Spatio temporal dynamics of the urban heat island in singapore 2

60 3.4 Data quality control The task of handling the data quality is seldom an easy one. In most climate research fields, the main reason for data quality control is to ensure that the dataset is homogeneous (Aguilar et al., 2003). The appeal behind ensuring homogeneity is that it removes any “noise” from sources that may potentially create non-climatic biases in the data. In the case of urban climate studies, there is a fine distinction between urban influences and other influences. The homogeneity in this case refers to climate data that represent variation due to urban development and possibly some indirect causation, and the elimination of other inhomogeneities, such as artefacts created by lagged events. The idea is to study the impact of urban development on an otherwise undeveloped location. Errors may also occur due to other reasons, such as instrument error, human error or spikes during data transfer or from external non-climatic forces (e.g. fires as in the case of S11 or a warm vehicle parking next to the sensor). Instrument calibration Calibrations across all sensors were done prior to mounting in the field in February 2008. The purpose was to ensure that deviations between sensors did not exceed acceptable margins. In July 2009, the sensors were taken down for another session of calibration. Calibrations are done by placing all sensors in a homogeneous environment in close proximity (e.g. Figure 3.19). In both calibrations, agreement across sensors was acceptable as differences were < ±0.1◦ C, which is less than the accuracy level of the sensor (±0.2◦ C). 62 Data post-processing While the determination of erroneous data often requires subjective judgement, the large volume of data in this study means that an objective method is first needed to systematically scan for parts of data where errors may occur. First a quick sweep of unlikely data points (T > 40◦ C and T < 16◦ C), to remove unrealistic extreme values (with reference to Singapore). Next, a despiking approach was used. As air temperature is not normally distributed, the distance of three SDs away from the mean was used as the lower bound and four SDs away from the mean was deemed the upper bound. All values exceeding the bounds were scrutinised visually for likelihood of being erroneous. A second net was set by comparing max values with 99th percentile values to determine isolated outliers. Scatter plots of two closely-related stations were used to identify any possible errors discussed above. Pearson correlation is used to determine reference sites that are highly correlated and to form a basis for comparison (Boissonnade et al., 2002; Tayan¸c et al., 1998). A correlation matrix was calculated and “best pairs” (see Figure 3.20) were selected based on the correlation coefficient (R value). These pairs were then plotted as scatter plots to identify any obvious non-conformities. Figure 3.21 shows an example of realistic values that escape the first net but become obvious when scatter plots of best pairs are plotted. In this case, some discretion has to be used as each pair has different acceptable levels of scatter. In Figure 3.21a it is clear that the stray values at the bottom of the large spread are artefacts rather than actual occurrences. These are most likely measurements made when sensors were already unmounted (e.g. in a car) but mistakenly still logging due to human error. As such, they are removed and Figure 3.21b shows the post-correction scatter plot. 65 0.12 RMSE between pre− and post−correction 0.10 0.08 0.06 0.04 0.02 St_01 St_02 St_03 St_04 St_05 St_06 St_07 St_08 St_09 St_10 St_11 St_12 St_13 St_14 St_15 St_16 St_17 St_18 St_19 St_20 St_21 St_22 St_23 St_24 St_25 St_27 St_28 St_29 St_30 St_31 St_32 St_33 St_34 St_36 St_37 St_38 St_39 St_40 St_41 St_42 St_43 St_44 St_45 St_46 0.00 Station code Figure 3.23: RMSE between pre- and post-corrected values for each station. 