Urban heat island diagnosis using ASTER satellite images and ‘in situ’ air temperature

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.

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

10.5194/amt-2021-301 ◽

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.

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Study and analysis of efficient green cover types for mitigating the air temperature and urban heat island effect

International Journal of Global Warming ◽

10.1504/ijgw.2018.090182 ◽

2018 ◽

Vol 14 (2) ◽

pp. 238 ◽

Cited By ~ 1

Author(s):

I. Chandramathy ◽

Jinu Louishidha Kitchley

Keyword(s):

Urban Heat Island ◽

Air Temperature ◽

Urban Heat Island Effect ◽

Heat Island ◽

Island Effect ◽

Green Cover ◽

Urban Heat

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

Sustainability ◽

10.3390/su12010365 ◽

2020 ◽

Vol 12 (1) ◽

pp. 365 ◽

Cited By ~ 2

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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Reflective Parking Lots for Microscale Urban Heat Island Mitigation

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198120919401 ◽

2020 ◽

Vol 2674 (8) ◽

pp. 663-671

Author(s):

Sushobhan Sen ◽

Juan Pablo Ricardo Mendèz-Ruiz Fernandèz ◽

Jeffery Roesler

Keyword(s):

Urban Heat Island ◽

Air Temperature ◽

Significant Proportion ◽

Climate Index ◽

Heat Island ◽

Universal Thermal Climate Index ◽

Parking Lots ◽

Parking Lot ◽

Thermal Climate ◽

Urban Heat

Paved surfaces, especially parking lots, occupy a significant proportion of the horizontal surface area in cities. The low albedo of many of these parking lots contribute to the urban heat island (UHI) and affect the local microclimate around them. The albedo of six parking lots in Champaign-Urbana, U.S., was measured using a ground-based albedometer and was found to vary between 0.18 and 0.28, with a statistically significant variation in albedo at different points within each parking lot. The numerical model ENVI-met was then employed to model the microclimate around one of these lots to examine the potential of increasing its albedo to mitigate UHI. The higher albedo decreased the air temperature over the parking lot by about 1°C. Furthermore, the Universal Thermal Climate Index (UTCI), which combines the effects of air temperature, reflected radiation, wind speed, clothing, metabolism, and humidity, demonstrated that increasing the albedo of the parking lot could improve overall pedestrian thermal comfort and even eliminate it during several hours of the day, and thus mitigate the UHI effect.

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Changes in the Water Temperature of Rivers Impacted by the Urban Heat Island: Case Study of Suceava City

Water ◽

10.3390/w12051343 ◽

2020 ◽

Vol 12 (5) ◽

pp. 1343 ◽

Cited By ~ 3

Author(s):

Andrei-Emil Briciu ◽

Dumitru Mihăilă ◽

Adrian Graur ◽

Dinu Iulian Oprea ◽

Alin Prisăcariu ◽

...

Keyword(s):

Heat Transfer ◽

Water Temperature ◽

Urban Heat Island ◽

River Water ◽

Air Temperature ◽

Stream Water ◽

Heat Island ◽

Urban Heat ◽

The City ◽

River Water Temperature

Cities alter the thermal regime of urban rivers in very variable ways which are not yet deciphered for the territory of Romania. The urban heat island of Suceava city was measured in 2019 and its impact on Suceava River was assessed using hourly and daily values from a network of 12 water and air monitoring stations. In 2019, Suceava River water temperature was 11.54 °C upstream of Suceava city (Mihoveni) and 11.97 °C downstream (Tişăuţi)—a 3.7% increase in the water temperature downstream. After the stream water passes through the city, the diurnal thermal profile of Suceava River water temperature shows steeper slopes and earlier moments of the maximum and minimum temperatures than upstream because of the urban heat island. In an average day, an increase of water temperature with a maximum of 0.99 °C occurred downstream, partly explained by the 2.46 °C corresponding difference between the urban floodplain and the surrounding area. The stream water diurnal cycle has been shifted towards a variation specific to that of the local air temperature. The heat exchange between Suceava River and Suceava city is bidirectional. The stream water diurnal thermal cycle is statistically more significant downstream due to the heat transfer from the city into the river. This transfer occurs partly through urban tributaries which are 1.94 °C warmer than Suceava River upstream of Suceava city. The wavelet coherence analyses and ANCOVA (analysis of covariance) prove that there are significant (0.95 confidence level) causal relationships between the changes in Suceava River water temperature downstream and the fluctuations of the urban air temperature. The complex bidirectional heat transfer and the changes in the diurnal thermal profiles are important to be analysed in other urban systems in order to decipher in more detail the observed causal relationships.

