The Urban Heat Island Intensity of Paris: A Case Study Based on a Simple Urban Surface Parametrization

We explore the ability of a simple urban surface parametrization, embedded in a mesoscale meteorological model, to correctly reproduce observed values of the urban heat island (UHI) intensity, which is defined as the urban-rural surface air temperature difference. To do so, a simple urban scheme was incorporated into the Advanced Regional Prediction System (ARPS). Subsequently, a simulation was performed with the coupled model over the wider area of Paris, for a 12-day period in June 2006 that was characterised by conditions prone to UHI development. Simulated 2-m air temperature was compared with observed values for urban and rural stations, yielding mean errors of 1.4 and 1.5 K, respectively. More importantly, it was found that the model also displayed an overall good capability of reproducing the observed temperature differences. In particular, the magnitude (up to 6 K) and timing of the diurnal cycle of the UHI intensity was simulated well, the model exhibiting a mean error of 1.15 K. As a result, our conclusion is that the ARPS model, extended with simple urban surface physics, is able to capture observed urban-rural air temperature differences well, at least for the domain and period studied.     

Statistical and dynamical characteristics of the urban heat island intensity in Seoul

The statistical and dynamical characteristics of the urban heat island (UHI) intensity in Seoul are investigated for non-precipitation days and precipitation days using 4-year surface meteorological data with 1-h time intervals. Furthermore, the quantitative influence of synoptic pressure pattern on the UHI intensity is examined using a synoptic condition clustering method. The statistical analysis shows that the daily maximum UHI intensity in Seoul for non-precipitation days is strongest in autumn (4.8°C) and weakest in summer (3.5°C). The daily maximum UHI intensity is observed around midnight in all seasons except in winter when the maximum occurrence frequency is found around 08 LST. This implies that anthropogenic heating contributes to the UHI in the cold season. The occurrence frequency of the UHI intensity has a negatively skewed distribution for non-precipitation days but a positively skewed distribution for precipitation days. The amplitude of the heating/cooling rate and the difference in the heating/cooling rate between the urban and rural areas are smaller in all seasons for precipitation days than for non-precipitation days, resulting in weaker UHI intensities for precipitation days. The urban cool island occurs very often in the daytime, with an occurrence frequency being 77% of the total non-precipitation days in spring. The analysis of the impact of large-scale dynamical forcing shows that the daily maximum UHI intensity varies with synoptic pressure pattern, ranging from ?22% in spring to 28% in summer relative to the seasonal mean daily maximum UHI intensity. Comparison of the UHI intensity calculated using station-averaged temperatures to that based on the conventional two-station approach indicates that local effects on the UHI intensity are minimized by using multiple-station data. Accordingly, an estimation of the UHI intensity using station-averaged temperatures for both urban and rural areas is suggested.     

The extent and intensity of the urban heat island in Iași city,Romania

The study underlines the characteristics of the urban heat island of Ia?i (Ia?i’s UHI) on the basis of 3 years of air temperature measurements obtained by fixed-point observations. We focus on the identification of UHI development and intensity as it is expressed by the temperature differences between the city centre and the rural surroundings. Annual, seasonal and daily characteristics of Ia?i’s UHI are investigated at the level of the classical weather observation. In brief, an intensity of 0.8 °C of UHI and a spatial extension which corresponds to the densely built area of the city were delineated. The Ia?i UHI is stronger during summer calm nights—when the inner city is warmer with 2.5–3 °C than the surroundings—and is weaker during windy spring days. The specific features of Ia?i’s UHI bear a profound connection to the specificity of the urban structure, the high atmospheric stability in the region and the local topography. Also, the effects of Ia?i’s UHI upon some environmental aspects are presented as study cases. For instance, under the direct influence of UHI, we have observed that in the city centre, the apricot tree blossoms earlier (with up to 4 days) and the depth of the snow cover is significantly lower (with up to 10 cm for a rural snow depth of 30 cm) than in the surrounding areas.     

