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Figure 4.18 shows the diurnal variation of the UHI difference between the vegetated simulations and the control simulation for the second day of the simulation. This shows peak reductions of up to 0.7 K at noon for the simulation with 30% vegetated fraction (veg2), and of over 0.3 K for the simulation with 10% vegetated fraction (veg1). There is also a smaller peak around 06:00 am. The timing of the peak agrees with Civerolo et al. (2000) who also find the largest differences occur at noon. Potential temperature difference (K) -0.1 -0.2 -0.3 -0.4 -0.5 -0.6 -0.7 0 00:00 04:00 08:00 12:00 16:00 20:00 00:00 Time veg1-a01 veg2-a02 Figure 4.18: Diurnal variation of the difference in potential temperature (K) between the control simulation a01 and the vegetated simulations veg1 (blue) and veg2 (pink) at x=0, y=0, z=10 m for the second day of simulation. The veg1 simulation represents an urban area with 10% vegetated fraction, and the veg2 simulation represents an urban area with 30% vegetated fraction. In order to investigate the existence of a correlation between the fraction of vegetated land cover and temperature, a further set of simulations was carried out, in which the vegetated 118
fraction was increased in steps of 10% from 0% to 40%. Figure 4.19 shows the correlation between the fraction of vegetation and the temperature at the centre of the urban area at noon, the time of day at which the results in Figure 4.18 showed that the peak differences occur. It can be seen that there is a linear correlation between the vegetation fraction and the potential temperature for the range covered by these model simulations. The effect of increasing the vegetation from non-existent to 40% is to reduce the potential temperature by almost 1.5 K. Clearly the amount of vegetation within an urban area could have important consequences for daytime temperatures and human comfort levels. Potential temperature (K) 296.6 296.4 296.2 296 295.8 295.6 295.4 295.2 295 294.8 y = -0.0357x + 296.4 R 2 = 0.9776 294.6 0 5 10 15 20 25 30 35 40 45 Fraction of vegetation in the urban area (%) Figure 4.19: Correlation between the fraction of vegetation in the urban area and the potential temperature (K) at x=y=0, z=10, at 12:00 noon for the second day of simulations It order to estimate whether increasing the vegetated fraction could be used as part of a mitigation strategy for urban temperatures it is necessary to consider what the realistic potential for changing this parameter in the city is. For example Sailor (1998) 119
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MODELLING THE IMPACT OF URBANISATIO
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Abstract Urban areas have well docu
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Abbreviations aLMo Meteo Swiss oper
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4.2.2 Impact of the urban area on t
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List of figures Figure 2.1: Scales
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Figure 4.21: Diurnal variation of t
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Figure 6.16: Mean horizontal wind s
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Chapter 1: Introduction Urban areas
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with trends derived from a statisti
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scheme, and therefore it was necess
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The effects of future urban expansi
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2.1 Urban climate modification Urba
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latitudes (Taha 1997), and this is
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100 - 200 m 1 - 2 km 10 - 20 km 100
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epresentatively sample the underlyi
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over the city centre, convergent fl
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Cold islands can also occur in urba
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of the atmosphere. For example, it
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amount of solar radiation absorbed
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(Peterson 1969; Changnon 1992; Coll
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2.3 Justification of modelling appr
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Trusilova (2006) carried out a stud
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constant flux surface layer in the
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the vertical distribution of vegeta
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using different building parameters
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Table 2.2: SWOT analysis for the ME
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Fraction of urban land cover [%] Fi
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The built up area of London can cur
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Figure 2.5(a-f): Builtup area of Lo
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• Mainly high density development
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climate of London, and currently on
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For mid-latitude cities, of which L
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warming of 0.04 °C per decade, in
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out differences in the urban heat i
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adopted in previous studies ranges
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y the implementation of a specific
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A prognostic equation is used to re
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At the surface the temperature is c
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The boundary conditions used for th
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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: educed near surface temperatures 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
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6.2 Effects of the current state of
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During daytime the shape of the are
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The calculation was performed for t
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ecause finding a rural reference po
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development (GLA 2006) and differen
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6.2.4 Wind speed and direction Klai
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Wind speed and direction (ms -1 ) F
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“urban” cells to be affected by
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the urban surface. Many other studi
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eason the near surface potential te
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Mean potential temperature at 12:00
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where A(x) is the area affected by
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Table 6.8: REI for the RADIUS, DENS
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COMBINED series also show a reducti
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for all runs in the series, and cor
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The runs are then combined into one
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to be more linear than the smaller
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The total reduction in mean wind sp
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The REI showed that for all the var
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Higher temperatures in the urban ar
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Over the next ten years the populat
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7.1.1 EXPANSION series The first sc
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Another cause of this form of expan
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7.2 Effects of urbanisation for the
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Mean near surface potential tempera
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UHI intensity (K) 4 3.5 3 2.5 2 1.5
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temperature, an increase in the mea
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7.2.3 Wind speed The effect of the
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7.3 Effects of urbanisation for the
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mean near surface potential tempera
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7.3.2 Wind speed Changes in the mea
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expansion strategies such as the re
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should also be considered to avoid
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models, for example Martilli et al.
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cities (Baker et al. 2002). Already
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uses, painting the roofs white to r
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The full influence of different met
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References Abercrombie, P. (1945).
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Bottema, M. (1997). "Urban Roughnes
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Craig, K. J. and R. D. Bornstein (2
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Grimmond, C. S. B. and T. R. Oke (2
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Jin, M. and J. M. Shepherd (2005).
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Lemonsu, A. and V. Masson (2002). "
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Nitis, T., Z. B. Klaic and N. Mouss
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alance in urban areas - Recent adva
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Seaman, N. L. (2000). "Meteorologic
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Therry, G. and P. Lacarrere (1983).
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Yague, C., E. Zurita and A. Martine
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