3.5 3.5.1 Selection of urban parameters Urban cover and fabric Built-up ratio (BUP) and vegetation ratio (VP) Satellite imagery is a common choice for delineating urban land cover types. Two main methods are used. Spectral analysis of satellite imagery (automated classification) or classification by eye (supervised classification). A popular algorithm for classification is the NDVI (e.g. Botty´an et al., 2005): N DV I = N IR − R N IR + R (3.5) where NIR = spectral signature of near infrared band and R = spectral signature of the red band. 66 Figure 3.24: Mosaicked satellite images used for land use classification. Source: Microsoft Virtual Earth. For this study, satellite imagery is used with supervised classification but not the NDVI algorithm. Part of the reason is the unavailability of high-resolution NIRband imagery. A panchromatic SPOT 5 image with 2.5 metre resolution (Figure 1.2) is used together with DigitalEye satellite images available on Microsoft Virtual EarthT M , digitally mosaicked for this purpose (Figure 3.24). Ground-truthing was conducted to ensure that no major land-use changes had occurred around the stations. After the entire study area has been classified, the percentages are calculated for the radii of 100 and 500 metres around each station (Appendix B). An example of the above can be seen in Figure 3.25a and an example of how the percentages are obtained by pixel counts is available in Figure 3.25b. Built-up areas include buildings, road surfaces, parking spaces and other man-made surfaces. Vegetation includes forests, parks, field, grass patches and other vegetated natural surfaces, excluding bare soil and water bodies. 67 (a) (b) Figure 3.25: (a) 100 metres (inner) and 500 metres (outer) radii from S02, and (b) calculation of land use percentages at 500 metre for S36. 68 3.5.2 Urban structure Sky view-factor (SVF) Similar to H/W and zH /W ratios (discussed later) in attempting to convey some information on the geometry of an urban canyon, the sky-view factor (SVF) quantifies the fraction of radiation emitted by one surface and captured by another (Oke, 1987; Grimmond et al., 2001). This has strong bearing on the L↑ values. Two main methods are used to determine SVF. The first method is to use complex geometrical calculations to provide view-factors given the known dimensions of the canyon (e.g. Oke, 1981; Johnson and Watson, 1984). GIS software can be used to perform these calculations, although they may not model vegetation well or provide an accurate results when dealing with complex geometry. A second method is to use fish-eye optical equipment. Grimmond et al. (2001) discuss the use of a digital camera with fish-eye optical sensor and the LI-COR LAI-2000 Plant Canopy Analyzer. This is an empirical method which 180◦ (studies have employed sensors from 140◦ to 189◦ ) hemispheric images obtained from full circular fisheye lenses. The added advantage of fisheye imagery is the ability to account for the sky-view for 360◦ around the point where the photograph is taken, and 180◦ to the axis of the lens, without the need for many mathematical assumptions. In this study, a Fujifilm IS Pro full-frame DSLR camera body is used with a Sigma 4.5mm F2.8 EX DC Circular Fisheye HSM lens (Figure 3.26). The lens has a documented view-angle of 180◦ , in line with the recommendations by Grimmond et al. (2001). The lens also has a quantifiable area/angle projection which makes it suitable for scientific purposes, in this case, areal calculations. For consistency, images are taken with the camera body mounted on a tripod, at a height of 1.2 metres. A fluid leveller is also used to ensure that the camera body is level when 69 images were taken. Figure 3.26: Top left: A Sigma 4.5mm F2.8 EX DC Circular Fisheye HSM lens mounted on the Fujifilm IS Pro full-frame DSLR. Top right: A flash hotshoe bubble leveller used to level the camera axis. Bottom: a tripod. Images were processed using the Gap Light Analyzer (GLA) software written by the Institute of Ecological Studies and Simon Fraser University (Figure 3.27). A first round of processing was done to convert the image into a dual-tone image representing “sky” and “non-sky” pixels. The sky view-factor is then obtained as a proportion of pixel area that is classified as “sky”, noting that pixel area has already been weighted based on the projection. The fish-eye images taken for the stations in this study can be found in Appendix D. 70 Figure 3.27: User interface of the Gap Light Analyzer (GLA) version 2.0 by the Institute of Ecological Studies and Simon Fraser University. Height-to-width ratio (H/W) and roughness height-to-width ratio (zH /W ratio) The height-to-width ratio (H/W) of an observation site is often used to characterize canyon geometry. The ratio of the height of sides of an urban canyon to its width provides this value. As with the sky-view factor (SVF), the