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.     

Urban heat island effect on annual mean temperature during the last 50 years in China

Summary Based on Chinas fifth population survey (2000) data and homogenized annual mean surface air temperature data, the urban heat island (UHI) effect on the warming during the last 50 years in China was analyzed in this study. In most cities with population over 10⁴, where there are national reference stations and principal stations, most of the temperature series are inevitably affected by the UHI effect. To detect the UHI effect, the annual mean surface air temperature (SAT) time series were firstly classified into 5 subregions by using Rotated Principal Components Analysis (RPCA) according to its high and low frequency climatic change features. Then the average UHI effect on each subregions regional annual mean STA was studied. Results indicate that the UHI effect on the annual mean temperatures includes three aspects: increase of the average values, decrease of variances and change of the climatic trends. The effect on the climatic trends is different from region to region. In the Yangtze River Valley and South China, the UHI effect enhances the warming trends by about 0.011°C/decade. In the other areas, such as Northeast, North-China, and Northwest, UHI has little impact on the warming trends of the regional annual temperature; while in the Southwest of China, introducing UHI stations slows down the warming trend by –0.006°C/decade. But no matter what subregion it is, the total warming/cooling of these effects is much smaller than the background change in regional temperature. The average UHI effect for the entire country, during the last 50 years is less than 0.06°C, which agrees well with the IPCC (2001). This suggests that we cannot conclude that urbanization during the last 50 years has had much obvious effect on the observed warming in China.     

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  $\mathrm{{m\,s}}^{-1})$ .     

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。     

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.     

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.     

Quantifying the influence of land-use and surface characteristics on spatial variability in the urban heat island

The urban thermal environment varies not only from its rural surroundings but also within the urban area due to intra-urban differences in land-use and surface characteristics. Understanding the causes of this intra-urban variability is a first step in improving urban planning and development. Toward this end, a method for quantifying causes of spatial variability in the urban heat island has been developed. This paper presents the method as applied to a specific test case of Portland, Oregon. Vehicle temperature traverses were used to determine spatial differences in summertime ~2 m air temperature across the metropolitan area in the afternoon. A tree-structured regression model was used to quantify the land-use and surface characteristics that have the greatest influence on daytime UHI intensity. The most important urban characteristic separating warmer from cooler regions of the Portland metropolitan area was canopy cover. Roadway area density was also an important determinant of local UHI magnitudes. Specifically, the air above major arterial roads was found to be warmer on weekdays than weekends, possibly due to increased anthropogenic activity from the vehicle sector on weekdays. In general, warmer regions of the city were associated with industrial and commercial land-use. The downtown core, whilst warmer than the rural surroundings, was not the warmest part of the Portland metropolitan area. This is thought to be due in large part to local shading effects in the urban canyons.     

Spatio-temporal variability of moisture conditions within the Urban Canopy Layer

Summary ¶Re-analysed data from an urban climate research project in Munich, Germany, were used to investigate the spatio-temporal variability of moisture conditions (expressed here in vapour pressure VP) within the Urban Canopy Layer UCL. The results, which apply to three main sites and additional subsidiary ones, cover both summer and winter months. The summer month variation of VP is characterised by higher monthly mean values of VP for all three sites, howbeit with considerable inter-site differences. The temporal variability of mean VP values at diurnal time scales is also examined. With respect to the UCL, they reveal different amplitudes and times of occurrence of their extreme values. In addition, results of car traverses performed during clear sky conditions in downtown Munich show a remarkable small-scale spatio-temporal variability of VP.In relation to a sealed downtown site within a courtyard in Munich, a time-dependent urban moisture excess (UME) was formed. A positive correlation between UME and the urban heat island (UHI) could be verified in general. However, it was slightly negative with a very low coefficient of determination in the summer month when the maximum UME preceded the maximum UHI up to 5hrs. As example for the effects of air moisture on the urban climate within the UCL, the role of VP on a thermal index (physiologically equivalent temperature PET) was investigated. Based on one-year data from another urban climate project in Munich, a positive correlation between PET and VP was found, although the coefficient of determination was somewhat low. However, during a human-biometeorological case study on a typical summer day in the northern downtown of Freiburg, a medium-sized city in southwest Germany, PET and VP showed a negative correlation (possibly because the specific temporal course of VP at the measuring points was mainly influenced by thermally induced turbulence).     

Urban climate simulation by incorporating satellite-derived vegetation cover distribution into a mesoscale meteorological model

Summary We simulated urban climate with surface boundary conditions based on satellite remote sensing (RS) data. Most previous mesoscale meteorological modeling studies use land-use data instead as the surface boundary conditions. However, small patches of vegetation-cover, such as roadside trees and garden trees, are excluded from the land-use data. Therefore, we made a fractional vegetation cover (FVC) dataset with these small patches of vegetation-cover from RS data, and then simulated the urban heat island in Tokyo with FVC data as new surface boundary conditions. In addition, we compared the above simulation results with results from a simulation that used only land-use data. The comparison shows that the air temperature with the new boundary condition is up to 1.5°C lower than that with the old boundary condition. Furthermore, the new boundary condition led to predicted air temperatures closer to the measured temperatures than those with the old boundary condition. Therefore, it is important for urban climate simulations to include small vegetation cover.     

