昆明城市热岛效应变化特征研究

利用昆明周边多个自动气象站观测的2004~2012年温度序列,研究了昆明城市热岛效应的日、季节和年际变化特征,并分析了昆明城市热岛水平分布和变化趋势。昆明城市热岛强度具有明显的日变化,夜间较强,白天较弱。城市热岛强度一般在早上08:00(北京时间,下同)达到最大值,在午后14:00减弱或消失。城市热岛强度在冬季最强,春、秋季次之,夏季最弱。昆明城市热岛强度多年平均值为1.27°C,在2004~2009年期间表现为逐年递减的趋势,其年际变化的主要影响因子是云量。2004~2007年昆明城市热岛中心主要分布在主城区。2008年以后,由于中小城镇经济和人口的迅速发展,昆明城市热岛面积不断扩大,并出现热岛中心向呈贡、石林一带偏移的现象。     

北京地区城市热岛的时空分布特征

应用自动气象站逐时气温观测资料分析了北京城市热岛的时空分布特征。结果表明，无论冬、夏季，北京的城市热岛在空间分布上均表现出多中心结构。热岛强度冬季大于夏季、夜间大于白天，热岛中心随时间存在漂移现象，日变化幅度冬季大于夏季。城市热岛在时间域上呈多尺度结构，冬、夏季均以20-30h所对应的日变化和120-270h的周变化为主，周变化振荡的波谷主要出现在周六至周一，波峰出现在周三至周五，变化趋势冬季比夏季明显。     

基于Landsat 8 TIRS地表温度数据反演的深圳城市热岛效应时空分布及土地利用的影响

基于2014～2021年Landsat 8 TIRS卫星遥感影像数据,分别采用劈窗算法(SWA)和大气校正法(ARC)反演了深圳市地表辐射温度,并利用地面站点监测数据对反演结果进行了验证,探讨了该期间深圳城市热岛效应的时空分布特征及其影响因素。研究结果表明,这两种算法反演的地表温度(T_SWA、T_ARC)与地面站点监测气温(T_M)都存在显著线性相关(T_SWA=1.01×T_M+2.65,T_ARC=0.85×T_M+5.51,p<0.01),但劈窗算法更接近地面站点监测数据。在2014～2021年间深圳城市热岛面积(HI>0.01)未观察到显著增加的趋势(p=0.94)。深圳市城市热岛分布与城市发展格局关系密切,城市规划用地类型对城市热岛效应有显著影响。生态水域和生态绿地缓解了城市热岛效应,而交通道路和工业仓储等城市用地强化了城市热岛的形成,并且城市路网的分布和密度对城市热岛的形成有强显著性影响(p=0.003)。     

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。      

济南市城市热岛效应分析

利用1964—2006年济南市气温观测资料,分析了济南市城市热岛效应的年变化特征,济南市热岛效应有逐年增强的趋势。应用区域自动气象站2007—2008年逐时气温观测资料,结合济南市地理信息数据,分析了济南城市热岛的时空分布特征。结果表明:无论春、夏、秋、冬,济南的城市热岛在空间分布上均以泉城广场、市政府为中心呈环状放射发展。热岛强度以冬季最强,秋、春季次之,夏季最弱。济南热岛日变化规律是夜间大于白天,城市热岛以日为周期呈规律性变化,表现为快速形成、快速消失的现象。     

上海城市热岛的精细结构气候特征分析

对上海地区59个自动站2006—2013年逐时气温资料进行了本地化的质量控制,得到一套高分辨率气温数据集。将其与常规观测资料进行对比,发现两者反映的上海年平均及季节平均气温基本一致,说明经质量控制的加密资料是可信的。但其空间差异更为明显,表明高分辨气温数据在城市热环境精细空间分布研究中更具代表性和有效性。基于该数据集研究了上海的城市热岛空间分布。结果表明,加密观测数据可反映出城市热岛的精细结构气候特征:热岛分布由中心城区向四周及西南部扩展,尤其是出现了"多中心"结构特征,即除中心城区的热岛主中心外,在闵行北部和松江南部均出现了与快速城市化进程相联系的区域性副热岛中心;受大气环流季节转换和局地海陆风的影响,热岛位置在秋冬季偏东南方向,春夏季偏西北方向。上述精细化特征在常规资料中并不明显或无法体现。由此可见,经质量控制的加密气温数据在城市热岛的精细结构研究中更具优势。     