H/W is often cited as a factor that promotes heat retention in urban areas. High H/W ratios indicate tall and tightly-packed structures, restricting the degree to which the sky is open to the surroundings of a site (Oke, 1982, 2006). As such, the H/W is a parameter which provides an indication of “street canyon” dimensions that influence the ability of urban areas to radiate heat. In urban climate zone (UCZ) site description scheme by Oke (2006), the generic “aspect ratio” is referred to as zH /W. While it is conceptually similar, the zH /W differs from the H/W in that vegetation is considered part of the canyon geometry and is included in the geometric calculations. This differs from many 71 common uses of height-to-width ratio measurements which take into consideration only buildings and structures in the calculations (e.g. Goh and Chang, 1999; Chow and Roth, 2006), thereby not giving “rural” areas a roughness value. According to Oke, vegetation is included in the calculation of aspect ratio because it has some form of influence on the flow regime and thermal properties such as roughness length, shading and dissipation of long-wave radiation (Oke, 2006). Roughness height-to-width ratio will be the term used to refer to zH /W ratio in this report. One challenge in determining both ratios is the wide-ranging urban configurations of stations in this study. As we are also interested in intra-urban differences and UHI in open spaces, not all of which have distinct urban canyons, a special method was devised to obtain the ratios. Ratios are measured along transects in 4-axes (N-S, E-W, NE-SW and NW-SE) and then averaged to provide an overall 8-directional mean height-to-width ratio (Figures 3.28 and 3.29). For each of the transects, to cater to irregular canyons and non-canyons, a mean height-to-width ratio is used and vertical surfaces up to 100 metres horizontal distance from the sensors are considered (Figure 3.28). Note that the height-towidth parameters used in this study are the 8-directional mean values and the individual transects are merely used to determine them. The same approach is used to obtain zH /W with the exception that vegetation cover is also considered in the height and width calculations. The zH /W tends to be considerably higher than H/W ratios for densely-vegetated areas (e.g. forests and parks); slightly higher for less vegetated areas (e.g. residential land use); and identical in areas without tall vegetation (e.g. open fields and open car parks). 72 Figure 3.28: Determination of height-to-width ratio for each transect. 74 Chapter 4 Results and Discussion 4.1 Determining the basis for comparison The main goal of Chapter 4 is to identify and describe distinct patterns of variation in the empirical data collected. The definitions used in this Chapter will follow closely to the discussions in Section 2.1. Times listed in this section will refer to local standard time (i.e. GMT +8) unless otherwise stated. Time interval for the air temperature and UHI calculations is 10 minutes unless otherwise stated. Definitions of UHI-related dependent variables Several calculations of UHI are employed. The term UHIraw will refer to the difference between a value measured at a particular site and the chosen reference site (S16) at a specific point in time, i.e. Tu − Tr , excluding hours which are windy, cloudy and/or wet (i.e. when Φm = 1 and Φw = 1). UHImax (“max” in lower-case) will refer to the absolute maximum UHI intensity under dry conditions for a given time interval (e.g. maximum UHI intensity possible at 21:00 hrs). Thus, UHImax calculations is similar to UHIraw except for the added criterion of Φa = 1, meaning no heavy cloud or rainfall events should have taken place at 75 any point in the day. UHIM AX (upper-case) will refer to the absolute maximum UHI intensity (UHIraw or maxmax ) measured for any station across all time periods. UHIraw is mainly used to provide results reflective of actual conditions and to account for seasonal and inter-annual weather variations. Where an ideal condition is required, e.g. the determination of UHIM AX , UHImax values will be used. Calm, clear nights with no antecedent conditions (defined later) will provide a better indication of the maximum possible influence of urban development alone. Minimum and maximum UHIraw (or UHImax ) are defined as the smallest and largest value (respectively) of UHIraw (UHImax ) for each station