... 2-m air temperature (Ta-2m) for SIM (BASE) at timings of peak and minimum Ta-2m 186 Appendix F Spatial variation of absolute mean radiant temperature (MRT) for Sim (BASE) at timing of peak MRT... Government of Singapore is aware of the concomitant needs of environmental management and economic (and urban) growth, and has the explicit goal of developing a “Sustainable Singapore using efficient,... variations in thermal comfort in Singapore To evaluate seasonality in thermal comfort conditions, he calculated the hourly standard effective temperature (SET) for a hypothetical person standing in an
MEASURING AND MODELLING SPATIAL VARIATION OF TEMPERATURE AND THERMAL COMFORT IN A LOW-DENSITY NEIGHBOURHOOD IN SINGAPORE LIM HUIMIN VANESSA (B.A (Hons), NUS and UNC-CH) A THESIS SUBMITTED FOR THE DEGREE OF MASTER OF SOCIAL SCIENCES DEPARTMENT OF GEOGRAPHY NATIONAL UNIVERSITY OF SINGAPORE 2014 DECLARATION I hereby declare that this thesis is my original work and it has been written by me in its entirety I have duly acknowledged all the sources of information which have been used in the thesis This thesis has also not been submitted for any degree in any university previously Lim Huimin Vanessa 16 June 2014 i ABSTRACT The research conducted for this thesis applies the ENVI-met v 3.1 microclimate model to a low-density neighbourhood in Singapore with two main objectives First, ENVI-met’s applicability in a humid tropical urban environment is evaluated after careful representation of the site for model input, based on field observations Micro- and bio-climatic evaluations are conducted using measured near-surface (2 m) air temperatures (Ta-2m) and mean radiant temperatures (MRT) at pedestrian height (1.1 m), respectively Results indicate that ENVI-met simulates spatially-averaged Ta-2m better (RMSE: 0.52-0.89°C) during the wetter Northeast (NE) and Southwest (SW) monsoons, than during the dry Inter-monsoon conditions (RMSE: 1.111.41°C) Despite the difference in model performance between periods, systematic errors dominate all the simulations MRT evaluations indicate variable daytime model performance (RMSE: 6.44-14.02°C) where unsystematic errors dominate Although nocturnal MRT is severely underestimated, the differences are consistent leading to smaller RMSE (4.299.18°C), with larger systematic errors The second objective is to assess how manipulating key urban design variables affects the micro- and bio-climate These variables are split into three categories: (i) albedo, (ii) vegetation type and cover, and (iii) building heights Simulations suggest that increasing roof albedo results in notable local-scale Ta-2m reductions but does little to ameliorate heat stress, while increasing wall albedo increases both Ta-2m and MRT, augmenting existing heat stress The vegetation scenarios result in significant micro-scale but negligible local-scale thermal comfort changes Finally, increasing building heights generally improves daytime thermal comfort through increased shading, although maximum heat stress increases at some locations, which predicted output reveals is partly attributable to reduced ventilation Key words: Microclimate modelling, tropical urban climate, thermal comfort, urban heat stress, urban design strategies, ENVI-met ii Acknowledgements