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1.
利用阳泉市3个国家级气象站资料分析了阳泉市城市热岛效应的年际变化、季节变化、月变化和日变化特征,结果表明：阳泉市存在弱的城市热岛效应,1972年-2011年平均热岛强度0．554℃。阳泉市热岛强度冬、秋季强,春、夏季弱;12月最强,5月最弱;阳泉市热岛强度整体呈显著上升趋势,热岛强度的增加主要是由于夏季热岛强度的增强。热岛强度日变化表现为12时最小,从傍晚开始随降温逐渐增大,到早晨气温降到最低时最大,日出之后迅速减小;2008年-2011年最强热岛强度出现在2010年1月14日08时达7．9℃。阳泉市主要城市发展因子与霾日数、气温呈显著正相关,在目前的经济发展水平条件下,城市化发展可能使阳泉城市温度增高,城市绿地面积的增加可能对热岛效应有缓解。 相似文献
2.
Urban Heat Island and Boundary Layer Structures under Hot Weather Synoptic Conditions: A Case Study of Suzhou City, China 总被引：3，自引：0，他引：3
A strong urban heat island (UHI) appeared in a hot weather episode in Suzhou City during the period from 25 July to 1 August 2007. This paper analyzes the urban heat island characteristics of Suzhou City under this hot weather episode. Both meteorological station observations and MODIS satellite observations show a strong urban heat island in this area. The maximum UHI intensity in this hot weather episode is 2.2℃, which is much greater than the summer average of 1.0℃ in this year and the 37-year (from 1970 to 2006) average of 0.35℃. The Weather Research and Forecasting (WRF) model simulation results demonstrate that the rapid urbanization processes in this area will enhance the UHI in intensity, horizontal distribution, and vertical extension. The UHI spatial distribution expands as the urban size increases. The vertical extension of UHI in the afternoon increases about 50 m higher under the year 2006 urban land cover than that under the 1986 urban land cover. The conversion from rural land use to urban land type also strengthens the local lake-land breeze circulations in this area and modifies the vertical wind speed field. 相似文献
3.
利用2001—2010年覆盖安徽省的MODIS数据,选取在气候、地理、城市化等方面具有代表性的合肥、芜湖、阜阳作为研究对象,并结合GIS技术,分析地表温度的日变化及季节变化特征,得到安徽省代表城市热岛效应的时空分布。结果表明:安徽省省会合肥的热岛效应最为显著,安徽省南部代表城市芜湖的热岛效应强于北部代表城市阜阳, 同时具有显著的日变化和季节变化特征。近10年来,安徽代表城市热岛面积和热岛强度均呈增加趋势,但合肥热岛强度大于3 ℃的极端热岛效应有一定缓解。白天大片水体对缓解城市的热岛效应作用明显,而夜晚则不明显,甚至成为地表温度的高值中心。夏季地表温度与归一化植被指数的负相关最显著,即提高城市植被覆盖度对降低地表温度和缓解城市热岛效应有重要影响。 相似文献
4.
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. 相似文献
5.
为了解浙江省城市增暖的地域差异及其受热岛效应影响的程度,对杭州、宁波、温州3个城市的气候变化趋势进行了分析。为减少观测资料的非均一性,运用最优插值法将浙江省1971—2010年62个站点资料的信息植入到NCEP月平均温度资料的背景场中,得到格距为5 km的格点资料。研究结果表明:3个城市在近40年表现出明显的增温趋势,城区增温明显快于乡村的。各地区增温率的时空变化存在一定差异,与人口密度、工业总产值、用电量及车辆拥有量等经济数据的分布及变化基本一致。沿海城市温州增温率最小,但城乡差异最明显。同样为沿海城市的宁波,由于经济发展地域差别较小,虽表现最明显的增温,但城市热岛效应最弱。3个城市的热岛效应及热岛增温贡献率均存在明显的季节变化:杭州的热岛效应呈春、夏、冬、秋依次减弱的变化规律;温州则呈春、秋、冬、夏依次减弱的变化规律;宁波类似于城市群发展的模式,导致其热岛效应较不明显,以夏季和秋季最强。城市化发展引起的增暖占有较大的比重,杭州和温州年平均气温的增加大约有1/3是由城市化引起的,其中杭州夏季热岛增温贡献率达到2/3以上,温州春、夏季热岛增温贡献率均达到43%左右,宁波城市热岛增温贡献率相对较小,夏季的最大,为20%左右。 相似文献
6.