across the entire study period, unless a specific period is stated. For example, monthly maximum UHIraw is the maximum UHI intensity in each month of the year. Their inclusion allows evaluation of the influence of various factors on extreme values. A subscript, (t), will be used to refer to the number of hours after sunrise during which a certain UHI event occurs, e.g. maximum UHIraw(t) hourly ensemble would mean the time of peak for ensemble hourly UHIraw(t) . As was already previously established, daytime and nocturnal UHI are influenced differently, therefore the nocturnal mean UHIraw (NM UHIraw ) and the daytime mean UHIraw (DM UHIraw ) are selected as dependent variables too. Artefacts in UHI calculations due to asynchronous rainfall events UHI intensities are calculated from values of two different stations. Synoptic weather conditions (Φw ) affect UHI but they do not always occur simultaneously and at equal intensities across all stations. This increases the complexity of normalizing the values as non-relevant factors may lead to misleading results (as discussed in Section 2.1). For example, a rainfall event that occurs asymmetrically over one 77 Table 4.1: Rainfall distribution across meteorological stations on 7 July 2010 at 13:00 hrs. Note that Tengah Meteorological Station is located approximately 2 kilometres east of the reference station (S16) in north-western Singapore (see Appendix A). Meteorological Station Tengah Meteorological Station Changi Meteorological Station Seletar Meteorological Station Paya Lebar Meteorological Station Sembawang Meteorological Station Rainfall (mm) on 7 July 2010 at 13:00 hrs 35 0 0 0 0 Filtering process In order to filter the dataset for the effects of Φw and Φm , hourly cloud and rainfall maps for the region were obtained for the entire study period from the Wundermap radar map repository (http://www.wunderground.com/wundermap/ ). A shell script using the ImageMagick image processing library was written to automate the cropping of these maps to the extent of the study area. The script was then used to identify days with heavy cloud cover and rainfall over Singapore. These were corroborated using hourly rainfall and wind data from five meteorological stations in Singapore, namely, Tengah, Changi, Seletar, Paya Lebar and Sembawang. As the meteorological stations do not have a good spatial coverage, the radar map plays an important role in identifying any periods of heavy cloud cover or rain at any location in the study area. In the case of UHIraw , to filter for effects of wetted surface (Φm = 1), and heavy cloud cover (Φw = 1), data points that fall within two hours from the occurrence of rainfall (hourly rainfall of >0 mm) and heavy cloud cover events that appear on the radar, are filtered out. Isolated data points spanning less than four consecutive hours are also removed as they are deemed to be unrepresentative. The calculation of UHImax also uses the same procedure but has added constraints: only 78 nocturnal values (19:00 to 06:50 hrs) are considered to remove effects of differential shading which is present during daytime. Also, for UHImax , only dry, clear and calm nights (Φw = 1, Φm = 1) with an uninterrupted times series throughout the entire night (72 points at a 10-min interval over 12 hours) are regarded as acceptable, in order to filter data points that are affected by antecedent conditions (Φa = 1). A calm night is defined as a day when the Changi Met. Station recorded mean daily wind speeds lower than 2 ms−1 . The above filtering strategies also remove artefacts created due to asynchronous rainfall events as they remove data points during these events. After filtering, the result is that for all years, less than a third of period fulfil the UHIraw criteria. As for UHImax , some years have as low as 17.3% of nights valid (Table 4.2). Table 4.2: Number of hours without rainfall or heavy clouds meeting the conditions of UHIraw and “ideal” nights meeting the conditions of UHImax . Note that there were only 11 months of observations for 2008 and 6 months of observations for 2011. Study period 2008* 2009 2010 2011* Hours without rain 2143 2909 2793 1272 “Ideal” nights 80 63 75 29 The basis for comparison The variance in thermal conditions propagates across dimensions of space and time. These are fundamental dimensions as the empirical data collected is spread across a large number of stations (with varying spatial