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Table 2.1: SWOT analysis for the BEP urban scheme BEP urban canopy scheme Strengths Weaknesses Sophisticated multi-layer urban canopy scheme which is well respected in research No explicit latent heat treatment field Validated in a number of research studies for detailed urban campaigns Expanding implementation into a number of research and operational models Availability of model code from author and permission to use in this PhD research Opportunities Threats Use of a number of urban classes allows best fit to CEH Land Use data and resolves some of the complexity of the urban surface Interpolation of results from the BEP grid back to the METRAS grid allows a higher vertical resolution over urban area Opportunity to compare model results and validation with literature Simplistic anthropogenic heat treatment Sophisticated scheme has the effect of being computationally expensive compared to more simple urban schemes Extensive data requirements Latent heat fluxes must be incorporated for use for London domain since this city differs widely in the vegetated fraction from Mediterranean cities where first implemented 36
Table 2.2: SWOT analysis for the METRAS mesoscale model METRAS mesoscale model Strengths Weaknesses Used for meteorological simulations in urban areas and impact of urban surface on Very simple treatment of urban surface air quality Part of a unique modelling system consisting of a microscale and mesoscale model using the same code Good agreement of simulated and meteorological data for an urban agglomeration Possibility to prescribe sub grid scale land cover classes Established use in department Opportunities Threats Opportunity to develop more advanced urban surface representation Opportunity to contribute towards the METRAS model development Opportunity for integration with the microscale model and urban air quality studies Ongoing development into parallelisation and speeding up model code Relatively small number of land use classes compared to more advanced models, especially for vegetation Complex routines to assimilate land cover data and meteorological data for initialization Computationally expensive for long and complex simulations Complex code structure and differences between BEP and METRAS structures Simple shortwave and long wave radiation treatment are not compatible directly with BEP code structure 37
Page 1 and 2: MODELLING THE IMPACT OF URBANISATIO
Page 3 and 4: Abstract Urban areas have well docu
Page 5 and 6: Abbreviations aLMo Meteo Swiss oper
Page 7 and 8: 4.2.2 Impact of the urban area on t
Page 9 and 10: List of figures Figure 2.1: Scales
Page 11 and 12: Figure 4.21: Diurnal variation of t
Page 13 and 14: Figure 6.16: Mean horizontal wind s
Page 15 and 16: Chapter 1: Introduction Urban areas
Page 17 and 18: with trends derived from a statisti
Page 19 and 20: scheme, and therefore it was necess
Page 21 and 22: The effects of future urban expansi
Page 23 and 24: 2.1 Urban climate modification Urba
Page 25 and 26: latitudes (Taha 1997), and this is
Page 27 and 28: 100 - 200 m 1 - 2 km 10 - 20 km 100
Page 29 and 30: epresentatively sample the underlyi
Page 31 and 32: over the city centre, convergent fl
Page 33 and 34: Cold islands can also occur in urba
Page 35 and 36: of the atmosphere. For example, it
Page 37 and 38: amount of solar radiation absorbed
Page 39 and 40: (Peterson 1969; Changnon 1992; Coll
Page 41 and 42: 2.3 Justification of modelling appr
Page 43 and 44: Trusilova (2006) carried out a stud
Page 45 and 46: constant flux surface layer in the
Page 47 and 48: the vertical distribution of vegeta
Page 49: using different building parameters
Page 53 and 54: Fraction of urban land cover [%] Fi
Page 55 and 56: The built up area of London can cur
Page 57 and 58: Figure 2.5(a-f): Builtup area of Lo
Page 59 and 60: • Mainly high density development
Page 61 and 62: climate of London, and currently on
Page 63 and 64: For mid-latitude cities, of which L
Page 65 and 66: warming of 0.04 °C per decade, in
Page 67 and 68: out differences in the urban heat i
Page 69 and 70: adopted in previous studies ranges
Page 71 and 72: y the implementation of a specific
Page 73 and 74: A prognostic equation is used to re
Page 75 and 76: At the surface the temperature is c
Page 77 and 78: The boundary conditions used for th
Page 79 and 80: are defined in the file m1tini_TAPE
Page 81 and 82: The terms representing the impact o
Page 83 and 84: 3.2.1 Calculation of dynamical effe
Page 85 and 86: θ k ∆z / 2 H Siu (Equation 3.12)
Page 87 and 88: 3.3 Implementation of BEP in METRAS
Page 89 and 90: difficulties in the linking of the
Page 91 and 92: Table 3.3: Description of how the 2
Page 93 and 94: 3.5.2 Orography data Orography data
Page 95 and 96: epresenting suburban development a
Page 97 and 98: Table 3.7 shows a summary of the MI
Page 99 and 100: 3.6 Summary In this PhD study the m
Page 101 and 102:
temperature, air pressure and air d
Page 103 and 104:
Potential temperature [K] Figure 4.
Page 105 and 106:
Wind speed [ms -1 ] Figure 4.2: Ver
Page 107 and 108:
4.2.1 Horizontal cross sections of
Page 109 and 110:
affected, as the flow bends around
Page 111 and 112:
Downwind of the city on the other h
Page 113 and 114:
the city, which is expected since t
Page 115 and 116:
Potential temperature [K] Figure 4.