The surface urban heat island in the city of Brno (Czech Republic) derived from land surface temperatures and selected reasons for its spatial variability

Thermal infrared images from Landsat satellites are used to derive land surface temperatures (LST) and to calculate the intensity of the surface urban heat island (UHI) during the summer season in and around the city of Brno (Czech Republic). Overall relief, land use structure, and the distribution of built-up areas determine LST and UHI spatial variability in the study area. Land-cover classes, amount and vigor of vegetation, and density of built-up areas are used as explanatory variables. The highest LST values typically occur in industrial and commercial areas, which contribute significantly to surface UHI intensity. The intensity of surface UHI, defined as the difference between mean LST for urban and rural areas, reached 4.2 and 6.7 °C in the two images analyzed. Analysis of two surface characteristics in terms of the amount of vegetation cover, represented by normalized difference vegetation index, demonstrates the predominance of LST variability (56–67 % of explained variance) over the degree of urbanization as represented by density of buildings (37–40 % of LST variance).     

Impact of land cover data on the simulation of urban heat island for Berlin using WRF coupled with bulk approach of Noah-LSM

Urban-rural difference of land cover is the key determinant of urban heat island (UHI). In order to evaluate the impact of land cover data on the simulation of UHI, a comparative study between up-to-date CORINE land cover (CLC) and Urban Atlas (UA) with fine resolution (100 and 10 m) and old US Geological Survey (USGS) data with coarse resolution (30 s) was conducted using the Weather Research and Forecasting model (WRF) coupled with bulk approach of Noah-LSM for Berlin. The comparison between old data and new data partly reveals the effect of urbanization on UHI and the historical evolution of UHI, while the comparison between different resolution data reveals the impact of resolution of land cover on the simulation of UHI. Given the high heterogeneity of urban surface and the fine-resolution land cover data, the mosaic approach was implemented in this study to calculate the sub-grid variability in land cover compositions. Results showed that the simulations using UA and CLC data perform better than that using USGS data for both air and land surface temperatures. USGS-based simulation underestimates the temperature, especially in rural areas. The longitudinal variations of both temperature and land surface temperature show good agreement with urban fraction for all the three simulations. To better study the comprehensive characteristic of UHI over Berlin, the UHI curves (UHIC) are developed for all the three simulations based on the relationship between temperature and urban fraction. CLC- and UA-based simulations show smoother UHICs than USGS-based simulation. The simulation with old USGS data obviously underestimates the extent of UHI, while the up-to-date CLC and UA data better reflect the real urbanization and simulate the spatial distribution of UHI more accurately. However, the intensity of UHI simulated by CLC and UA data is not higher than that simulated by USGS data. The simulated air temperature is not dominated by the land cover as much as the land surface temperature, as air temperature is also affected by air advection.     

Temporal characteristics of the Beijing urban heat island

Summary This paper describes the inter-annual trend, and the seasonal and hourly variation of the near surface urban heat island (UHI) in Beijing. The surface air temperature data (mean, maximum, and minimum) from one urban (downtown Beijing) and one rural (70 km from downtown Beijing) station were used for the period 1977 and 2000. It is found that the temperatures in both urban and rural stations show an increasing tendency. Specifically, minimum temperature shows the greatest tendency at the urban station whereas maximum temperature shows the greatest increase at the rural station. The UHI intensity obtained by calculating the difference in temperatures between the two stations identifies that the intensity is greatest and has the greatest increasing trend for minimum temperature, while the UHI intensity of maximum temperature shows a slow decrease over time. UHI intensity for minimum temperature has a strong positive correlation with the increase in the urban population and the expansion of the yearly construction area. Seasonal analyses showed the UHI intensity is strongest in winter. This seasonal UHI variation tends to be negatively correlated with the seasonal variation of relative humidity and vapor pressure. Hourly variation reveals that the strongest UHI intensity is observed in the late nighttime or evening, while the weakest is observed during the day.     

Estimating daily net radiation in the FAO Penman–Monteith method

Heatwave intensity and frequency are predicted to increase in the coming years, and this will bear adverse consequences to the environmental well-being and the socio-economic fabric in urbanized areas. The hazardous combination of increased heat storage and reduced water retention capacities of the land surface make the urban areas warmer than the surrounding rural areas in what is commonly known as the urban heat island (UHI) effect. The primary motives of this study are to quantify the interaction of this city-scale UHI with synoptic-scale heatwave episodes and to analyze the factors that mediate this interaction. A modified version of the Weather Research and Forecasting model (WRF) is utilized to simulate two heatwave episodes in New York City. The land surface scheme in the default WRF model is modified to better represent the surface to atmosphere exchanges over urban areas. Our results indicate that during the heatwave episodes, the daily-averaged UHI in NYC increased by 1.5 K. Furthermore, most of this amplification occurs in the mid-afternoon period when the temperatures peak. Wind direction and urban-rural contrasts in available energy and moisture availability are found to have significant and systematic effects on the UHI, but wind speed plays a secondary role.     

