scholarly journals Investigation of Urban Air Temperature and Humidity Patterns during Extreme Heat Conditions Using Satellite-Derived Data

2015 ◽  
Vol 54 (11) ◽  
pp. 2245-2259 ◽  
Author(s):  
Leiqiu Hu ◽  
Andrew J. Monaghan ◽  
Nathaniel A. Brunsell
Keyword(s):  
Urban Heat Island ◽  
Air Temperature ◽  
Urban Land Use ◽  
Extreme Heat ◽  
Heat Island ◽  
Near Surface ◽  
Vegetation Fraction ◽  
Dewpoint Temperature ◽  
Temperature And Humidity ◽  
Urban Heat

AbstractExtreme heat is a leading cause of weather-related human mortality. The urban heat island (UHI) can magnify heat exposure in metropolitan areas. This study investigates the ability of a new MODIS-retrieved near-surface air temperature and humidity dataset to depict urban heat patterns over metropolitan Chicago, Illinois, during June–August 2003–13 under clear-sky conditions. A self-organizing mapping (SOM) technique is used to cluster air temperature data into six predominant patterns. The hottest heat patterns from the SOM analysis are compared with the 11-summer median conditions using the urban heat island curve (UHIC). The UHIC shows the relationship between air temperature (and dewpoint temperature) and urban land-use fraction. It is found that during these hottest events 1) the air temperature and dewpoint temperature over the study area increase most during nighttime, by at least 4 K relative to the median conditions; 2) the urban–rural temperature/humidity gradient is decreased as a result of larger temperature and humidity increases over the areas with greater vegetation fraction than over those with greater urban fraction; and 3) heat patterns grow more rapidly leading up to the events, followed by a slower return to normal conditions afterward. This research provides an alternate way to investigate the spatiotemporal characteristics of the UHI, using a satellite remote sensing perspective on air temperature and humidity. The technique has potential to be applied to cities globally and provides a climatological perspective on extreme heat that complements the many case studies of individual events.

scholarly journals Estimating urban heat island effects on near-surface air temperature records of Uccle (Brussels, Belgium): an observational and modeling study

2011 ◽  
Vol 6 (1) ◽  
pp. 27-34 ◽  
Author(s):  
R. Hamdi ◽  
H. Van de Vyver
Keyword(s):  
Remote Sensing ◽  
Urban Heat Island ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Heat Island ◽  
Urban Bias ◽  
Near Surface ◽  
Urban Warming ◽  
Urban Heat ◽  
Weather Stations

Abstract. In this letter, the Brussels's urban heat island (UHI) effect on the near-surface air temperature time series of Uccle (the national suburban recording station of the Royal Meteorological Institute of Belgium) was estimated between 1955 and 2006 during the summer months. The UHI of Brussels was estimated using both ground-based weather stations and remote sensing imagery combined with a land surface scheme that includes a state-of-the-art urban parameterization, the Town Energy Balance scheme. Analysis of urban warming based on the remote sensing method reveals that the urban bias on minimum air temperature is rising at a higher rate, 2.5 times (2.85 ground-based observed) more, than on maximum temperature, with a linear trend of 0.15 °C (0.19 °C ground-based observed) and 0.06 °C (0.06 °C ground-based observed) per decade respectively. The summer-mean urban bias on the mean air temperature is 0.8 °C (0.9 °C ground-based observed). The results based on remote sensing imagery are compatible with estimates of urban warming based on weather stations. Therefore, the technique presented in this work is a useful tool in estimating the urban heat island contamination in long time series, countering the drawbacks of an ground-observational approach.

scholarly journals Simultaneous assessment of the summer urban heat island in Moscow megacity based on in situ observations, thermal satellite images and mesoscale modeling

2019 ◽  
Vol 12 (4) ◽  
pp. 74-95 ◽  
Author(s):  
Mikhail I. Varentsov ◽  
Mikhail Y. Grishchenko ◽  
Hendrik Wouters
Keyword(s):  
Remote Sensing ◽  
Spatial Structure ◽  
Urban Heat Island ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Atmospheric Model ◽  
Heat Island ◽  
Near Surface ◽  
Urban Heat