基于MODIS资料分析近15年广州城市热岛特征及演变规律

利用MODIS地表温度数据,计算城市热岛强度指数,分析近15年广州市城市热岛的时空分布特征及演变规律,并结合气象观测数据、社会统计数据定性分析其主要影响因素。结果表明:广州市城市热岛的空间分布受地形地貌影响明显,负热岛区主要分布于森林密集的北部山区,无热岛区主要分布于中部低山丘陵区域,热岛区主要分布于高度城市化的中南部平原区。关于城市热岛的日变化规律,白天热岛区、负热岛区面积均小于夜间,但白天热岛区强度、负热岛区强度大于夜间。关于城市热岛的季节变化规律,冬季热岛区面积最大,热岛强度最小,夏季热岛区面积最小,热岛强度最大;冬季负热岛区面积最小,负热岛强度最小,夏季负热岛区面积最大,负热岛强度最大。对于城市热岛的年际变化规律,近15年来广州市的热岛区、负热岛区占全市总面积的百分比呈上升趋势,无热岛区所占百分比呈下降趋势,人为热排放在城市中心区域的持续增长,加上区内建筑物密度大、植被覆盖度低,导致了热岛区的增加,而北部山区至中部丘陵山区的植被的持续好转,加上地理特征限制了该区域的城市化发展,导致了负热岛区的增加。      

城市景观格局与热岛效应研究进展

概述了城市景观格局和城市热岛效应及城市景观格局对城市热岛效应影响等领域的研究现状,探讨了城市景观格局对城市热岛效应的影响作用,并结合城市景观格局研究中的新领域——景观格局优化,提出了解决城市热岛问题的新思路,即通过优化景观格局来达到缓解甚至消除热岛效应。展望了城市景观格局和城市热岛效应研究领域存在的问题和面临的任务。     

规划建设对深圳夏季城市热岛影响的数值模拟研究

以区域边界层模式RBLM为工具,研究了城市规划建设对深圳夏季城市热岛的影响。分别模拟了当前的城市热岛、高密度建设和能耗增加后的城市热岛、以及布设通风走廊后的城市热岛,得到了以下结论:(1)深圳夏季存在城市热岛现象,且昼间热岛比夜间更为明显,高温中心集中在建设密度较高的南山、福田、罗湖和宝安西部等区;(2)建设密度加大及能源消费增加会导致深圳夏季近地面气温出现大面积的升高,并且夜间升温比昼间更为明显;(3)通风走廊的设置可以在一定程度上抵消高密度建设和能耗增加带来的负面效应。     

基于MODIS数据的近8年长三角城市群热岛特征及演变分析

利用MODIS hdf数据来反演地表温度,首先通过数据挑选少云覆盖图像,再经多波段综合法去云,用近8年的MODIS历史资料选择劈窗算法反演计算给出了长三角平均地表温度分布图,以长三角作为区域整体研究热岛效应,分析了城市群热岛分布特征,指出主要城市热岛分布呈"Z"字型分布格局。长三角地区热岛强度季节变化是夏季最强,春季次之,秋冬季除少数地区为较强热岛外,大部分地区都显示为弱热岛或无热岛。采用GIS地理统计方法比较16城市的强热岛面积分布,分析了2001—2008年夏季各城市热岛强度的年际变化趋势。     

城市下垫面对河谷城市兰州冬季热岛效应及边界层结构的影响

利用中尺度数值模式WRF耦合单层城市冠层模块UCM,引入2005年MODIS土地利用类型资料,在对2005年1月25—28日兰州市热岛现象进行高分辨率数值模拟的基础上,设计了去除城市下垫面敏感性试验,探讨了城市下垫面对城市边界层的影响程度。结果表明,城市下垫面能使近地层大气温度升高而风速减小,并且,在夜间表现更明显。由城市热岛强度日变化分析可知,城市下垫面对兰州市热岛强度的贡献率为44%。夜间,城市上空200 m以下的近地层大气保持了白天的混合层特征,热岛环流的上升运动促进了山风环流,使得上升气流到达地面以上600 m左右;白天,由于山峰加热效应,城市上空400—600 m存在一个脱地逆温层,城市热岛环流使得11—15时（北京时）市区近地层出现弱上升气流,抑制了谷风环流的形成及发展。城市下垫面的低反照率特性和建筑物的多次反射作用导致城市下垫面的净辐射通量大于非城市下垫面;城市下垫面由于建筑材料的不透水性,导致潜热通量远小于感热通量,而储热项所占比重明显增大。     

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.     