Effects of the large-scale atmospheric circulation on the onset and strength of urban heat islands: a case study

Air temperature was monitored at 13 sites across the urban perimeter of a Brazilian midsize city in winter 2011. In this study, we show that the urban heat island (UHI) develops only at night and under certain weather conditions, and its intensity depends not only on the site's land cover but also on the meteorological setting. The urban heat island intensity was largest (6.6 °C) under lingering high-pressure conditions, milder (3.0 °C) under cold anticyclones and almost vanished (1.0 °C) during the passage of cold fronts. The cooling rates were calculated to monitor the growth and decay of the UHI over each specific synoptic setting. Over four contiguous days under the effect of a lingering high-pressure event, we observed that the onset of cooling was always at about 2 h before sunset. The reference site attained mean cooling rate of ?2.6 °C h^?1 at sunset, whilst the maximum urban rate was ?1.2 °C h^?1. Under a 3-day cold anticyclone episode, cooling also started about 2 h before sunset, and the difference between maximum rural (?2.0 °C h^?1) and urban (?1.0 °C h^?1) cooling rates diminished. Under cold-front conditions, the cooling rate was homogeneous for all sites and swang about zero throughout the day. The air temperature has a memory effect under lingering high-pressure conditions which intensified the UHI, in addition to the larger heat storage in the urban area. Cold anticyclone conditions promoted the development of the UHI; however, the cold air pool and relatively light winds smoothed out its intensity. Under the influence of cold fronts, the urban fabric had little effect on the city's air temperature field, and the UHI was imperceptible.     

Responses of urban heat island in Atlanta to different land-use scenarios

The urban heat island (UHI) effect changes heat and water cycles in urban areas, and has been accused of elevating energy consumption, deteriorating living environment, and increasing mortality rates. Understanding various UHI effects necessitates a systematic modeling approach. A major problem in UHI simulations is that urban areas were either considered to have only one category of land use/cover or outdated in land use/cover patterns due to the lack of high resolution data. Therefore, this study aims at integrating up-to-date remotely sensed land use/cover data with the Weather Research and Forecasting (WRF/UCM)/Urban Canopy Model modeling systems to simulate surface temperature patterns in Atlanta, Georgia. In addition, three land-use scenarios, i.e., spontaneous scenario (SS), concentrated scenario (CS), and local policy scenario (LPS), were designed and incorporated into the modeling. Five numerical experiments were conducted by using the Weather Research and Forecasting (WRF) model to explore the impact of urbanization-induced land-cover changes on temperature patterns. Land use and land-cover patterns under all three scenarios suggested that urban growth would continue through in-filling development and outward expansion. Compared to temperature simulations in 2011, temperature maps corresponding to the three urban growth scenarios showed warmer and cooler temperature patterns outside and inside the urban core, respectively. Analysis of the mean diurnal temperature cycle suggested that the highest temperature difference of 3.9 K was observed between 2011 and the LPS, and occurred around 22:00 local time. Overall, the simulations showed different UHI effects respond to the land-use scenarios in the summer. It is recommended for urban managers and policy makers to reflect on the potential impacts of alternative urban growth policies on thermal environment.     

Climate-induced changes in river water temperature in North Iberian Peninsula

With the surface air temperature (SAT) data at 37 stations on Central Yunnan Plateau (CYP) for 1961–2010 and the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light data, the temporal-spatial patterns of the SAT trends are detected using Sen’s Nonparametric Estimator of Slope approach and MK test, and the impact of urbanization on surface warming is analyzed by comparing the differences between the air temperature change trends of urban stations and their corresponding rural stations. Results indicated that annual mean air temperature showed a significant warming trend, which is equivalent to a rate of 0.17 °C/decade during the past 50 years. Seasonal mean air temperature presents a rising trend, and the trend was more significant in winter (0.31 °C/decade) than in other seasons. Annual/seasonal mean air temperature tends to increase in most areas, and higher warming trend appeared in urban areas, notably in Kunming city. The regional mean air temperature series was significantly impacted by urban warming, and the urbanization-induced warming contributed to approximately 32.3–62.9 % of the total regional warming during the past 50 years. Meantime, the urbanization-induced warming trend in winter and spring was more significant than that in summer and autumn. Since 1985, the urban heat island (UHI) intensity has gradually increased. And the urban temperatures always rise faster than rural temperatures on the CYP.     