I am grateful to my advisor A/P Matthias Roth for being a truly great mentor through the years Without your patience, encouragement and sharp critiques, this thesis would not have come to fruition Thank you for always looking out for me, I could not have asked for a better advisor I would also like to thank Drs Winston Chow and Erik Velasco for all their constructive feedback and guidance I owe my thanks to Soks, who has been my sounding board and an incredible support (and proofreader) throughout the thesis writing process I am indebted to Seth, Shermaine and Suraj for providing much needed field assistance, and also to Pam for helping to read my chapters I also have to thank Seonyoung for her company and deliciously healthy Korean meals that kept me going through the many nights we laboured together Finally, I’d like to thank my family for believing in me and supporting the completion of this thesis Then, there is Alexander who has gotten me through the toughest times Thank you Vanessa Lim June 2014 iii Table of Contents Table of Contents iv List of Tables viii List of Figures .xi Chapter Introduction 1.1 The urban climate and the outdoor environment 1.2 Study goals 1.3 Organization of thesis Chapter Literature review 2.1 Urban climate scales 2.2 Selected aspects of the built environment and their influence on microclimate and thermal comfort 2.2.1 Canyon geometry and orientation 2.2.2 Surface materials 10 2.2.3 Anthropogenic causes 11 2.3 Selected microclimate models 11 2.3.1 Remarks about models 14 2.4 Outdoor thermal comfort 14 2.4.1 Biometeorological parameters affecting thermal comfort 15 2.5 Thermal comfort indices 19 2.6 Outdoor thermal comfort studies 22 2.6.1 Questionnaire surveys evaluating thermal perception 22 2.6.2 Existing intra-urban differences 25 2.6.3 Numerical experiments 29 2.6.4 Summary of outdoor thermal comfort research 33 iv 2.7 Outdoor thermal comfort research in Singapore 33 Chapter Study area and methods 38 3.1 Research approach 38 3.2 Background of Singapore 38 3.2.1 Climatology 38 3.2.2 Urbanization in Singapore 41 3.3 Study area 44 3.4 Field measurements 47 3.4.1 Soil measurements 51 3.4.2 Measurements for mean radiant temperature (MRT) 53 3.4.3 Air temperature and relative humidity measurements 55 3.5 Background of ENVI-met 58 3.6 Model configuration 61 3.6.1 Basic configurations 61 3.6.2 Local vegetation database 64 3.6.3 Other input parameters for model initialization 67 Chapter ENVI-met model evaluations 75 4.1 Introduction 75 4.2 Model evaluation of spatially-averaged Ta-2m 78 4.3 Evaluation of predicted Ta-2m at individual sensor locations 83 4.4 Discussion of model performance for Ta-2m 91 4.5 Evaluation of predicted MRT 94 4.6 Discussion of MRT model evaluation results 99 4.7 Thermal comfort conditions (PET) 104 Chapter Urban design: effects on micro- and bioclimate 109 v 5.1 Description of urban design scenarios 110 5.1.1 Albedo 110 5.1.2 Vegetation 111 5.1.3 Building heights 113 5.2 Influence of albedo 117 5.2.1 Near-surface air temperature (Ta-2m) 117 5.2.2 Mean radiant temperature (MRT) 122 5.2.3 Physiologically equivalent temperature (PET) 127 5.2.4 Summary and discussion of albedo scenarios 129 5.3 Influence of vegetation 131 5.3.1 Near-surface air temperature (Ta-2m) 131 5.3.2 Mean radiant