configurations and locations), and across a relatively long period of time (with annual, seasonal, diurnal cycles, among others). These spatial and temporal considerations will be the basis for comparisons. It is not possible for all known parameters to be controlled 79 as the study is not a lab-based experiment and is thus subject to various uncontrollable factors. However, where possible, comparisons will be made between subsets of data where differences in all other parameters are minimized, apart from the parameter being studied. Where high levels of accuracy are necessary, specific case studies or subsets of stations are chosen for analysis. However, as some of the methods require a large number of sample points for increased utility (such as in spatial interpolation), stations with less data are sometimes also used in the analyses (and so stated). While analyses that employ data from all stations tend to be those that provide description on the mean behaviour across all stations, care must be taken interpreting the exact values from these results as biases may be present. 4.2 4.2.1 Descriptive statistics Statistical summary for air temperature measurements Maximum and minimum air temperatures recorded Solar noon occurs around 13:00 hrs local time and peak air temperature values are often recorded within a couple of hours after it. Sunrise occurs ±20 minutes from 07:00 hrs and cooling throughout the night ensures that lowest temperatures are measured just before sunrise. Across all weather conditions, with no filtering of weather conditions, the maximum air temperature measured at a 10-min interval from a single station (S11) was 36.59◦ C at 15:00 hrs on March 10, 2010. The month of occurrence is somewhat surprising as peak air temperatures are typically expected in the months between April and June. The time of the day, however, is within expectation as air tem- 80 perature typically peaks between 13:00 to 15:00 hrs (see Section 4.3.1). The mean air temperature across all stations available for the same time interval (N = 28) was 33.25◦ C with a low SD (σ) of 1.3◦ C suggesting that measurements across all stations are in agreement. The minimum air temperature was measured as 20.08◦ C at 06:20 hrs on 19 January 2009 at the rural site S23. January is typically the coolest month of the year and air temperatures tend to reach their minimum just before sunrise. The mean air temperature across all stations (N = 29) for the same time interval was 23.41◦ C where σ = 1.4◦ C, a low value, thus also suggesting agreement across all stations. Mean, minimum and maximum air temperatures for each station Over a third of the stations had a mean air temperature within the range of 28.0◦ C to 28.5◦ C, with only nine stations having means below 27.0◦ C (Figure 4.2). Minimum air temperature measurements are in greater agreement with more than 80% of stations having recorded temperatures within the small range of 21.5◦ C to 23.0◦ C. The most populated 0.5◦ C bin is 22.0◦ C to 22.5◦ C. Maximum air temperatures appear to be distributed normally while mean and minimum air temperatures are skewed towards higher values. The most common interval was 34.5◦ C to 35.0◦ C. A single station (S09) had a maximum below 32.5◦ C but this is likely to be attributed to the short period of time for which its data were available (approximately 100 days). Station-specific summaries for air temperature measurements are available in Table 4.3. Maximum air temperatures of industrial areas (S02: 36.29◦ C and S12: 36.36◦ C) are among the highest measured. High maximum values are also 81 found at stations surrounded by vegetation (S10: 36.13◦ C and S11: 36.59◦ C), at low-rise urban sites (S05: 35.87◦ C, S19: 36.10◦ C and S45: 35.92◦ C) and at high-rise residential (>30 storeys) estates (S17: 36.19◦ C and S38: 35.85◦ C). Interestingly, among the stations located in the core of the city, the stations located in high-rise areas have considerably lower maxima (S07: 34.4◦ C and S22: 34.77◦ C) as compared to those found in low-rise areas (S24: 35.19◦ C and S31: 35.67◦ C). However, the same cannot be said of their mean air temperatures (across all weather conditions and available periods) as the difference between the four stations do not exceed 0.5◦ C, with S22 having the highest mean at 28.75◦ C. As maximum air temperatures are expected in the daytime, shading by high-rise building is the likely cause of lower maxima