Assessment of urban heat island effect for different land use–land cover from micrometeorological measurements and remote sensing data for megacity Delhi

Urban heat island intensities (UHI) have been assessed based on in situ measurements and satellite-derived observations for the megacity Delhi during a selected period in March 2010. A network of micrometeorological observational stations was set up across the city. Site selection for stations was based on dominant land use–land cover (LULC) classification. Observed UHI intensities could be classified into high, medium and low categories which overall correlated well with the LULC categories viz. dense built-up, medium dense built-up and green/open areas, respectively. Dense urban areas and highly commercial areas were observed to have highest UHI with maximum hourly magnitude peaking up to 10.7 °C and average daily maximum UHI reaching 8.3 °C. UHI obtained in the study was also compared with satellite-derived land surface temperatures (LST). UHI based on in situ ambient temperatures and satellite-derived land surface temperatures show reasonable comparison during nighttime in terms of UHI magnitude and hotspots. However, the relation was found to be poor during daytime. Further, MODIS-derived LSTs showed overestimation during daytime and underestimation during nighttime when compared with in situ skin temperature measurements. Impact of LULC was also reflected in the difference between ambient temperature and skin temperature at the observation stations as built-up canopies reported largest gradient between air and skin temperature. Also, a comparison of intra-city spatial temperature variations based UHI vis-à-vis a reference rural site temperature-based UHI indicated that UHI can be computed with respect to the station measuring lowest temperature within the urban area in the absence of a reference station in the rural area close to the study area. Comparison with maximum and average UHI of other cities of the world revealed that UHI in Delhi is comparable to other major cities of the world such as London, Tokyo and Beijing and calls for mitigation action plans.     

Large-Eddy Simulation of Mesoscale Circulations Forced by Inhomogeneous Urban Heat Island

The large-eddy simulation mode of the Weather Research and Forecasting model is employed to simulate the planetary boundary-layer characteristics and mesoscale circulations forced by an ideal urban heat island (UHI). In our simulations, the horizontal heterogeneity of the UHI intensity distribution in urban areas is considered and idealized as a cosine function. Results indicate that the UHI heating rate and the UHI intensity heterogeneity affect directly the spatial distribution of the wind field; a stronger UHI intensity produces a maximum horizontal wind speed closer to the urban centre. The strong advection of warm air from the urban area to the rural area in the upper part of the planetary boundary-layer causes a more stable atmospheric stratification over both the urban and rural areas. The mesoscale sensible heat flux caused by the UHI circulation increases with UHI intensity but vanishes when the background wind speed is sufficiently high $(>$ 3.0  $\mathrm{{m\,s}}^{-1})$ .     

应用城市地表能量平衡方案研究城市冠层结构对城市热岛的热力影响

用南京大学区域边界层模式NJU-RBLM, 通过对一组理想试验的模拟, 研究了TEB方案 (town energy balance) 和SVAT方案 (soil－vegetation－atmosphere transfer) 模拟城市热岛现象的差异及本质原因, 发现TEB方案对城市热岛 (UHI) 尤其是夜间UHI模拟效果更优, 这是由于TEB方案具备较强模拟城市储热项的能力形成的。此外, 深入探讨UHI对大气边界层热力结构的影响, 发现UHI现象使城市和郊区的近地层位温廓线在清晨和傍晚都存在明显差异, 同时使城市区域气温全天高于郊区, 且日间城乡温差能达到的高度明显高于夜间。分析人为热源和建筑物冠层对UHI的影响时发现: 人为热源对UHI的影响在夜间强于白天, 而建筑物对白天城市湍能的影响强于人为热源的作用。     

Characterization and estimation of urban heat island at Toronto: impact of the choice of rural sites