This study compares three popular approaches to quantify the urban heat island (UHI) effect in Moscow megacity in a summer season (June-August 2015). The first approach uses the measurements of the near-surface air temperature obtained from weather stations, the second is based on remote sensing from thermal imagery of MODIS satellites, and the third is based on the numerical simulations with the mesoscale atmospheric model COSMO-CLM coupled with the urban canopy scheme TERRA_URB. The first approach allows studying the canopy-layer UHI (CLUHI, or anomaly of a near- surface air temperature), while the second allows studying the surface UHI (SUHI, or anomaly of a land surface temperature), and both types of the UHI could be simulated by the atmospheric model. These approaches were compared in the daytime, evening and nighttime conditions. The results of the study highlight a substantial difference between the SUHI and CLUHI in terms of the diurnal variation and spatial structure. The strongest differences are found at the daytime, at which the SUHI reaches the maximal intensity (up to 10°С) whereas the CLUHI reaches the minimum intensity (1.5°С). However, there is a stronger consistency between CLUHU and SUHI at night, when their intensities converge to 5–6°С. In addition, the nighttime CLUHI and SUHI have similar monocentric spatial structure with a temperature maximum in the city center. The presented findings should be taken into account when interpreting and comparing the results of UHI studies, based on the different approaches. The mesoscale model reproduces the CLUHI-SUHI relationships and provides good agreement with in situ observations on the CLUHI spatiotemporal variations (with near-zero biases for daytime and nighttime CLUHI intensity and correlation coefficients more than 0.8 for CLUHI spatial patterns). However, the agreement of the simulated SUHI with the remote sensing data is lower than agreement of the simulated CLUHI with in situ measurements. Specifically, the model tends to overestimate the daytime SUHI intensity. These results indicate a need for further in-depth investigation of the model behavior and SUHI–CLUHI relationships in general.

scholarly journals Seasonal Variations of the Urban Heat Island at the Surface and the Near-Surface and Reductions due to Urban Vegetation in Mexico City

2012 ◽  
Vol 51 (5) ◽  
pp. 855-868 ◽  
Author(s):  
Yu Yan Cui ◽  
Benjamin de Foy
Keyword(s):  
Air Temperature ◽  
Seasonal Variations ◽  
Vegetation Cover ◽  
Land Surface ◽  
Heat Island ◽  
Wet Season ◽  
Near Surface ◽  
Vegetation Fraction ◽  
Urban Heat ◽  
Surrounding Areas

AbstractThe contrast of vegetation cover in urban and surrounding areas modulates the magnitude of the urban heat island (UHI). This paper examines the seasonal variations of the UHI using the Moderate Resolution Imaging Spectroradiometer (MODIS), surface meteorological observations, and the Weather Research and Forecasting (WRF) model. A distinction is made between the land surface UHI observed by satellite and the near-surface UHI observed by measuring the air temperature. The land surface UHI is found to be high at night throughout the year but drops during the wet season. The daytime UHI is low or even exhibits an urban cool island throughout the year but increases during the wet season. The near-surface air temperature UHI trend is similar to the land surface temperature UHI at night. By day, however, the air temperature UHI remains constant throughout the year. Regression analysis showed that the daytime land surface UHI correlates with the difference in vegetation fraction between the urban and surrounding areas, and, to a lesser extent, with daytime insolation. At night, the UHI correlates with nighttime atmospheric stability and only weakly with differences in vegetation cover and daytime insolation. WRF simulations with the single-layer Urban Canopy Model were initialized with MODIS data, especially for the urban fraction parameter. The simulations correctly represented the distinct seasonal variations of both types of UHIs. The model was used to test the impact of changes in vegetation fraction in the urban area, indicating that increased vegetation would reduce both the land surface UHI and the air temperature UHI at night, as well as the land surface UHI during the daytime.

scholarly journals The Trend Inconsistency between Land Surface Temperature and Near Surface Air Temperature in Assessing Urban Heat Island Effects

2020 ◽  
Vol 12 (8) ◽  
pp. 1271 ◽  
Author(s):  
Tao Sun ◽  
Ranhao Sun ◽  
Liding Chen
Keyword(s):  
Surface Temperature ◽  
Urban Heat Island ◽  
Air Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Temporal Trends ◽  
Surface Air Temperature ◽  
Heat Island ◽  
Near Surface ◽  
Urban Heat

The credible urban heat island (UHI) trend is crucial for assessing the effects of urbanization on climate. Land surface temperature (LST) and near surface air temperature (SAT) have been extensively used to obtain UHI intensities. However, the consistency of UHI trend between LST and SAT has rarely been discussed. This paper quantified the temporal stability and trend consistency between Moderate Resolution Imaging Spectroradiometer (MODIS) LST and in situ SAT. Linear regressions, temporal trends and coefficients of variations (CV) were analyzed based on the yearly mean, maximum and minimum temperatures. The findings in this study were: (1) Good statistical consistency (R2 = 0.794) and the same trends were found only in mean temperature between LST-UHI and SAT-UHI. There are 54% of cities that showed opposite temporal trends between LST-UHI and SAT-UHI for minimum temperature while the percentage was 38% for maximum temperature. (2) The high discrepancies in temporal trends were observed for all cities, which indicated the inadequacy of LST for obtaining reliable UHI trends especially when using the maximum and minimum temperatures. (3) The larger uncertainties of LST-UHI were probably due to high inter-annual fluctuations of LST. The topography was the predominant factor that affected the UHI variations for both LST and SAT. Therefore, we suggested that SAT should be combined with LST to ensure the dependable temporal series of UHI. This paper provided references for understanding the UHI effects on various surfaces.