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Integration of a Building Energy Model in an Urban Climate Model and its Application

Boundary-Layer Meteorology ◽

10.1007/s10546-020-00569-y ◽

2020 ◽

Cited By ~ 1

Author(s):

Luxi Jin ◽

Sebastian Schubert ◽

Daniel Fenner ◽

Fred Meier ◽

Christoph Schneider

Keyword(s):

Temporal Variation ◽

Urban Heat Island ◽

Air Temperature ◽

Indoor Air ◽

Climate Model ◽

Building Energy ◽

Energy Model ◽

Heat Island ◽

Energy Fluxes ◽

Urban Heat

Abstract We report the ability of an urban canopy model, coupled with a regional climate model, to simulate energy fluxes, the intra-urban variability of air temperature, urban-heat-island characteristics, indoor temperature variation, as well as anthropogenic heat emissions, in Berlin, Germany. A building energy model is implemented into the Double Canyon Effect Parametrization, which is coupled with the mesoscale climate model COSMO-CLM (COnsortium for Small-scale MOdelling in CLimate Mode) and takes into account heat generation within buildings and calculates the heat transfer between buildings and the urban atmosphere. The enhanced coupled urban model is applied in two simulations of 24-day duration for a winter and a summer period in 2018 in Berlin, using downscaled reanalysis data to a final grid spacing of 1 km. Model results are evaluated with observations of radiative and turbulent energy fluxes, 2-m air temperature, and indoor air temperature. The evaluation indicates that the improved model reproduces the diurnal characteristics of the observed turbulent heat fluxes, and considerably improves the simulated 2-m air temperature and urban heat island in winter, compared with the simulation without the building energy model. Our set-up also estimates the spatio–temporal variation of wintertime energy consumption due to heating with canyon geometry. The potential to save energy due to the urban heat island only becomes evident when comparing a suburban site with an urban site after applying the same grid-cell values for building and street widths. In summer, the model realistically reproduces the indoor air temperature and its temporal variation.

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Impact of a Detailed Urban Parameterization on Modeling the Urban Heat Island in Beijing Using TEB-RAMS

Advances in Meteorology ◽

10.1155/2014/602528 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11

Author(s):

Lei Jiang ◽

Lixin Lu ◽

Lingmei Jiang ◽

Yuanyuan Qi ◽

Aqiang Yang

Keyword(s):

Land Use ◽

Urban Heat Island ◽

Air Temperature ◽

Metropolitan Area ◽

Atmospheric Modeling ◽

Anthropogenic Heat ◽

Heat Island ◽

Urban Heat ◽

Four Levels ◽

Geometric Morphology

The Town Energy Budget (TEB) model coupled with the Regional Atmospheric Modeling System (RAMS) is applied to simulate the Urban Heat Island (UHI) phenomenon in the metropolitan area of Beijing. This new model with complex and detailed surface conditions, called TEB-RAMS, is from Colorado State University (CSU) and the ASTER division of Mission Research Corporation. The spatial-temporal distributions of daily mean 2 m air temperature are simulated by TEB-RAMS during the period from 0000 UTC 01 to 0000 UTC 02 July 2003 over the area of 116°E~116.8°E, 39.6°N~40.2°N in Beijing. The TEB-RAMS was run with four levels of two-way nested grids, and the finest grid is at 1 km grid increment. An Anthropogenic Heat (AH) source is introduced into TEB-RAMS. A comparison between the Land Ecosystem-Atmosphere Feedback model (LEAF) and the detailed TEB parameterization scheme is presented. The daily variations and spatial distribution of the 2 m air temperature agree well with the observations of the Beijing area. The daily mean 2 m air temperature simulated by TEB-RAMS with the AH source is 0.6 K higher than that without specifying TEB and AH over the metropolitan area of Beijing. The presence of urban underlying surfaces plays an important role in the UHI formation. The geometric morphology of an urban area characterized by road, roof, and wall also seems to have notable effects on the UHI intensity. Furthermore, the land-use dataset from USGS is replaced in the model by a new land-use map for the year 2010 which is produced by the Institute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences (CAS). The simulated regional mean 2 m air temperature is 0.68 K higher from 01 to 02 July 2003 with the new land cover map.

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

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-15-0051.1 ◽

2015 ◽

Vol 54 (11) ◽

pp. 2245-2259 ◽

Cited By ~ 5

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.

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