temperature (MRT) 136 5.3.3 Physiologically equivalent temperature (PET) 140 5.3.4 Summary and discussion of vegetation scenarios 142 5.4 Influence of building heights 143 5.4.1 Near-surface air temperature (Ta-2m) 143 5.4.2 Mean radiant temperature (MRT) 148 5.4.3 Physiologically equivalent temperature (PET) 152 5.4.4 Discussion and summary of building height scenarios 155 5.5 Chapter Chapter summary 156 Summary and conclusions 158 6.1 Evaluations of ENVI-met 158 6.2 Effects of manipulating urban design variables 161 6.3 Final considerations 164 References 166 vi Appendix A Python shell script for data-mining 176 Appendix B Comparisons of domain averages with spatial averages from receptor data 177 Appendix C Sample input data for RayMan 179 Appendix D Wind vector maps for SIM 1-8 at 1500 hrs 182 Appendix E Spatial variation of absolute 2-m air temperature (Ta-2m) for SIM (BASE) at timings of peak and minimum Ta-2m 186 Appendix F Spatial variation of absolute mean radiant temperature (MRT) for Sim (BASE) at timing of peak MRT 187 Appendix G Average daytime mean radiant temperature (MRT) at seven receptor locations for scenarios discussed in Chapter 188 Appendix H Daily mean predicted wind speeds (u) at seven receptor locations for scenarios discussed in Chapter 190 vii List of Tables Table 2-1: Explanation of terms in the human heat balance model 15 Table 2-2: Grades of thermal stress and perception in relation to predicted mean vote (PMV) and physiologically equivalent temperature (PET) 20 Table 2-3: Summary of key themes in questionnaire-survey studies from 2003 onwards for hot and humid environments 23 Table 2-4: Summary of the types of urban morphologies and thermal comfort parameters and indices used in key studies examining intra-urban thermal comfort differences 26 Table 2-5: Average daytime and evening values for the five thermal comfort indices in the four environments studied in Clarke and Bach (1971) 27 Table 2-6: Selected numerical studies that examine the influence of urban design variables on thermal comfort 30 Table 3-1: Average monthly wind direction in Singapore, and the monsoon and inter-monsoon periods 40 Table 3-2: Variables measured in the field campaigns and their respective uses 47 Table 3-3: Characteristics of the seven locations (R1 to R7) chosen for air temperature and relative humidity measurements 49 Table 3-4: Instrumentation and accuracy for variables measured at respective sensor locations 50 Table 3-5: Corrections applied to the Vaisala HMP45C (R1) and Onset HOBO (R2-R7) sensors based on inter-sensor comparisons 56 Table 3-6: Tree species, common name, distribution, average height (ztree-avg) and average leaf area density (LAI) from NParks database (Tan & Angelia, 2010) of trees in model domain 65 Table 3-7: No of rain days and rainfall amounts during the months of Oct 2012, Jan, Feb and July 2013 71 Table 3-8: Input parameters reflecting local soil and meteorological conditions as well as typical building characteristics for the first four simulations (SIM 14) 73 Table 3-9: Same as Table 3-8 but for SIM 5-8 74 viii Table 4-1: Summary of maximum and minimum values for observed (Omax and Omin) and predicted (Pmax and Pmin) mean (average of all measurement/receptor locations) 2-m air temperature (Ta-2m), diurnal ranges (Omax-min and Pmax-min) and diurnal averages (Oavg and Pavg) with standard deviations (σo and σp) for SIM 1-8 81 Table 4-2: Difference measures of predicted and observed day- and nighttime mean (average of all measurement/receptor locations) 2-m air temperature (Ta2m) RMSE = root mean squared error, RMSEs = systematic RMSE, RMSEu = unsystematic RMSE, MBE = mean bias error, MAE = mean average error, r2 = coefficient of determination (dimensionless) and d = index of agreement (dimensionless) 82 Table 4-3: Difference measures of predicted and observed day- and nighttime 2-m air temperature at the seven receptor locations RMSE = root mean squared error, RMSEs = systematic RMSE, RMSEu = unsystematic RMSE, MBE = mean bias error, MAE = mean average error, r2 = coefficient of determination (dimensionless) and d = index of agreement (dimensionless) 90 Table 4-4: Summary of maximum, minimum and standard deviations of observed and predicted mean radiant temperatures (MRT) at 1.1 m above ground at R1 for SIM 1-8 O-MRT = observed MRT, P-MRT = predicted MRT, σ-O = standard deviation of O-MRT and σ-P = standard deviation of P-MRT 96 Table 4-5: Difference measures of predicted and observed mean radiant temperature (MRT) at 1.1 m above the ground at R1 for SIM 1-8 RMSE = root mean squared error, RMSEs = systematic RMSE, RMSEu = unsystematic RMSE, MBE = mean bias error, MAE = mean average error, r2 = coefficient of determination (dimensionless) and d = index of agreement (dimensionless) 98 Table 4-6: Projection factors at different sun elevations (γ) calculated using ENVI-met (fp-ENVI) and VDI guidelines (fp-VDI) 102 Table 4-7: Summary of average predicted daytime physiologically equivalent temperature (PETmean) with standard deviations for all simulations (SIM 1-8) and locations (R1-R7) 106 Table 5-1: Albedo simulation scenarios and the alterations made to roof and wall albedo values (α-roof and α-wall, respectively) 111 Table 5-2 : Characteristics of BASE and the seven vegetation scenarios 112 Table 5-3: Proportion of plan area allotted to building footprint, tree and grass cover for three building height scenarios 114 Table 5-4: Geometric characteristics of the seven receptor locations used for BASE and three building height scenarios 115 Table 5-5: Mean daytime 1.1-m mean radiant temperature differences (ΔMRT) between BASE and the five albedo scenarios at seven receptor locations 123 ix Appendix B Comparisons of domain averages with spatial averages from receptor data To verify if the receptors adequately represented the model domain, the average Ta-2m from all grid cells (unoccupied by buildings) across the entire model domain (Dmean) was compared to Ta-2m averaged from the seven receptors (RCmean), for all eight simulations Figure B-1 indicates that there is very good agreement between the two Pearson product-moment correlation coefficients (r) were computed to assess the relationship R is greater than 0.999 for all simulations, and RCmean and Dmean deviate little from the 1:1 lines drawn on the plots in Figure B-1 This indicates that RCmean does predict Dmean very well and is a reliable representation of average domain-wide conditions The maximum difference (Diffmax) between RCmean and Dmean for each simulation's time series is