1978—2008年城市化对北京地区气温变化影响的初步分析 总被引：2，自引：2，他引：0
应用北京地区20个常规站1978-2008年经均一性序列多元分析方法均一化处理的气温数据,初步分析了北京地区城市化对年平均和不同季节日最高、最低以及平均气温的影响。结果表明,1978—2008年,年平均日最低、平均气温空间分布自北向南、自西向东,温度逐渐升高,在城区达到最高,日最高气温表现为从西向东南逐步升高,在城区形成较为明显的热岛。温度变化趋势表明,各站日最低气温、平均气温、最高气温均呈升温趋势。城市化对北京地区城区及近郊区站点日平均气温和最低气温影响最大,对自北部佛爷顶至昌平到城区一带站点的最高气温影响最大。城市化对北京(观象台)站的增温影响最为明显,对城区站点温度平均的增温影响次之,对全市站点温度平均的增温影响最小。城市化对观象台站、城区站点平均、全市站点平均日平均气温、最低气温的年平均、各季节均非常显著,其中在秋季影响最大,对日最高气温的影响则是在夏季最大。 相似文献
7.
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。 相似文献
8.
利用1972－2011年阳泉市3个国家级气象站资料、2011年36个乡镇区域自动站气温资料,分析了阳泉市城市热岛效应的年际变化、季节变化、月变化和日变化特征。结果表明：阳泉市存在弱的城市热岛效应,1972－2011年平均热岛强度0.554 ℃。阳泉市城市热岛强度整体呈显著上升趋势,热岛强度的增加主要是由于夏季热岛强度的增强;热岛强度冬、秋季强,春、夏季弱;12月最强,5月最弱;热岛强度日变化表现为12时最小,从傍晚开始随降温逐渐增大,到早晨气温降到最低时最大,日出之后迅速减小;2008－2011年最强热岛强度出现在2010年1月14日08时,达7.9 ℃。阳泉在升温天气热岛强度变幅增大,易在早晨形成较强城市热岛,下午形成城市冷岛;降温天气热岛强度变幅减小;温度变化较小时则易维持弱的城市热岛。阳泉市主要城市发展因子与霾日数、气温呈显著正相关,在目前的经济发展水平条件下,阳泉市城市化发展可能使城市温度增高,城市绿地面积的增加可能对热岛效应有缓解作用。 相似文献
9.
苏州夏季城市热岛现状及影响因子分析研究 总被引：9，自引：4，他引：5
利用2007年夏季常规和自动站气象观测资料,分析研究苏州城市热岛及其与影响因子关系.气温分布表明,市中心干将桥气温相对较高,而靠近太湖的新区镇湖镇、东山等郊区气温相对较低.苏州城市热岛强度日变化呈现双峰分布,两个峰值分别出现在10时和20时左右,最低值出现在16时左右.热岛强度与气象条件关系分析表明:(1)热岛强度受云量的影响较大;(2)与城区气温分布关系密切,相关系数为0.62;(3)与风向有关,城区风向为西风时的热岛强度大于东风时热岛强度;而城区热岛强度与风速关系不明显.另外相关站点的合理选取对城市热岛研究也十分重要. 相似文献
10.
北京"城市热岛"效应现状及特征 总被引：37，自引：16，他引：21
利用2002年北京自动气象站资料,对北京“城市热岛”效应现状进行了分析。为了与20世纪70年代的结果相比较,选择城区代表站为天安门广场站,城郊代表站为朝阳气象站站。与20世纪70年代相比,目前北京的“城市热岛”表现出一些新特点:1)利用城区与城郊日均温差表示的“城市热岛”强度的统计结果表明,现在北京的“城市热岛”效应在夏季最强,秋、冬季次之,春季最弱,2)除夏季“城市热岛”整天存在(午后的平均强度在2℃左右)以外,其他季节的午后,天安门广场地区经常出现“城市冷岛”现象。3)北京“城市热岛”消失的极限风速没有发生系统性变化,当风速>3级时,北京“城市热岛”基本上消失。作者还研究了北京“城市热岛”形成和消失的日变化特征,以及“城市热岛”强度对风速等气象要素变化的响应特征。值得指出的是,对强“城市热岛”的个案分析显示,冬季夜晚“城市热岛”强度经常表现出较大的波动性,与此相伴随,城郊地面风出现风向突变和风速的阵性现象。 相似文献
11.
Manju Mohan Yukihiro Kikegawa B. R. Gurjar Shweta Bhati Narendra Reddy Kolli 《Theoretical and Applied Climatology》2013,112(3-4):647-658
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. 相似文献
12.
