equations are,
HW  CW TW  TS ,(C-1)

H G  CG TG  TS  .

(C-2)

CW  CG  7.51U S0.78 ,(C-3)

CW  CG  6.15 4.18U S .

(C-4)

If U S  5 m s -1 then,else,

Here, TW and TG are surface temperatures at the wall and road, respectively. TS is thereplaced canyon surface temperature defined at a certain height within a canopy and above
the displacement height. The US is the wind speed at zo + d. The sensible heat exchange

100between the canyon space and the overlaying atmosphere is the heat flux through thecanyon top, and is,
H a  c p

ku*

h

TS  Ta  .

(C-5)

Here, u* is the friction velocity and k is the Von Karman constant,  and c p are the air
density at the reference height and the heat capacity of dry air, respectively.  h is theintegrated universal function,h  

' 

h

T '

d ' ,(C-6)

where  T  zT / L and   za  d  / L and L is the Obukhov stability length, computedfrom iterations.
The sensible heat flux from the building wall HW and from the road HG must be balancedby the sensible heat flux to the atmosphere from the canyon space. In other words, the totalheat flux to the atmosphere from the urban area is the area-weighted average from the roof
and that through the canyon top. If the ground surface area is classified into urban andvegetated with specific area ratios, the total heat fluxes at the grid points are the averaged
heat fluxes weighted by their areas.
2. Wind speed
The mean wind speed in the canyon was used as a reference to calculate HG and HW. The
wind speeds at the roof level Ur and within the canyon US are

Ur Ua

 mr
,
m

101

(C-7)

and
h

U S  U r exp  0.386  .
w


(C-8)

Here, h and w are the normalized building height and width, respectively. The integrated
universal functions above are calculated using
' 

m 

d ' ,(C-9)

d ' ,(C-10)

 r  z r  d  / L(C-11)

0

'

' 

 mr  

m

m

0 '

3. Surface temperatures
The ground heat flux GZ,I and interior temperature TZ,i at the depth z to the i surface arecalculated by the following equations,G z ,i  i

Tz ,i,z(C-12)andTz ,i
1 G z ,i.
z i ci z(C-13)The i is the interior thermal conductivity and  i ci is volumetric heat capacity that are
specific to surface i. The subscript i indicates roof, wall, or road.

102

Acknowledgments

Special thanks to my very supportive supervisor, Prof. Manabu Kanda, for the continual
support and enthusiasm for research. I also owe the success to my mentor, Prof. Makoto
Nakayoshi. His supervision was the reason for the smooth progress of research. Theirknowledge and experience is indeed an inspiration. Finally, I dedicate this dissertation to
my family, my friends in the laboratory and outside, my girlfriend, the Kanda laboratory
staff, and most especially to God.
This research was supported by the Research Program on Climate change Adaptation(RECCA) Fund from the Ministry of Education, Culture, Sports, Science and Technology(MEXT) of Japan and the Grant-in-Aid for Scientific Research (A): 25249066

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