scholarly journals Effectiveness of Different Urban Heat Island Mitigation Methods and Their Regional Impacts

2017 ◽  
Vol 18 (11) ◽  
pp. 2991-3012 ◽  
Author(s):  
Ning Zhang ◽  
Yan Chen ◽  
Ling Luo ◽  
Yongwei Wang
Keyword(s):  
Boundary Layer ◽  
Urban Heat Island ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Green Roofs ◽  
Heat Island ◽  
Near Surface ◽  
Regional Impacts ◽  
Urban Heat ◽  
Cool Roofs

Abstract Cool roofs and green roofs are two popular methods to mitigate the urban heat island and improve urban climates. The effectiveness of different urban heat island mitigation strategies in the summer of 2013 in the Yangtze River delta, China, is investigated using the Weather Research and Forecasting (WRF) Model coupled with a physically based single-layer urban canopy model. The modifications to the roof surface changed the urban surface radiation balance and then modified the local surface energy budget. Both cool roofs and green roofs led to a lower surface skin temperature and near-surface air temperature. Increasing the roof albedo to 0.5 caused a similar effectiveness as covering 25% of urban roofs with vegetation; increasing the roof albedo to 0.7 caused a similar near-surface air temperature decrease as 50% green roof coverage. The near-surface relative humidity increased in both cool roof and green roof experiments because of the combination of the impacts of increases in specific humidity and decreases in air temperature. The regional impacts of cool roofs and green roofs were evaluated using a regional effect index. A regional impact was found for near-surface air temperature and specific/relative humidity when the percentage of roofs covered with high-albedo materials or green roofs reached a higher fraction (greater than 50%). The changes in the vertical profiles of temperature cause a more stable atmospheric boundary layer over the urban area; at the same time, the crossover phenomena occurred above the boundary layer due to the decrease in vertical wind speed.

scholarly journals Patterns of Urban Heat Island Effect in Adelaide: A Mobile Traverse Experiment

2017 ◽  
Vol 11 (4) ◽  
pp. 80
Author(s):  
Ehsan Sharifi ◽  
Ali Soltani
Keyword(s):  
Heat Stress ◽  
Urban Heat Island ◽  
Urban Policy ◽  
Urban Heat Island Effect ◽  
Urban Structure ◽  
Heat Island ◽  
Near Surface ◽  
Island Effect ◽  
Urban Rural ◽  
Urban Heat

Urban structure, hard surfaces and shortage of vegetation cause an artificial temperature increase in cities, known as the urban heat island effect. This paper determines the daily patterns of urban heat in Adelaide, Australia. The near-surface temperature profile of Adelaide was mapped in 60 journeys alongside a straight cross route connecting Adelaide Hills to the West Beach between 26 July and 15 August 2013. Results indicate that the most intense urban-rural temperature differences occurred during midnight in Adelaide. However, the afternoon urban heat had more temperature variation in the urban area. In the late afternoon, the near-surface urban heat fluctuates by 2°C within three kilometres and by 1.2°C in just one kilometer. Afternoon heat stress can vary based on space configurations and urban surface covers. Afternoon heat stress causes the highest heat load on urban dwellers. A better understanding of daily urban heat variations in cities assists urban policy making and public life management in the context of climate change.

scholarly journals A High-Resolution Monitoring Approach of Canopy Urban Heat Island using Random Forest Model and Multi-platform Observations

2021 ◽  
Author(s):  
Shihan Chen ◽  
Yuanjian Yang ◽  
Fei Deng ◽  
Yanhao Zhang ◽  
Duanyang Liu ◽  
...  
Keyword(s):  
High Resolution ◽  
Random Forest ◽  
Urban Heat Island ◽  
Air Temperature ◽  
Anthropogenic Heat ◽  
Heat Island ◽  
Model Framework ◽  
Rapid Urbanization ◽  
Urban Heat ◽  
The City

Abstract. Due to rapid urbanization and intense human activities, the urban heat island (UHI) effect has become a more concerning climatic and environmental issue. A high spatial resolution canopy UHI monitoring method would help better understand the urban thermal environment. Taking the city of Nanjing in China as an example, we propose a method for evaluating canopy UHI intensity (CUHII) at high resolution by using remote sensing data and machine learning with a Random Forest (RF) model. Firstly, the observed environmental parameters [e.g., surface albedo, land use/land cover, impervious surface, and anthropogenic heat flux (AHF)] around densely distributed meteorological stations were extracted from satellite images. These parameters were used as independent variables to construct an RF model for predicting air temperature. The correlation coefficient between the predicted and observed air temperature in the test set was 0.73, and the average root-mean-square error was 0.72 °C. Then, the spatial distribution of CUHII was evaluated at 30-m resolution based on the output of the RF model. We found that wind speed was negatively correlated with CUHII, and wind direction was strongly correlated with the CUHII offset direction. The CUHII reduced with the distance to the city center, due to the de-creasing proportion of built-up areas and reduced AHF in the same direction. The RF model framework developed for real-time monitoring and assessment of high-resolution CUHII provides scientific support for studying the changes and causes of CUHII, as well as the spatial pattern of urban thermal environments.