Maximum urban heat island intensity in a medium-sized coastal Mediterranean city

This paper studies the maximum intensity of the urban heat island (UHI) that develops in Volos urban area, a medium-sized coastal city in central Greece. The maximum temperature difference between the city center and a suburb is 3.4°C and 3.1°C during winter and summer, respectively, while during both seasons the average maximum UHI intensity is 2.0°C. The UHI usually starts developing after sunset during both seasons. It could be attributed to the different nocturnal radiative cooling rate and to the different anthropogenic heat emission rate that are observed at the city center and at the suburb, as well as to meteorological conditions. The analysis reveals that during both seasons the daily maximum hourly (DMH) UHI intensity is positively correlated with solar radiation and with previous day’s maximum hourly UHI intensity and negatively correlated with wind speed. It is also negatively correlated with relative humidity during winter but positively correlated with it during summer. This difference could be attributed to the different mechanisms that mainly drive humidity levels (i.e., evaporation in winter and sea breeze (SB) in summer). Moreover, it is found that SB development triggers a delay in UHI formation in summer. The impact of atmospheric pollution on maximum UHI intensity is also examined. An increase in PM10 concentration is associated with an increase in maximum UHI intensity during winter and with a decrease during summer. The impact of PM10 on UHI is caused by the attenuation of the incoming and the outgoing radiation. Additionally, this study shows that the weekly cycle of the city activities induces a weekly variation in maximum UHI intensity levels. The weekly range of DMH UHI intensity is not very large, being more pronounced during winter (0.4°C). Moreover, a first attempt is made to predict the DMH UHI intensity by applying regression models, whose success is rather promising.     

An investigation of urban heat island intensity (UHII) as an indicator of urban heating

The objective of this paper is to evaluate the reliability of urban heat island intensity (UHII) as an indicator of urban heating. The diurnal patterns of air and surface-temperature based UHII and variations in urban and rural area heating were analyzed and discussed. The detailed air-temperature based UHII patterns were determined in one urban and four suburban areas of Hong Kong. UHII was determined as spatially-averaged air-temperature difference between an urban/suburban area and its surrounding rural area. Additionally, reported air and surface-temperature based UHII patterns were integrated in the discussion to carry out a comprehensive analysis. The urban and rural area heating variations (i.e., the diurnal variations in net radiation, sensible heat flux, latent heat flux, and heat stored by an area) were examined in the light of UHII patterns to validate UHII as an indicator for urban heating. It is noted that the air-temperature based UHIIs were higher and positive in the night-time but lower and negative during the daytime. On the other hand, most of the surface-temperature based UHIIs, investigated through satellite data were positive during both the daytime and night-time. It is revealed that UHII can well reflect urban heating during night-time and early morning. However, the lower and negative UHII during solar peak time (daytime when solar radiation is the dominant source of heating) has seemingly not been representing urban heating.     

A multiple linear statistical model for estimating the mean maximum urban heat island

Summary ¶This study examines the spatial and quantitative influence of urban factors on the surface air temperature field of the medium-sized of Szeged, Hungary, using mobile measurements under different weather conditions in the periods of March 1999–February 2000 and April–October 2002. Efforts have been concentrated on the development of the urban heat island (UHI) in its peak development during the diurnal cycle. Tasks included: (1) determination of spatial distribution of mean maximum UHI intensity and some urban surface parameters (built-up and water surface ratios, sky view factor, building height) using the standard Kriging procedure, as well as (2) development of a statistical model in the so-called heating and non-heating seasons using the above mentioned parameters and their areal extensions. In both seasons the spatial distribution of the mean maximum UHI intensity fields had a concentric shape with some local irregularities. The intensity reaches more than 2.1°C (heating season) and 3.1°C (non-heating season) in the centre of the city. For both seasons statistical model equations were determined by means of stepwise multiple linear regression analysis. As the measured and calculated mean maximum UHI intensity patterns show, there is a clear connection between the spatial distribution of the urban thermal excess and the examined land-use parameters, so these parameters play an important role in the evolution of the strong UHI intensity field. From the above mentioned parameters the sky-view factor and the building height were the most determining factors which are in line with the urban surface energy balance. Therefore in the future, using our model it will be possible to predict mean maximum UHI intensity in other cities, which have land-use features similar to Szeged.Received September 26, 2002; revised February 25, 2003; accepted March 22, 2003 Published online July 30, 2003     

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  $\mathrm{{m\,s}}^{-1})$ .     