Dimitris K. Papanastasiou Constantinos Kittas 《Theoretical and Applied Climatology》2012,107(3-4):407-416
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. 相似文献
13.
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。 相似文献
14.
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。 相似文献
15.
Summary This study investigates the characteristics of the daily maximum urban heat island (UHI) intensity in the six largest cities of South Korea (Seoul, Incheon, Daejeon, Daegu, Gwangju, and Busan) during the period 1973–2001. The annually-averaged daily maximum UHI intensity in all cities tends to increase with time, but the rate of increase differs. It is found that the average annual daily maximum UHI intensity tends to be smaller in coastal cities (Incheon and Busan) than in inland cities (Daejeon, Daegu, and Gwangju), even if a coastal city is larger than an inland city.A spectral analysis shows a prominent diurnal cycle in the UHI intensity in all cities and a prominent annual cycle in coastal cities. A multiple linear regression analysis is undertaken in order to relate the daily maximum UHI intensity to the maximum UHI intensity on the previous day (PER), wind speed (WS), cloudiness (CL), and relative humidity (RH). In all cities, the PER variable is positively correlated with the daily maximum UHI intensity, while WS, CL, and RH variables are negatively correlated with it. The most important variable in all cities is PER, but the relative importance of the other three variables differs depending on city. The total variance explained by the multiple linear regression equation ranges from 29.9% in Daejeon to 44.7% in Seoul. A multidimensional scaling analysis performed with a correlation matrix obtained using the daily maximum UHI intensity data appears to distinguish three city groups. These groupings are closely connected with distances between cities. A multidimensional scaling analysis undertaken using the normalized regression coefficients obtained from the multiple linear regression analysis distinguishes three city groups. Notably, Incheon and Busan form one group, whose points in the two-dimensional space are very close. The results of a cluster analysis performed using the multivariate data of PER, WS, RH, and CL are consistent with those of the multidimensional scaling analysis. The analysis results in this study indicate that the characteristics of the UHI intensity in a coastal city are in several aspects different from those in an inland city. 相似文献
16.
17.
The association between heat waves and the urban heat island effect can increase the impact on environment and society inducing biophysical hazards. Heat stress and their associated public health problems are among the most frequent. This paper explores the heat waves impact on surface urban heat island and on the local economy loss during three heat periods in Cluj-Napoca city in the summer of 2015. The heat wave events were identified based on daily maximum temperature, and they were divided into three classes considering the intensity threshold: moderate heat waves (daily maximum temperature exceeding the 90th percentile), severe heat waves (daily maximum temperature over the 95th percentile), and extremely severe heat waves (daily maximum temperature exceeding the 98th percentile). The minimum length of an event was of minimum three consecutive days. The surface urban heat island was detected based on land surface temperature derived from Landsat 8 thermal infrared data, while the economic impact was estimated based on data on work force structure and work productivity in Cluj-Napoca derived from the data released by Eurostat, National Bank of Romania, and National Institute of Statistics. The results indicate that the intensity and spatial extension of surface urban heat island could be governed by the magnitude of the heat wave event, but due to the low number of satellite images available, we should consider this information only as preliminary results. Thermal infrared remote sensing has proven to be a very efficient method to study surface urban heat island, due to the fact that the synoptic conditions associated with heat wave events usually favor cloud free image. The resolution of the OLI_TIRS sensor provided good results for a mid-extension city, but the low revisiting time is still a drawback. The potential economic loss was calculated for the working days during heat waves and the estimated loss reached more than 2.5 mil. EUR for each heat wave day at city scale, cumulating more than 38 mil. EUR for the three cases considered. 相似文献
18.
应用卫星资料分析苏州夏季城市热岛效应 总被引：5，自引：3，他引：2
利用苏州2004-2007年自动气象站资料以及购自中国科学院对地观测与数字地球科学中心的Landsat 5卫星(25 m分辨率)资料,分析研究苏州地区城市热岛总体特点以及分布规律,并对可能变化做一些探讨.分析认为,由于城市热岛效应,苏州地区气温呈中间高两侧低的分布特征,气温高值中心呈西北-东南走向,沿太湖及沿江地区气温相对较低;苏州城市地表温度呈明显的放射型分布,以市区中心向四周呈放射状分布. 相似文献
19.
Characterization and estimation of urban heat island at Toronto: impact of the choice of rural sites 总被引：3，自引：2，他引：1
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. 相似文献