of the high-rise parts of the city. In fact, maximum air temperatures of stations in the rural north-west (S16: 35.42◦ C, S23: 35.40◦ C and S28: 35.57◦ C) are comparable with those of low-rise areas in the city centre. In terms of mean air temperature, the urban stations in and around the city centre have among the highest values (S07: 28.26◦ C, S18: 28.24◦ C, S22: 28.75◦ C, S24: 28.48◦ C, S31: 28.27◦ C, S40: 28.50◦ C, S42: 28.66◦ C, S44: 28.63◦ C and S46: 28.54◦ C). Another cluster of stations with high mean air temperatures are found in the south-eastern coast (S13: 28.55◦ C, S15: 28.28◦ C, S41: 28.52◦ C). This part of the island is located farther from the cooler rural north-west and central catchment, possibly a reason why the stations located here have a higher mean temperature as compared to stations in other parts of the island. For minimum temperatures, the result is less complicated as all four stations with a minimum of less than 21◦ C are found in rural or forested areas (S03, S16, 82 S23, S28 and S39). These same stations are also among those with the lowest mean air temperatures, which can be explained by high rates of evaporative cooling due to moisture availability. Min Mean Max Frequency 15 10 5 0 20 21 22 23 24 25 26 27 28 29 32 33 34 35 36 37 Air Temperature (°C) Figure 4.2: Histograms of mean, maximum and minimum air temperature taking all stations (N = 44) and periods (N = 175795) into consideration. The relationship between mean, maximum and minimum air temperatures were analysed using regression analysis, with values paired at station-level (Figure 4.3). Results show that for each stations, maximum recorded air temperature has no distinct relationship with either mean air temperature (R2 = .0196) or minimum air temperature (R2 = .001). On the other hand, mean air temperature and minimum air temperature have a statistically significant relationship (p < 0.01; R2 = .762). The regression equation y = 6.7 + 0.96x suggests mean air temperature is consistently about 7 ◦ C higher than minimum air temperature for the given set of stations. 83 Table 4.3: Summary of air temperature measurements across all weather conditions for each station. Valid days refers to the number of days on record and % valid refers to the ratio of days on record for a specific station against the entire study period of 1221.8 days. Minimum and maximum values refer to single lowest and highest values recorded, respectively. Mean values are obtained by averaging all data points across all weather conditions. Min Mean Max Valid days % valid Min Mean Max Valid days % valid Min Mean Max Valid days % valid Min Mean Max Valid days % valid Min Mean Max Valid days % valid S01 S02 S03 S04 S05 S06 S07 S08 22.07 21.66 20.79 21.66 22.00 22.12 22.81 22.30 27.45 27.82 25.72 26.61 27.98 27.68 28.26 28.03 35.43 36.29 34.52 34.99 35.87 34.40 34.40 35.01 1012.80 719.42 988.24 829.82 380.43 235.60 1161.44 1158.77 82.96 58.93 80.95 67.98 31.16 19.30 95.14 94.92 S09 S10 S11 S12 S13 S14 S15 S16 22.13 21.47 21.93 22.31 22.64 22.33 22.02 20.18 27.26 26.93 27.24 28.18 28.55 28.22 28.28 26.18 32.03 36.13 36.59 36.36 35.09 34.56 35.19 35.42 101.76 1146.83 861.59 982.37 1156.90 1157.57 1159.62 1094.12 8.34 93.94 70.58 80.47 94.77 94.82 94.99 89.63 S17 S18 S19 S20 S21 S22 S23 S24 22.09 22.38 21.90 21.93 22.16 23.12 20.08 22.50 27.96 28.24 28.04 27.78 27.56 28.75 26.36 28.48 36.19 33.95 36.10 33.59 34.87 34.77 35.40 35.19 1167.14 247.32 757.19 252.45 1111.46 1172.25 1079.54 1192.35 95.61 20.26 62.03 20.68 91.05 96.03 88.43 97.67 S25 22.04 28.05 34.95 998.08 81.76 S34 21.85 26.89 34.21 1051.88 86.17 S27 S28 S29 21.76 20.90 22.28 26.45 26.65 28.03 33.14 35.57 34.72 23.33 896.08 1094.33 1.91 73.40 89.64 S36 S37 S38 22.01 22.63 22.99 27.44 28.11 28.00 34.33 35.34 35.85 514.03 812.15 812.02 42.11 66.53 66.52 S43 S44 Min 21.99 22.99 Mean 27.05 28.63 Max 35.21 34.89 Valid days 364.07 119.05 % valid 29.82 9.75 S30 S31 21.69 22.40 27.21 28.27 34.37 35.67 739.65 956.83 60.59 78.38 S39 S40 20.86 22.87 26.39 28.50 33.73 34.84 72.90 779.48 5.97 63.85 S45 S46 22.70 22.92 28.03 28.54 35.82 34.44 113.10 117.99 9.26 9.66 S32 22.02 27.33 35.51 891.02 72.99 S41 22.75 28.52 35.53 719.55 58.94 S33 22.57 28.09 34.69 235.04 19.25 S42 23.21 28.66 34.39 217.51 17.82 87 in and around the periphery of the central business district (CBD). The remaining three (two) are in extensive low-rise commercial or residential areas. Four (three) stations