In this study, the urban heat island of Toronto was characterized and estimated in order to examine the impact of the selection of rural sites on the estimation of urban heat island (UHI) intensity (?T _u-r). Three rural stations, King Smoke Tree (KST), Albion Hill, and Millgrove, were used for the analysis of UHI intensity for two urban stations, Toronto downtown (Toronto) and Toronto Pearson (Pearson) using data from 1970 to 2000. The UHI intensity was characterized as winter dominating and summer dominating, depending on the choice of the rural station. The analyses of annual and seasonal trends of ?T _u-r suggested that urban heat island clearly appears in winter at both Toronto and Pearson. However, due to the mitigating effect on temperature from Lake Ontario, the estimated trend of UHI intensity was found to be less at Toronto compared to that at Pearson which has no direct lake effect. In terms of the impacts of the rural stations, for both KST and Millgrove, the trends in UHI intensity were found to be statistically significant and also were in good agreement with the estimates of UHI intensities reported for other large cities in the USA. Depending on the choice of the rural station, the estimated trend for the UHI intensity at Toronto ranges from 0.01°C/decade to 0.02°C/decade, and that at Pearson ranges from 0.03°C/decade to 0.035°C/decade during 1970–2000. From the analysis of the seasonal distribution of ?T _u-r, the UHI intensity was found to be higher at Toronto in winter than that at Pearson for all three rural stations. This was likely accounted for by the lower amount of anthropogenic heat flux at Pearson. Considering the results from the statistical analysis with respect to the geographic and surface features for each rural station, KST was suggested to be a better choice to estimate UHI intensity at Toronto compared to the other rural stations. The analysis from the current study suggests that the selection of a unique urban–rural pair to estimate UHI intensity for a city like Toronto is a critical task, as it will be for any city, and it is imperative to consider some key features such as the physiography, surface characteristics of the urban and rural stations, the climatology such as the trends in annual and seasonal variation of UHI with respect to the physical characteristics of the stations, and also more importantly the objectives of a particular study in the context of UHI effect.     

拉萨市城市热岛效应分析研究

利用拉萨、墨竹工卡、尼木建站以来的多年历史资料和近两年新建的区域自动站、8个城市热岛效应自动气象站资料分析拉萨城市热岛强度日、季、年变化,时空分布及其可能的影响因子。分析表明:拉萨城市热岛强度呈显著的逐年增强趋势,在1978~2011年间平均每10年增加0.24℃;多年热岛强度冬季最强(2.0℃),其次是春季(1.8℃)和秋季(1.7℃),夏季强度最小(1.6℃);拉萨城市高温中心主要在城市中心,气温分布沿着高值区向两侧呈递减状态,郊外的气温比城区平均低0.9℃左右,夜间热岛效应强度明显高于白天。随着城市化进程的不断增强,大量改变的下垫面状况,不断增多的城市建筑群,骤增的人类活动和能源消耗,导致城市热岛强度不断增强。      

Simulation of urban climate with high-resolution WRF model: A case study in Nanjing, China

In this study, urban climate in Nanjing of eastern China is simulated using 1-km resolution Weather Research and Forecasting (WRF) model coupled with a single-layer Urban Canopy Model. Based on the 10-summer simulation results from 2000 to 2009 we find that the WRF model is capable of capturing the high-resolution features of urban climate over Nanjing area. Although WRF underestimates the total precipitation amount, the model performs well in simulating the surface air temperature, relative humidity, and precipitation frequency and inter-annual variability. We find that extremely hot events occur most frequently in urban area, with daily maximum (minimum) temperature exceeding 36°C (28°C) in around 40% (32%) of days. Urban Heat Island (UHI) effect at surface is more evident during nighttime than daytime, with 20% of cases the UHI intensity above 2.5°C at night. However, The UHI affects the vertical structure of Planet Boundary Layer (PBL) more deeply during daytime than nighttime. Net gain for latent heat and net radiation is larger over urban than rural surface during daytime. Correspondingly, net loss of sensible heat and ground heat are larger over urban surface resulting from warmer urban skin. Because of different diurnal characteristics of urban-rural differences in the latent heat, ground heat and other energy fluxes, the near surface UHI intensity exhibits a very complex diurnal feature. UHI effect is stronger in days with less cloud or lower wind speed. Model results reveal a larger precipitation frequency over urban area, mainly contributed by the light rain events (< 10 mm d^?1). Consistent with satellite dataset, around 10?C20% more precipitation occurs in urban than rural area at afternoon induced by more unstable urban PBL, which induces a strong vertical atmospheric mixing and upward moisture transport. A significant enhancement of precipitation is found in the downwind region of urban in our simulations in the afternoon.     