Effects of Dubai coastline extreme urbanization in land and sea on local near-surface climate variables patterns

2021 ◽  
Author(s):  
Emily Elhacham ◽  
Pinhas Alpert
Keyword(s):  
Urban Heat Island ◽  
Heat Island ◽  
Rapid Urbanization ◽  
Near Surface ◽  
Modis Data ◽  
The World ◽  
Urban Heat ◽  
Surface Urban Heat Island ◽  
Coastal Urbanization ◽  
The Impact

<p>Over a billion people currently live in coastal areas, and coastal urbanization is rapidly growing worldwide. Here, we explore the impact of an extreme and rapid coastal urbanization on near-surface climatic variables, based on MODIS data, Landsat and some in-situ observations. We study Dubai, one of the fastest growing cities in the world over the last two decades. Dubai's urbanization centers along its coastline – in land, massive skyscrapers and infrastructure have been built, while in sea, just nearby, unique artificial islands have been constructed.</p><p>Studying the coastline during the years of intense urbanization (2001-2014), we show that the coastline exhibits surface urban heat island characteristics, where the urban center experiences higher temperatures, by as much as 2.0°C and more, compared to the adjacent less urbanized zones. During development, the coastal surface urban heat island has nearly doubled its size, expanding towards the newly developed areas. This newly developed zone also exhibited the largest temperature trend along the coast, exceeding 0.1°C/year on average.</p><p>Overall, we found that over land, temperature increases go along with albedo decreases, while in sea, surface temperature decreases and albedo increases were observed particularly over the artificial islands. These trends in land and sea temperatures affect the land-sea temperature gradient which influences the breeze intensity. The above findings, along with the increasing relative humidity shown, directly affect the local population and ecosystem and add additional burden to this area, which is already considered as one of the warmest in the world and a climate change 'hot spot'.</p><p> </p><p><strong>References:</strong></p><p>E. Elhacham and P. Alpert, "Impact of coastline-intensive anthropogenic activities on the atmosphere from moderate resolution imaging spectroradiometer (MODIS) data in Dubai (2001–2014)", <em>Earth’s Future</em>, 4, 2016. https://doi.org/10.1002/2015EF000325</p><p>E. Elhacham and P. Alpert, "Temperature patterns along an arid coastline experiencing extreme and rapid urbanization, case study: Dubai", submitted.</p>

scholarly journals Spatiotemporal Changes in the Built Environment Characteristics and Urban Heat Island Effect in a Medium-Sized City, Chiayi City, Taiwan

2020 ◽  
Vol 12 (1) ◽  
pp. 365 ◽  
Author(s):  
Jou-Man Huang ◽  
Heui-Yung Chang ◽  
Yu-Su Wang
Keyword(s):  
Population Density ◽  
Built Environment ◽  
Urban Heat Island ◽  
Air Temperature ◽  
Area Ratio ◽  
Heat Island ◽  
Downtown Area ◽  
Green Area ◽  
Spatiotemporal Changes ◽  
Urban Heat

This study took Chiayi City—a tropical, medium-sized city—as an example to investigate the urban heat island (UHI) effect using mobile transects and built environment characteristics in 2018. The findings were compared to those from a study in 1999 to explore the spatiotemporal changes in the built environment characteristics and UHI phenomenon. The result for the UHI intensity (UHII) during the day was approximately 4.1 °C and at midnight was approximately 2.5 °C. Compared with the survey in 1999, the UHII during the day increased by approximately 1.3 °C, and the UHII at midnight decreased by approximately 1.2 °C. The trend of the spatial distribution of the increasing artificial area ratio (AAR) proved the importance of urban land use expansion on UHI. The results of the air temperature survey were incorporated with the nesting space in GIS to explore the role of built environment characteristics in UHI effects. The higher the population density (PD) and artificial area ratio (AAR) were, the closer the proximity was to the downtown area. The green area ratio (GAR) was less than 0.2 in the downtown area and increased closer to the rural areas. The built environment factors were analyzed in detail and correlated with the UHI effect. The air temperature in the daytime increased with the population density (PD) and artificial area ratio (AAR), but decreased with the green area ratio (GAR) (r = ±0.3–0.4). The result showed good agreement with previous studies.

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