shown in Table B-1 For further verification, independent samples t-test between RCmean and Dmean were conducted for each of the eight simulations For all eight simulations, there were no significant differences between Dmean and RCmean, where p-values >0.7 for all simulations (Table B-1) This suggests that the seven receptor locations were well chosen so the RCmean is not skewed towards any particular microclimate 177 SIM SIM SIM SIM SIM SIM SIM SIM 34 32 30 28 26 34 32 30 D mean°C 28 26 34 32 30 28 26 34 32 30 28 26 26 28 30 32 34 26 28 30 32 34 R C mean°C Figure B-1: Scatter plots of RCmean (averages derived from seven receptors) and Dmean (averages derived from all grid cells in model domain that are unoccupied by buildings) for SIM 1- Black dashed line = 1:1 line Table B-1: Summary of maximum differences (Diffmax) between Dmean and RCmean, and ttest statistics for each of the eight simulations (SIM 1-8) df = degrees of freedom Function SIM SIM SIM SIM SIM SIM SIM SIM Diffmax (°C) 0.13 -0.11 -0.07 0.12 -0.09 0.12 0.08 -0.07 t-value 0.061 0.20 0.061 0.19 0.17 0.38 0.087 0.06 94 94 94 94 94 94 94 94 0.87 0.84 0.95 0.85 0.87 0.71 0.93 0.95 df p-value 178 Appendix C Sample input data for RayMan Sample data for input to Rayman V 1.2 for calculating PET is given in Table below The input data for this particular example is based on ENVImet output for location R2, for the MIX scenario (see Chapter for more details) Note that the columns are strictly ordered when used for input to RayMan and must follow the sequence shown in Figure C-1 to avoid errors in the model output Table C-1: Sample input data for location R2 in the MIX scenario Date 28/7/2013 28/7/2013 28/7/2013 28/7/2013 28/7/2013 28/7/2013 28/7/2013 28/7/2013 28/7/2013 28/7/2013 28/7/2013 28/7/2013 28/7/2013 28/7/2013 28/7/2013 28/7/2013 28/7/2013 28/7/2013 28/7/2013 28/7/2013 28/7/2013 28/7/2013 28/7/2013 28/7/2013 28/7/2013 28/7/2013 28/7/2013 29/7/2013 29/7/2013 Time 10:00 10:30 11:00 11:30 12:00 12:30 13:00 13:30 14:00 14:30 15:00 15:30 16:00 16:30 17:00 17:30 18:00 18:30 19:00 19:30 20:00 20:30 21:00 21:30 22:00 22:30 23:30 0:00 0:30 Ta (°C) 30.08 30.31 30.55 30.81 31.05 31.32 31.52 31.65 31.65 31.60 31.51 31.33 31.12 30.88 30.60 30.28 29.92 29.66 29.44 29.25 29.08 28.93 28.78 28.64 28.52 28.39 28.28 28.16 28.05 RH (%) 55.59 54.40 53.49 52.73 52.07 51.44 50.93 50.48 50.20 49.96 49.76 49.71 49.72 49.81 50.12 50.77 51.55 51.77 51.86 51.87 51.86 51.85 51.94 52.03 52.14 52.28 52.44 52.64 52.88 u (ms-1) 0.45 0.46 0.46 0.46 0.47 0.47 0.47 0.48 0.48 0.48 0.48 0.48 0.48 0.48 0.48 0.48 0.48 0.48 0.47 0.47 0.47 0.47 0.46 0.46 0.46 0.46 0.46 0.46 0.45 MRT (°C) 67.18 66.18 58.62 58.01 57.71 58.08 59.02 60.16 49.86 49.67 47.85 39.70 34.00 32.27 29.91 25.92 22.91 22.44 22.09 21.81 21.57 21.37 21.19 21.02 20.88 20.74 20.62 20.50 20.39 (Continued on next page) 179 Continued from Table C-1 29/7/2013 29/7/2013 29/7/2013 29/7/2013 29/7/2013 29/7/2013 29/7/2013 29/7/2013 29/7/2013 29/7/2013 29/7/2013 29/7/2013 29/7/2013 29/7/2013 29/7/2013 29/7/2013 29/7/2013 1:00 1:30 2:00 2:30 3:00 3:30 4:00 4:30 5:00 5:30 6:00 6:30 7:00 7:30 8:00 8:30 9:00 27.84 27.74 27.65 27.56 27.48 27.40 27.32 27.25 27.18 27.11 27.04 26.98 26.97 27.26 27.76 28.37 29.00 53.39 53.63 53.84 54.04 54.23 54.41 54.59 54.76 54.93 55.09 