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.     

站点选择对城市热岛效应的影响分析

根据城郊站间距离等对辽宁56个气象站进行筛选,采用城郊温差法对选站和未选站时郊区站点数量有变化的大连、丹东、锦州和铁岭4个城市的月和年热岛特征进行分析。结果表明：对于年热岛特征而言,1980-2011年,大连、丹东、锦州和铁岭4个城市选站和未选站时热岛强度大小明显不同,但其变化趋势基本一致。4城市相比,选站和未选站时后均表现为铁岭年热岛强度最大,多年平均值分别为1.53 ℃和1.85 ℃,其变化范围分别为1.17-1.80 ℃和1.55-2.15 ℃,变化幅度分别为0.63 ℃和0.60 ℃。1980-2011年,铁岭热岛强度等级发生变化的年份最多,占25 %。总体来讲,选站对年热岛特征影响不是很大。对于月热岛特征而言,大连选站和未选站时热岛强度变化较大,但其他3个城市选站选站和未选站时变化不大,尤其是锦州选站和未选站时变化基本一致。4城市均有冬半年热岛效应明显,夏半年热岛效应不明显的特征。1980-2011年,各月平均热岛强度等级在选站和未选站时变化均较大,最大为丹东10月和11月,等级变化的年份占90.6 %,总体而言,选站对月热岛强度特征影响较大。     

Numerical Modelling of Urban Heat-Island Intensity

A three-dimensional, non-hydrostatic, high-resolution numerical model was used toanalyse urban heat-island (UHI) intensity in an idealised but realistic configuration.The urban area was 20 km square and lay on flat land at about latitude 50° Nin a maritime climate. In the model the urban area was represented by anomalies ofalbedo, anthropogenic heat flux, emissivity, roughness length, sky-view factor (SVF),surface resistance to evaporation (SRE) and thermal inertia. A control simulationincluded all these factors and the resultant UHI structure, energetics and intensitywere validated against observations. The results also compared favourably withearlier simulations.A series of experiments was conducted in which successively one of the anomaliesthat represented the urban area was omitted from the control simulation so as toprovide the basis for an assessment of its effect. In daytime the individual effectsdue to albedo, anthropogenic heat, emissivity, SVF and thermal inertia ranged from0.2 to 0.8 °C. In common with albedo, anthropogenic heat, emissivity andSVF, the SRE aided the formation of a UHI; it was also the most important factorin increasing its intensity. The roughness length had the opposite effect. At nightemissivity, roughness length, SVF and SRE had effects ranging from 0.3 to0.75 °C, but the largest effect (2 °C) was due to the anthropogenicheat. These results showed a difference in the causes of daytime and nighttime UHIs.In daytime the roughness length and SRE were the most important factors affectingUHI intensity; at night the anthropogenic heat was the most important. The simulationssuggested that the size of the urban area had a minimal effect on UHI intensity.     

北京中央商务区(CBD)城市热岛效应的研究

利用2012~2013年北京中央商务区（Central Business District,CBD）加密观测资料,分析CBD区域城市热岛（Urban Heat Island,UHI）强度日变化和空间变化特征及其影响因子。研究发现,CBD区域气温高于周边自动站气温,平均偏高0.64℃;CBD区域城市热岛强度呈现夜间强、白天弱的现象,中午甚至存在“城市冷岛”现象。季节平均UHI日变化表现为:在夜间,秋季最强,冬季次之,春季和夏季较弱;在白天,夏季最强,冬季次之,春季和秋季较弱。相对于晴朗无风天气,雾、雨、大风等天气对城市热岛有抑制作用,并结合小波分析结果发现,秋季城市热岛强度强于冬季是由于冬季雾、雨、大风等天气过程发生比例较高的缘故。CBD区域城市热岛空间变化特征研究发现,花园、学校等绿地有助于缓解城市热岛效应。雾日、雨日和大风日的CBD区域城市热岛强度空间变化标准差比晴朗无风日小。