Estimation of urban heat island intensity using biases in surface air temperature simulated by a nonhydrostatic regional climate model

This study demonstrates that urban heat island (UHI) intensity can be estimated by comparing observational data and the outputs of a well-developed high-resolution regional climate model. Such an estimate is possible because the observations include the effects of UHI, whereas the model used does not include urban effects. Therefore, the errors in the simulated surface air temperature, defined as the difference between simulated and observed temperatures (simulated minus observed), are negative in urban areas but 0 in rural areas. UHI intensity is estimated by calculating the difference in temperature error between urban and rural areas. Our results indicate that overall UHI intensity in Japan is 1.5 K and that the intensity is greater in nighttime than in daytime, consistent with the previous studies. This study also shows that root mean square error and the magnitude of systematic error for the annual mean temperature are small (within 1.0 K).     

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).     

URBAN HEAT ISLAND EFFECT AND ITS CONTRIBUTION TO OBSERVED TEMPERATURE INCREASE AT WUHAN STATION, CENTRAL CHINA

Based on an in-homogeneity adjusted dataset of the monthly mean temperature, minimum and maximum temperature, this paper analyzes the temporal characteristics of Urban Heat Island (UHI) intensity at Wuhan Station, and its impact on the long-term trend of surface air temperature change recorded during 1961–2015 by using an urban-rural method. Results show that UHI effect is obvious near Wuhan Station in the past 55 years, especially for minimum temperature. The strongest UHI intensity occurs in summer and the weakest in winter. For the period 1961–2004, UHI intensity undergoes a significant increase near the urban station, with the increase especially large for the period 1988–2004, but the last 10 years witness a significant decrease, with the decrease in minimum temperature being more significant than that of maximum temperature. The annual mean urban warming and its contribution to overall warming are 0.18?C/10yr and 48.8% respectively for the period 1961–2015, with a more significant and larger urbanization effect seen in Tmin than Tmax. Thus, a large proportion warming, about half of the overall increase in annual mean temperature, as observed at the urban station, can be attributed to the rapid urbanization in the past half a century.     

城市化发展与气象环境影响的观测与分析研究

通过分析2002年7月12日和2006年5月4日Landsat-5高分辨率资源卫星资料,表明南京市热岛分布特征与南京市的城市建设和区域经济发展的空间特征相当一致,土地利用类型、地表反照率、叶面积指数、植被覆盖度等地表参数分布与城市热岛分布相吻合.运用数值模拟手段对南京城市化对边界层特性产生的影响进行研究,结果表明,随着城市发展,地表反照率减小、植被减少、地表湿度降低,使蒸发耗热减小、感热通量增多,城市波恩比增加,地表和大气间的热交换增强.     

北京"城市热岛"效应现状及特征

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。     

北京夏季强热岛分析及数值模拟研究

应用北京地区20个常规地面气象站、2个自动气象站和中国科学院大气物理研究所325m气象铁塔的资料,对北京2003年7月热岛状况进行了统计分析,发现北京夏季夜间存在强热岛效应,夏季夜间存在强热岛效应的天数占到了1/3,强弱热岛天数合计占到了大约4/5。进一步分析7月1日强热岛特征及其气象影响因子,结果表明：夜间存在强热岛时,郊区所有测站的地面气温都要低于主城区地面气温,城市强热岛的高温中心在天安门和白家庄连线的主城区;白天日照充足的晴夜,日落后城区320m以下低层大气存在逆温和弱的风速,城区地面气温下降速率和幅度均远小于郊区,城市强热岛因此得以形成和维持。日出后至正午,北京北部郊区日照时间比城区长,郊区地面大气得到来自太阳辐射的能量多于城区,而太阳辐射的加热作用使城区低层大气逆温消失,大气稳定度减弱,并使郊区地面气温上升速率和幅度大于城区,最终导致夜间出现的强热岛减弱、消失。此外,应用MM5模式对强热岛进行了初步数值模拟研究,发现在MM5中考虑城市人为热和热储存,可以改善模式对热岛的数值模拟,表明城市人为热和热储存在夏季强热岛的形成中有重要作用。     

Urban Heat Island and Boundary Layer Structures under Hot Weather Synoptic Conditions: A Case Study of Suzhou City, China

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.     