55.24 55.39 55.39 54.94 54.35 53.67 52.67 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.46 0.46 0.46 0.46 20.19 20.10 20.01 19.93 19.85 19.78 19.70 19.63 19.57 19.50 19.44 19.38 23.08 47.62 53.41 56.65 67.42 The user interface of RayMan v 1.2 software is shown in Figure C-1 Users have to specify the geographic data and general biometric data of subjects being studied For continuous time-series data, the “Date and time” and “Current data” sections can be ignored Time series data (*txt) files are input into the software in a separate window shown in Figure C-3 180 Figure C-1: Main user interface for RayMan software, where basic geographic and biometric data may be specified Instantaneous calculations may be done using this interface For input of time-series data, the interface in Figure C-2 is used Figure C-3: Input window for uploading.*txt format data files to the software This step is necessary for time-series data 181 Appendix D Wind vector maps for SIM 1-8 at 1500 hrs SIM 1, prevailing wind direction: 225° Receptors Windspeed, u 0.50 m/ s 1.00 m/ s 1.50 m/ s 2.00 m/ s 2.50 m/ s N 100 m SIM 2, prevailing wind direction: 0° Receptors Windspeed, u 0.50 m/ s 1.00 m/ s 1.50 m/ s 2.00 m/ s 2.50 m/ s N 100 m Figure D-1: Wind vector maps showing direction and speed within the model domain at height of 1.1 m for (top) SIM and (bottom) SIM at 1500 hrs Prevailing wind directions are noted above each map 182 SIM 3, prevailing wind direction: 0° Receptors Windspeed, u 0.50 m/s 1.00 m/s 1.50 m/s 2.00 m/s 2.50 m/s N 100 m SIM 4, prevailing wind direction: 45° Receptors Wind speed, u 0.75 m/s 1.50 m/s 2.25 m/s 3.00 m/s 3.75 m/s N 100 m Figure D-2: Same as Figure D-1, but for (top) SIM and (bottom) SIM 183 SIM 5, prevailing wind direction: 45° Receptors Wind speed, u 0.75 m/s 1.50 m/s 2.25 m/s 3.00 m/s 3.75 m/s N 100 m SIM 6, prevailing wind direction: 200° Receptors Windspeed, u 0.50 m/s 1.00 m/s 1.50 m/s 2.00 m/s 2.50 m/s N 100 m Figure D-3: Same as Figure D-1, but for (top) SIM and (bottom) SIM 184 SIM 7, prevailing wind direction: 180° Receptors Windspeed, u 0.50 m/s 1.00 m/s 1.50 m/s 2.00 m/s 2.50 m/s N 100 m SIM 8, prevailing wind direction: 180° Receptors Windspeed, u 0.50 m/s 1.00 m/s 1.50 m/s 2.00 m/s 2.50 m/s N 100 m Figure D-4: Same as Figure D-1, but for (top) SIM and (bottom) SIM 185 Appendix E Spatial variation of absolute 2-m air temperature (Ta-2m) for SIM (BASE) at timings of peak and minimum Ta-2m Figure E-1: Spatial variability of simulated 2-m air temperature (Ta-2m) and wind flow throughout the model domain at (top) 1400 hrs and (bottom) 0600 hrs, for SIM (BASE) 186 Appendix F Spatial variation of absolute mean radiant temperature (MRT) for Sim (BASE) at timing of peak MRT Receptors 100 m Figure F-1: Spatial variability of ENVI-met simulated mean radiant temperature (MRT) at 1500 hrs, which is the timing of peak MRT, for SIM (BASE) 187 Appendix G Average daytime mean radiant temperature (MRT) at seven receptor locations for scenarios discussed in Chapter Table G-1: ENVI-met predicted average daytime 1.1-m MRT (°C) and standard deviations at receptor locations for BASE, five albedo (Table 5-1), seven vegetation (Table 5-2) and three building height scenarios (Figure 5-1) discussed in Chapter 5, for 28 July 2013 SIM Average