had a mean UHIraw (UHImax ) intensity close to zero (±0.5◦ C), all of which are located in rural or forested sites, i.e. in the shortlist for reference sites discussed in the previous section (S03, S23, S27 and S28). Min Mean Max Frequency 15 10 5 0 −4 −2 0 2 4 6 8 −4 −2 0 2 4 6 8 −4 −2 0 2 4 6 8 4 6 8 UHIraw (°C) Min Mean Max Frequency 15 10 5 0 −4 −2 0 2 4 6 8 −4 −2 0 2 4 6 8 −4 −2 0 2 UHImax (°C) Figure 4.6: Histograms showing mean, minimum and maximum UHIraw (top row) and UHImax (bottom row) values. N=43 as two stations have no data and the reference site is used in the calculation of UHI. 88 Table 4.4: Summary of calculated UHIraw intensities. Statistics are gathered across the entire study period (Feb 2008 to Jun 2011). Min Mean Max Valid days % valid Min Mean Max Valid days % valid Min Mean Max Valid days % valid Min Mean Max Valid days % valid Min Mean Max Valid days % valid S01 S02 S03 S04 S05 S06 S07 -2.87 -1.64 -4.44 -3.83 -2.12 -2.74 -3.79 1.56 1.93 -0.27 0.67 2.51 2.20 2.38 4.75 5.22 2.26 3.50 5.42 4.76 6.44 213.47 147.25 182.08 178.19 74.31 48.96 246.90 17.48 12.06 14.91 14.59 6.09 4.01 20.22 S09 S10 S11 S12 S13 S14 S15 -3.73 -3.01 -3.96 -2.89 -3.24 -3.41 -3.65 2.01 1.01 1.33 2.30 2.71 2.35 2.42 5.34 4.04 5.00 5.69 6.19 6.10 5.95 22.23 244.62 188.66 223.25 245.26 244.72 246.46 1.82 20.03 15.45 18.28 20.09 20.04 20.18 S18 S19 S20 S21 S22 S23 S24 -2.05 -3.16 -2.61 -3.09 -3.04 -1.97 -2.95 2.83 2.30 2.29 1.66 2.89 0.22 2.69 5.35 5.72 4.86 5.17 6.70 2.88 6.20 44.20 164.29 52.95 226.35 246.56 226.88 255.70 3.62 13.46 4.34 18.54 20.19 18.58 20.94 S27 S28 S29 S30 S31 S32 S33 -3.18 -2.43 -3.12 -3.62 -3.74 -3.33 -1.48 -0.26 0.47 2.18 1.23 2.43 1.35 2.73 0.88 3.20 5.27 4.90 5.78 4.51 5.07 7.51 203.97 235.37 155.96 202.44 183.92 38.06 0.61 16.70 19.28 12.77 16.58 15.06 3.12 S36 S37 S38 S39 S40 S41 S42 -3.58 -2.73 -2.84 -2.34 -2.78 -3.79 -2.15 1.20 2.11 1.98 0.56 2.58 2.57 2.55 5.30 5.62 6.03 2.86 6.43 6.00 6.20 135.42 192.59 192.63 17.58 178.75 172.19 60.56 11.09 15.77 15.78 1.44 14.64 14.10 4.96 S44 S45 S46 Min -2.70 -3.65 -1.64 Mean 2.50 1.41 2.50 Max 5.67 5.36 5.80 Valid days 30.29 29.58 29.35 % valid 2.48 2.42 2.40 S08 -3.39 2.16 5.93 246.60 20.20 S17 -3.53 2.10 6.30 247.35 20.26 S25 -3.13 2.24 6.32 212.97 17.44 S34 -3.21 0.85 3.81 220.86 18.09 S43 -1.59 0.79 4.04 78.68 6.44 89 Table 4.5: Summary of calculated UHImax intensities. Statistics are gathered across the entire study period (Feb 2008 to Jun 2011) Min Mean Max Valid nights % valid Min Mean Max Valid nights % valid Min Mean Max Valid nights % valid Min Mean Max Valid nights % valid Min Mean Max Valid nights % valid S01 S02 S03 S04 S05 S06 -0.44 -0.73 -2.44 -2.06 0.00 -0.13 2.50 2.69 0.30 1.32 3.19 3.16 4.34 5.22 2.10 3.50 5.03 4.76 84 42 77 68 33 22 6.88 3.40 6.30 5.54 2.68 1.82 S09 S10 S11 S12 S13 S14 -0.74 -0.85 -1.85 -1.01 -0.10 -0.52 3.35 1.56 1.87 3.05 3.70 3.51 5.28 3.75 4.95 5.37 6.19 6.06 12 87 77 86 89 88 0.95 7.12 6.30 7.12 7.28 7.2 S07 -0.68 3.64 5.87 89 7.28 S15 -0.28 3.40 5.36 89 7.28 S08 -1.22 3.28 5.93 89 7.28 S17 -0.25 3.19 6.30 89 7.28 S18 S19 S20 S21 0.20 -0.05 -0.57 -1.30 3.69 2.99 3.05 2.56 5.30 5.72 4.74 5.17 20 57 25 76 1.66 4.68 2.06 6.24 S27 S28 S29 S30 -0.15 -1.15 -0.28 -0.30 0.51 0.48 3.19 2.08 0.88 2.09 5.27 4.26 4 78 85 58 0.32 6.06 6.80 4.78 S36 S37 S38 S39 -2.14 -0.33 -0.19 -0.12 1.80 3.04 3.23 0.75 5.03 5.40 5.80 1.94 53 65 65 6 4.34 5.30 5.30 0.48 S24 0.36 3.76 5.80 93 7.60 S33 0.37 3.30 5.03 15 1.26 S42 0.36 3.45 5.57 29 2.38 S25 -1.23 3.17 5.57 83 6.80 S34 -1.23 1.57 3.75 75 6.14 S43 -1.31 0.99 3.03 35 2.84 Min Mean Max Valid nights % valid S44 1.96 4.46 5.67 7 0.56 S45 2.59 3.91 5.36 5 0.42 S22 S23 0.37 -1.97 4.14 0.17 6.46 2.56 90 82 7.38 6.70 S31 S32 0.12 -0.71 3.36 1.91 5.63 4.43 68 68 5.64 5.64 S40 S41 0.41 0.04 3.66 3.39 5.64 5.80 61 56 5.00 4.60 S46 2.23 4.39 5.80 7 0.56 [...]... 82. 96 58.93 80.95 67.98 31.16 19.30 95.14 94. 92 S09 S10 S11 S 12 S13 S14 S15 S16 22 .13 21 .47 21 .93 22 .31 22 .64 22 .33 22 . 02 20.18 27 .26 26 .93 27 .24 28 .18 28 .55 28 .22 28 .28 26 .18 32. 03 36.13 36.59 36.36 35.09 34.56 35.19 35. 42 101.76 1146.83 861.59 9 82. 37 1156.90 1157.57 1159. 62 1094. 12 8.34 93.94 70.58 80.47 94.77 94. 82 94.99 89.63 S17 S18 S19 S20 S21 S 22 S23 S24 22 .09 22 .38 21 .90 21 .93 22 .16 23 . 12 20.08... S19 S20 S21 S 22 S23 S24 -2. 05 -3.16 -2. 61 -3.09 -3.04 -1.97 -2. 95 2. 83 2. 30 2. 29 1.66 2. 89 0 .22 2. 69 5.35 5. 72 4.86 5.17 6.70 2. 