山西省阳泉市城市热岛效应特征分析

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。     

Reduction of forecast error for global numerical weather prediction by The Florida State University (FSU) Superensemble

Summary The skill of the FSU Superensemble technique as applied to global numerical weather prediction is evaluated extensively in this paper. The global mass and motion fields for year 2000 and precipitation over the domain 55S to 55N for year 2001, as predicted by the Superensemble, the ensemble member models, and the mean of the ensemble members, are evaluated by standard statistical measures of skill to determine the performance of the Superensemble in relation to the other models. The member models are global forecast models from 5 of the worlds operational forecast centers in addition to the FSU global spectral model. For precipitation 5 additional versions of the FSU global model are utilized in the ensemble, as defined by different initial conditions provided by various physical initialization algorithms. Statistical parameters calculated for the mass and motion fields include root mean square (RMS) error, systematic error (or bias), and anomaly correlation. These are applied to the mean sea level pressure, 500hPa heights, and the wind fields at 850hPa and 200hPa. Statistical parameters that were calculated for precipitation include RMS error, correlation, equitable threat score (ETS), and a special definition of bias appropriate for the precipitation field. For the mass and motion fields the performance of the Superensemble was considered for the annual global case, as well as for each hemisphere (north and south) and for each of the four seasons. For precipitation only the annual case was considered over the domain cited above.For the mass and motion fields the RMS calculations showed the Superensemble to be superior (to have the smallest total forecast error) in all comparisons to the ensemble member models, and to be superior to the ensemble mean in the vast majority of comparisons. Performance in comparison to the other models was generally better in the Southern Hemisphere than in the Northern Hemisphere, and better in the transition seasons of fall and spring than in the extreme seasons of winter and summer. The Superensemble had the best success with mean sea level pressure, followed in order by 500hPa geopotential heights, 850hPa winds, and 200hPa winds.In the calculations of 500hPa geopotential height anomaly correlation the Superensemble had higher scores in all comparisons to the ensemble member models, as well as higher scores in the majority of comparisons to the ensemble mean. As with the RMS error results, the Superensemble performed better in the Southern Hemisphere than in the Northern Hemisphere, and better in fall than in summer, in comparison to the other models. The superior anomaly correlation scores of the Superensemble attest to the ability of the model to forecast daily perturbations from the climatological means, perturbations that are associated with transient synoptic scale features, given the horizontal resolution in the forecast models.In terms of systematic error reduction the Superensemble produces its most impressive results. Annual global mean sea-level pressure systematic errors for day 5 forecasts are generally in the range of ±1hPa (compared to errors as high as 8hPa in other models), and day 2 forecasts of 500hPa geopotential height produced systematic errors generally in the range of ±10 meters (compared to errors as high as 60 meters in other models). The Superensemble was able to reduce systematic errors in forecasts of a variety of important features in the global mass and motion fields: surface equatorial trough, wave amplitude in geopotential heights at 500hPa, trade winds and Somali Jet at 850hPa, mid-latitude westerlies, subtropical jet, and Tropical Easterly Jet (TEJ) at 200hPa.In terms of forecasting precipitation the Superensemble outperforms all ensemble member models and the ensemble mean in terms of RMS error, correlation coefficient, equitable threat score, and bias. The superior correlation scores indicate that the Superensemble is more reliable than the other models in predicting perturbations in the area distribution of precipitation, perturbations that are essentially associated with migrant synoptic scale disturbances, considering the horizontal resolution of the forecast models.The Superensemble is a valuable tool for significantly improving upon the global model forecasts of the worlds operational forecast centers. These forecasts are used daily as important guidance in making weather forecasts in all regions of the world. This paper will demonstrate that the Superensemble improves upon the ensemble member model forecasts: (1) in a statistical sense considering broad areas of the globe, (2) in a synoptic climatology sense through focus on the improved forecasts of climatological features seen in the global mass and motion fields, (3) in a synoptic sense through use of anomaly correlation and correlation coefficient where improvement is demonstrated in the forecasts of perturbations from mean fields which are essentially associated with transient synoptic scale disturbances.     

Transition from the Southern to the Northern Hemisphere summer of zones of active convection over the Congo Basin

Summary This study used monthly rainfall totals for the period 1961 to 1988 and pentad OLR values for the period 1974 to 1991 to study the structure and transition of active convention across the Congo Basin (10°S–5°N, 15°E–35°E) from the southern to the northern hemisphere summer. This involved the examination of map patterns and cross-sections of monthly rainfall and pentad OLR data.The results from the study indicated that there were two seasons observed over the Congo Basin; one is the wet season lasting from September to April and the other a dry season covering the rest of the year. The onset of the wet season takes place rapidly with active convection spreading very quickly to the south near latitude 20°S. This is due to the formation of the meridional (north–south) branch of the ITCZ over this region.This study has confirmed that the annual rainfall over the Congo Basin is reliable with the coefficient of variation of less than 30%. The wet seasons (e.g., SON and DJF) also show reliable rainfall occurrence but the dry season (e.g., JJA) has low reliability.The anomalously wet seasons are characterised by a relatively slow transition rate (1° latitude per pentad) of zones of active convection resulting in a late withdrawal of the rainy season while the dry seasons show a rapid transition rate with an early withdrawal of zones of active convection.High-rainfall months (>200mm) are concentrated within the Southern Hemisphere summer months. These high-rainfall months progress from the equator to the southern latitude following generally the movement of the overhead sun.The results further revealed that the years 1987/1984 had the lowest/highest mean OLR values over the Congo Basin within the period 1979 to 1991. The rates of transition of the zones of low OLR values were 0.9/5.0 degrees of latitude during 1987/1984, respectively.Received June 18, 2002; revised September 30, 2002; accepted November 21, 2002 Published online: June 12, 2003     

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。