daytime MRT (°C) R1 R2 R3 R4 R5 R6 R7 51.9± 14.4 57.4± 16.0 30.4± 6.4 54.9± 17.9 46.2± 18.3 57.8± 16.7 46.1± 16.1 CR.Med 51.8± 14.4 57.4± 15.9 30.4± 6.4 54.9± 18.0 46.2± 18.3 57.8± 16.7 46.1± 16.1 CR.Hi 51.8± 14.3 57.3± 15.9 30.4± 6.4 54.9± 18.0 46.2± 18.3 57.8± 16.6 45.9± 16.4 CW 54.5± 15.3 63.6± 18.0 32.0± 6.9 60.9± 19.3 53.4± 19.6 61.9± 18 54.1± 18.1 MA 53.1± 14.8 60.3± 16.9 31.2± 6.7 57.7± 18.6 49.5± 18.9 59.7± 17.3 49.9± 17.0 HA 53.1± 14.8 60.3± 16.9 31.2± 6.7 57.7± 18.6 49.5± 18.9 59.7± 17.3 49.8± 17.0 NT 53.1± 13.1 60.6± 16.7 63.3± 16.4 55.5± 18.0 49.6± 21.0 59.2± 16.9 48.5± 17.5 GR 51.7± 14.7 56.4± 16.3 30.4± 6.5 54.3± 17.8 45.3± 18.1 56.5± 16.9 46.1± 16.1 TC9.1 51.8± 14.4 57.3± 16.1 30.4± 6.4 53.3± 17.0 46.0± 18.2 55.8± 16.2 46.0± 17.5 TC12.5 51.8± 14.4 57.3± 16.1 30.4± 6.4 54.0± 17.9 46.1± 18.2 55.1± 16.1 46.0± 17.5 ST 53.0± 13.1 57.9± 16.1 33.5± 8.0 55± 18.0 46.2± 18.3 57.8± 16.7 46.7± 17.4 MT 52.7± 13.4 56.6± 15.7 32.8± 7.7 54.0± 17.5 46.6± 18.8 56.2± 15.9 46.8± 17.4 TT 51.9± 14.3 54.6± 14.9 30.4± 6.4 52.3± 16.3 46.4± 18.9 54.7± 15.2 46.4± 17.2 BASE Albedo Vegetation (Continued on next page) 188 Continued from Table G-2 Building heights R1 R2 R3 R4 R5 R6 R7 B.2z 49.0± 16.2 52.3± 16.5 29.9± 6.5 52.5± 16.4 43.1± 17.4 53.4± 17.6 42.6± 14.6 B.25 44.4± 17.1 44.5± 13.8 29.5± 6.4 51.3± 16.6 40.0± 16.6 49.5± 16.6 40.3± 13.8 MIX 44.6± 17.1 47.2± 15.4 46.6± 21 51.5± 16.5 41.9± 18.3 51.6± 17.5 42.1± 16.3 189 Appendix H Daily mean predicted wind speeds (u) at seven receptor locations for scenarios discussed in Chapter Table H-1: Diurnal mean 1.1-m wind speeds (u) and standard deviations predicted by ENVI-met at seven receptor locations for BASE, five albedo (Table 5-1), seven vegetation (Table 5-2) and three building height scenarios (Figure 5-1) as discussed in Chapter 5, for 28 July 2013 SIM Daily mean u (ms-1) R1 R2 R3 R4 R5 R6 R7 0.6± 0.04 0.27± 0.02 0.81± 0.05 0.19± 0.03 1.16± 0.09 0.68± 0.05 0.68± 0.06 CR.Med 0.60± 0.03 0.27± 0.02 0.81± 0.05 0.20± 0.03 1.15± 0.09 0.67± 0.04 0.69± 0.06 CR.Hi 0.59± 0.03 0.26± 0.02 0.81± 0.05 0.20± 0.03 1.15± 0.09 0.67± 0.04 0.72± 0.06 CW 0.60± 0.04 0.27± 0.02 0.81± 0.05 0.19± 0.03 1.17± 0.1 0.68± 0.05 0.69± 0.07 MA 0.60± 0.03 0.27± 0.02 0.81± 0.05 0.20± 0.03 1.16± 0.09 0.68± 0.05 0.69± 0.06 HA 0.60± 0.03 0.26± 0.02 0.81± 0.05 0.20± 0.03 1.16± 0.09 0.68± 0.05 0.69± 0.06 NT 0.59± 0.04 0.27± 0.03 1.64± 0.06 0.22± 0.03 1.40± 0.1 0.69± 0.04 0.76± 0.07 GR 0.51± 0.04 0.24± 0.02 0.82± 0.05 0.15± 0.03 1.19± 0.1 0.71± 0.05 0.69± 0.06 TC9.1 0.6± 0.04 0.27± 0.02 0.8± 0.05 0.20± 0.03 1.16± 0.1 0.61± 0.05 0.73± 0.07 TC12.5 0.59± 0.04 0.26± 0.02 0.8± 0.05 0.20± 0.03 1.14± 0.09 0.59± 0.05 0.73± 0.07 ST 0.58± 0.04 0.26± 0.03 0.78± 0.07 0.20± 0.03 1.16± 0.1 0.67± 0.05 0.72± 0.07 MT 0.57± 0.04 0.27± 0.02 0.79± 0.06 0.19± 0.03 1.19± 0.1 0.67± 0.05 0.73± 0.07 TT 0.56± 0.04 0.26± 0.02 0.79± 0.05 0.18± 0.03 1.17± 0.1 0.66± 0.05 0.70± 0.07 BASE Albedo Vegetation (Continued on next page) 190 Continued from Table H-2 SIM Daily mean u (ms-1) R1 R2 R3 R4 R5 R6 R7 B.2z 0.54± 0.02 0.20± 0.03 0.95± 0.05 0.43± 0.03 0.97± 0.11 0.29± 0.01 0.66± 0.03 B.25 0.06± 0.03 0.46± 0.01 1.06± 0.05 0.53± 0.05 0.84± 0.1 0.19± 0.02 0.55± 0.03 MIX 0.09± 0.03 0.46± 0.01 0.26± 0.01 0.52± 0.04 0.88± 0.11 0.14± 0.02 0.59± 0.03 Building heights 191