88 6 .20 44 .20 164 .29 52. 95 22 6.35 24 6.56 22 6.88 25 5.70 3. 62 13.46 4.34 18.54 20 .19 18.58 20 .94 S27 S28 S29 S30 S31 S 32 S33 -3.18 -2. 43 -3. 12 -3. 62 -3.74 -3.33 -1.48 -0 .26 0.47 2. 18 1 .23 2. 43 1.35 2. 73 0.88 3 .20 5 .27 4.90 5.78 4.51 5.07 7.51 20 3.97 23 5.37 155.96 20 2.44 183. 92. .. -2. 12 -2. 74 -3.79 1.56 1.93 -0 .27 0.67 2. 51 2. 20 2. 38 4.75 5 .22 2. 26 3.50 5. 42 4.76 6.44 21 3.47 147 .25 1 82. 08 178.19 74.31 48.96 24 6.90 17.48 12. 06 14.91 14.59 6.09 4.01 20 .22 S09 S10 S11 S 12 S13 S14 S15 -3.73 -3.01 -3.96 -2. 89 -3 .24 -3.41 -3.65 2. 01 1.01 1.33 2. 30 2. 71 2. 35 2. 42 5.34 4.04 5.00 5.69 6.19 6.10 5.95 22 .23 24 4. 62 188.66 22 3 .25 24 5 .26 24 4. 72 246.46 1. 82 20.03 15.45 18 .28 20 .09 20 .04 20 .18... 22 .01 22 .63 22 .99 27 .44 28 .11 28 .00 34.33 35.34 35.85 514.03 8 12. 15 8 12. 02 42. 11 66.53 66. 52 S43 S44 Min 21 .99 22 .99 Mean 27 .05 28 .63 Max 35 .21 34.89 Valid days 364.07 119.05 % valid 29 . 82 9.75 S30 S31 21 .69 22 .40 27 .21 28 .27 34.37 35.67 739.65 956.83 60.59 78.38 S39 S40 20 .86 22 .87 26 .39 28 .50 33.73 34.84 72. 90 779.48 5.97 63.85 S45 S46 22 .70 22 . 92 28.03 28 .54 35. 82 34.44 113.10 117.99 9 .26 9.66 S 32 22. 02. .. 20 .08 22 .50 27 .96 28 .24 28 .04 27 .78 27 .56 28 .75 26 .36 28 .48 36.19 33.95 36.10 33.59 34.87 34.77 35.40 35.19 1167.14 24 7. 32 757.19 25 2.45 1111.46 11 72. 25 1079.54 11 92. 35 95.61 20 .26 62. 03 20 .68 91.05 96.03 88.43 97.67 S25 22 .04 28 .05 34.95 998.08 81.76 S34 21 .85 26 .89 34 .21 1051.88 86.17 S27 S28 S29 21 .76 20 .90 22 .28 26 .45 26 .65 28 .03 33.14 35.57 34. 72 23.33 896.08 1094.33 1.91 73.40 89.64 S36 S37 S38 22 .01... all weather conditions Min Mean Max Valid days % valid Min Mean Max Valid days % valid Min Mean Max Valid days % valid Min Mean Max Valid days % valid Min Mean Max Valid days % valid S01 S 02 S03 S04 S05 S06 S07 S08 22 .07 21 .66 20 .79 21 .66 22 .00 22 . 12 22. 81 22 .30 27 .45 27 . 82 25. 72 26.61 27 .98 27 .68 28 .26 28 .03 35.43 36 .29 34. 52 34.99 35.87 34.40 34.40 35.01 10 12. 80 719. 42 988 .24 829 . 82 380.43 23 5.60... 19 .28 12. 77 16.58 15.06 3. 12 S36 S37 S38 S39 S40 S41 S 42 -3.58 -2. 73 -2. 84 -2. 34 -2. 78 -3.79 -2. 15 1 .20 2. 11 1.98 0.56 2. 58 2. 57 2. 55 5.30 5. 62 6.03 2. 86 6.43 6.00 6 .20 135. 42 1 92. 59 1 92. 63 17.58 178.75 1 72. 19 60.56 11.09 15.77 15.78 1.44 14.64 14.10 4.96 S44 S45 S46 Min -2. 70 -3.65 -1.64 Mean 2. 50 1.41 2. 50 Max 5.67 5.36 5.80 Valid days 30 .29 29 .58 29 .35 % valid 2. 48 2. 42 2.40 S08 -3.39 2. 16 5.93 24 6.60... 22 . 02 27.33 35.51 891. 02 72. 99 S41 22 .75 28 . 52 35.53 719.55 58.94 S33 22 .57 28 .09 34.69 23 5.04 19 .25 S 42 23 .21 28 .66 34.39 21 7.51 17. 82 87 in and around the periphery of the central business district (CBD) The remaining three (two) are in extensive low-rise commercial or residential areas Four (three) stations had a mean UHIraw (UHImax ) intensity close to zero (±0.5◦ C), all of which are located in. .. (S07: 28 .26 ◦ C, S18: 28 .24 ◦ C, S 22: 28 .75◦ C, S24: 28 .48◦ C, S31: 28 .27 ◦ C, S40: 28 .50◦ C, S 42: 28 .66◦ C, S44: 28 .63◦ C and S46: 28 .54◦ C) Another cluster of stations with high mean air temperatures are found in the south-eastern coast (S13: 28 .55◦ C, S15: 28 .28 ◦ C, S41: 28 . 52 C) This part of the island is located farther from the cooler rural north-west and central catchment, possibly a reason why the. .. 3.64 5.87 89 7 .28 S15 -0 .28 3.40 5.36 89 7 .28 S08 -1 .22 3 .28 5.93 89 7 .28 S17 -0 .25 3.19 6.30 89 7 .28 S18 S19 S20 S21 0 .20 -0.05 -0.57 -1.30 3.69 2. 99 3.05 2. 56 5.30 5. 72 4.74 5.17 20 57 25 76 1.66 4.68 2. 06 6 .24 S27 S28 S29 S30 -0.15 -1.15 -0 .28 -0.30 0.51 0.48 3.19 2. 08 0.88 2. 09 5 .27 4 .26 4 78 85 58 0. 32 6.06 6.80 4.78 S36 S37 S38 S39 -2. 14 -0.33 -0.19 -0. 12 1.80 3.04 3 .23 0.75 5.03 5.40 5.80 1.94 ... S19 S20 S21 S 22 S23 S24 22 .09 22 .38 21 .90 21 .93 22 .16 23 . 12 20.08 22 .50 27 .96 28 .24 28 .04 27 .78 27 .56 28 .75 26 .36 28 .48 36.19 33.95 36.10 33.59 34.87 34.77 35.40 35.19 1167.14 24 7. 32 757.19 25 2.45... S46 22 .70 22 . 92 28.03 28 .54 35. 82 34.44 113.10 117.99 9 .26 9.66 S 32 22. 02 27.33 35.51 891. 02 72. 99 S41 22 .75 28 . 52 35.53 719.55 58.94 S33 22 .57 28 .09 34.69 23 5.04 19 .25 S 42 23 .21 28 .66 34.39 21 7.51... S19 S20 S21 S 22 S23 S24 -2. 05 -3.16 -2. 61 -3.09 -3.04 -1.97 -2. 95 2. 83 2. 30 2. 29 1.66 2. 89 0 .22 2. 69 5.35 5. 72 4.86 5.17 6.70 2. 88 6 .20 44 .20 164 .29 52. 95 22 6.35 24 6.56 22 6.88 25 5.70 3. 62 13.46