基于MODIS数据的拉萨市地表温度变化研究

采用2000～2011年6月MODIS地表温度产品和拉萨市4个气象站6月平均地表温度对拉萨市地表温度的时空变化进行了分析.结果表明：拉萨市在近12年内地表温度呈明显上升趋势,2009年地表温度达到最高为28.49℃,最小值出现在2003年为14.12℃.在空间分布上高温区主要集中在城市中心和城市周边区域,并随着时间推移不断向外扩张,在2007年6月拉萨市地表温度高温区分布范围最大,其中纳木错东部和林周县的高温区增加最显著;在利用实测的地表温度与MO-DIS反演的地表温度做相关分析发现,两者的相关系数为0.64通过了0.001的显著性检验,两种地表温度的时间变化趋势也较为一致,因此MODIS地表温度反演产品适用于大范围地表温度和城市热环境监测是可行的.     

Spatio-temporal variance and meteorological drivers of the urban heat island in a European city

Urban areas are especially vulnerable to high temperatures, which will intensify in the future due to climate change. Therefore, both good knowledge about the local urban climate as well as simple and robust methods for its projection are needed. This study has analysed the spatio-temporal variance of the mean nocturnal urban heat island (UHI) of Hamburg, with observations from 40 stations from different suppliers. The UHI showed a radial gradient with about 2 K in the centre mostly corresponding to the urban densities. Temporarily, it has a strong seasonal cycle with the highest values between April and September and an inter-annual variability of approximately 0.5 K. Further, synoptic meteorological drivers of the UHI were analysed, which generally is most pronounced under calm and cloud-free conditions. Considered were meteorological parameters such as relative humidity, wind speed, cloud cover and objective weather types. For the stations with the highest UHI intensities, up to 68.7 % of the variance could be explained by seasonal empirical models and even up to 76.6 % by monthly models.     

Objective method for classifying air masses: an application to the analysis of Buenos Aires’ (Argentina) urban heat island intensity

Summary ?During recent years, numerous studies have examined the Buenos Aires urban climate, but the relationship between large-scale weather conditions and the Buenos Aires urban heat island (UHI) intensity has not been studied. The goal of this paper is to apply an objective synoptic climatological method to identify homogeneous air masses or weather types affecting Buenos Aires during winter, and to relate the results to the UHI intensity. A K-means clustering method was used to define six different air masses considering the 03:00, 09:00, 15:00 and 21:00 LT surface observations of dry bulb temperature, dew point, cloud cover, atmospheric pressure and wind direction and velocity at Ezeiza, the most rural meteorological station of the Buenos Aires metropolitan area (Fig. 1). Results show that the mean UHI intensity is at its maximum (2.8 °C) a few hours before sunrise when conditions are dominated by cold air masses associated with cold-core anticyclones, weak winds and low cloud cover. Inverse heat islands are found during the afternoon for all air masses indicating that surface processes are not dominant at that time. The relatively infrequent and warmest air mass is the only one that presents a mean negative urban-rural temperature difference (−0.1 °C) during the afternoon with the smallest diurnal cycle of the UHI intensity probably due to the prevailing high humidity and cloudy sky conditions. The paper provides an insight into the Buenos Aires urban–rural temperature difference under a variety of winter weather types and results could be useful to improve local daily temperature forecasts for the metropolitan area of Buenos Aires on the basis of the routine forecasts of weather types. Received October 24, 2001; revised June 12, 2002; accepted October 10, 2002     

基于Landsat TM/ETM＋数据的北京城市热岛季节特征研究

利用1988—2006年20景LandsatTM和ETM＋数据分析了北京市城市热岛的季节变化特征。通过反演地表温度,建立统一的城市和农村区域,计算了城市热岛强度,并采用多项式拟合获取了城市热岛强度的季节变化曲线;同时,分析了热岛强度季节特征与气候因子的关系。另外利用4景2005—2006年不同季节Landsat TM影像,分析了不同季节城市热岛的空间变化特征,并选择穿越北京城区的两条不同方向剖线（SE-NW和SW-NE）,分析了沿剖线方向城市热岛与地表类型的关系。结果显示,北京城市热岛具有明显的季节变化特征,与总云量的季节变化关系显著。最大热岛强度出现在夏季,呈现片状发散和零星热岛并存的空间分布特征。冬季为冷岛特征,其空间分布与夏季热岛一致。春秋两季热岛强度最小,但春季热岛空间差异较大。在相同季节,城市热岛强度和空间尺度在不同剖线方向具有明显的差异,这与不同地类的空间分布有关。