Page 117 and 118:
simulation this effect is verticall
Page 119 and 120:
differences are expected during day
Page 121 and 122:
tend to cool the surface, but can a
Page 123 and 124:
Figure 4.15 shows the vertical prof
Page 125 and 126:
appropriate value for these paramet
Page 127 and 128:
0.61) and that of conventional grav
Page 129 and 130:
Difference in potential temperature
Page 131 and 132:
educed near surface temperatures of
Page 133 and 134:
fraction was increased in steps of
Page 135 and 136:
works. In the control simulation th
Page 137 and 138:
characterised by the albedo, therma
Page 139 and 140:
is higher by a similar amount. The
Page 141 and 142:
(a) Potential temperature [K] (c) T
Page 143 and 144:
Figure 4.25 shows the vertical sect
Page 145 and 146:
tests also reproduced well document
Page 147 and 148:
Chapter 5: Evaluation of the urbani
Page 149 and 150:
Fraction of urban land cover [%] Fi
Page 151 and 152:
For the summers of 1995-2000 the an
Page 153 and 154:
Table 5.2: Selected periods of simu
Page 155 and 156:
commonly used as a rural reference
Page 157 and 158:
newly urbanised version of the METR
Page 159 and 160:
adiation trapping in the street can
Page 161 and 162:
the second day of simulation (31 st
Page 163 and 164:
Air temperature ( o C) 35 30 25 20
Page 165 and 166:
the model. The smaller percentage o
Page 167 and 168:
Wind speed (ms -1 ) Wind direction
Page 169 and 170:
London Heathrow Airport (LHR) Wind
Page 171 and 172:
A full set of hourly wind speed and
Page 173 and 174:
contain both roofs exposed to direc
Page 175 and 176:
Chapter 6: The effects of urban lan
Page 177 and 178:
simulations with two hypothetical s
Page 179 and 180:
dependent on the r(i,j), the radial
Page 181 and 182:
centre of the domain was more than
Page 183 and 184:
6.2 Effects of the current state of
Page 185 and 186:
During daytime the shape of the are
Page 187 and 188:
The calculation was performed for t
Page 189 and 190:
ecause finding a rural reference po
Page 191 and 192:
development (GLA 2006) and differen
Page 193 and 194:
6.2.4 Wind speed and direction Klai
Page 195 and 196:
Wind speed and direction (ms -1 ) F
Page 197 and 198:
“urban” cells to be affected by
Page 199 and 200:
the urban surface. Many other studi
Page 201 and 202:
eason the near surface potential te
Page 203 and 204:
Mean potential temperature at 12:00
Page 205 and 206:
where A(x) is the area affected by
Page 207 and 208:
Table 6.8: REI for the RADIUS, DENS
Page 209 and 210:
COMBINED series also show a reducti
Page 211 and 212:
for all runs in the series, and cor
Page 213 and 214:
The runs are then combined into one
Page 215 and 216:
to be more linear than the smaller
Page 217 and 218:
The total reduction in mean wind sp
Page 219 and 220:
The REI showed that for all the var
Page 221 and 222:
Higher temperatures in the urban ar
Page 223 and 224:
Over the next ten years the populat
Page 225 and 226:
7.1.1 EXPANSION series The first sc
Page 227 and 228:
Another cause of this form of expan
Page 229 and 230:
7.2 Effects of urbanisation for the
Page 231 and 232:
Mean near surface potential tempera
Page 233 and 234:
UHI intensity (K) 4 3.5 3 2.5 2 1.5
Page 235 and 236:
temperature, an increase in the mea
Page 237 and 238:
7.2.3 Wind speed The effect of the
Page 239 and 240:
7.3 Effects of urbanisation for the
Page 241 and 242:
mean near surface potential tempera
Page 243 and 244:
7.3.2 Wind speed Changes in the mea
Page 245 and 246:
expansion strategies such as the re
Page 247 and 248:
should also be considered to avoid
Page 249 and 250:
models, for example Martilli et al.
Page 251 and 252:
cities (Baker et al. 2002). Already
Page 253 and 254:
uses, painting the roofs white to r
Page 255 and 256:
The full influence of different met
Page 257 and 258:
References Abercrombie, P. (1945).
Page 259 and 260:
Bottema, M. (1997). "Urban Roughnes
Page 261 and 262:
Craig, K. J. and R. D. Bornstein (2
Page 263 and 264:
Grimmond, C. S. B. and T. R. Oke (2
Page 265 and 266:
Jin, M. and J. M. Shepherd (2005).
Page 267 and 268:
Lemonsu, A. and V. Masson (2002). "
Page 269 and 270:
Nitis, T., Z. B. Klaic and N. Mouss
Page 271 and 272:
alance in urban areas - Recent adva
Page 273 and 274:
Seaman, N. L. (2000). "Meteorologic
Page 275 and 276:
Therry, G. and P. Lacarrere (1983).
Page 277:
Yague, C., E. Zurita and A. Martine
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