Urban heat island patterns and their dynamics based on an urban climate measurement network

Gál, Tamás; Skarbit, Nóra; Unger, János

doi:10.15201/hungeobull.65.2.2

Cited by 25 publications

(20 citation statements)

References 2 publications

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“…In Szeged, urban climate investigations have been typically based on in-situ (e.g., Unger et al, 2015) and mobile (e.g., Unger et al, 2001) measurements, but WRF was not used up to now. The study of Gál et al (2016) must be pointed out, who evaluated a one-year long observation of the local urban climate monitoring system established in the spring of 2014. On average, UHI released around sunset and decreased to 0 °C after a few hours after sunrise.…”

Section: Introductionmentioning

confidence: 99%

Modeling of urban heat island using adjusted static database

Molnár¹,

Gyöngyösi²,

Gál³

2019

Időjárás

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In this study, the Weather Research and Forecasting (WRF) model was applied to examine the spatial and temporal formation of urban heat island (UHI) phenomenon in Szeged, Hungary. In order to achieve a more accurate representation of complex urban surface properties in WRF, a modified static database (consists of land use and urban canopy parameters) had been developed using satellite images and building information. In the new database, the number of urban grids increased by 76% related to the default case. The urban landscape in WRF has become more complex after employing two urban land use classes instead of only one. The modification of the default parameters of a single layer urban scheme (i.e., Single Layer Urban Canopy Model -SLUCM) revealed that urban fractions decreased in all urban categories, while street widths increased resulting in narrower urban canyons. For testing the impact of the modifications on near-surface temperature estimation, a four-day heatwave period was selected from 2015. The model outputs had been evaluated against the observations of the local urban climate monitoring system (UCMS). WRF with the modified parameters simulated most of the features of UHI reasonably well. In most cases, biases with the simulations of the adjusted static database tended to be significantly lower than with the default parameters. Additionally, we picked out a longer time period (i.e., the summer of 2015) when the extreme values of near-surface air temperature and maxima of UHI intensities were evaluated on the basis of an urban and a rural site of UCMS. It was concluded that the maxima and minima of observed nearsurface air temperature were underestimated (overestimated) by about 1-3 °C at the urban (rural) site. The maxima of UHI intensities indicated cold biases on 86 of 91 days.

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Section: Introductionmentioning

confidence: 99%

Modeling of urban heat island using adjusted static database

Molnár¹,

Gyöngyösi²,

Gál³

2019

Időjárás

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“…Several studies dealing with relation of air temperature and LCZs using various research methods of LCZs were published soon after the initial definition of LCZs. For instance, Fenner et al (2014), Müller et al (2014), Lehnert et al (2015) or Gál et al (2016) analysed thermal differences of LCZs based on dense monitoring networks. An advantage of this approach lies in data availability for a longer period, while its disadvantage is a limited spatial representativeness.…”

Section: Introductionmentioning

confidence: 99%

Identifying hot and cool spots in the city centre based on bicycle measurements: The case of Olomouc, Czech Republic

Lehnert¹,

Kubeček²,

Geletič³

et al. 2018

Geographica Pannonica

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In this study we focus on a detailed analysis of air temperature in the city centre of Olomouc, using a bicycle for mobile measurements. We studied the spatial pattern of air temperature, analysed temperature differences between local climate zones (LCZs) and identified hot spots and cool spots in the city centre. The results point to a significant influence of microclimate and local climate on the field of temperature. In the daytime, hotspots occurred namely in LCZ 8 and E and in the well irradiated spaces within LCZ 2. Larger areas of scattered trees (LCZ B) in combination with watercourses created cool spots with a cooling potential for their surroundings. During night time, the warmest spots were detected mostly in LCZ 2 and the coolest spots in areas with low plants (LCZ D).

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“…The spatial pattern and intensity of urban heat island (UHI) is strongly connected to the density of impervious surfaces (Gál, T. et al 2016;Herbel, I. et al 2016;Henits, L. et al 2017). The primary aim of this study is to create an accurate land cover map based on the combination of multi-and hyperspectral images, field observations, and laboratory measurements.…”

Section: Introductionmentioning

confidence: 99%

The evaluation and application of an urban land cover map with image data fusion and laboratory measurements

et al. 2017

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High spatial and spectral resolution aerial images make it possible to develop detailed and large-scale (about 1:5,000) urban land cover maps. The main objectives of this study are (1) to evaluate the correlation between laboratory and hyperspectral image spectra to select proper bands and training samples for classification; (2) to develop a classification process to combine the spectral and spatial information of multispectral and hyperspectral images and make an urban land cover map for the study area in Szeged, Hungary; and (3) to examine the effect of different roof types on the modification of surface temperature. Reference materials were collected from the training area and their spectral characteristics were measured by a laboratory spectrometer. The hyperspectral image and laboratory spectral data between 500-800 nm showed a very strong correlation, the correlation coefficient was 0.99. The urban land cover map was produced by the combination of segmentation procedure and Spectral Angle Mapper (SAM) method using the spatial information derived from multispectral image and the spectral information of the hyperspectral image. Eight land cover classes were identified as impervious surfaces (asphalt, 4 types of tiled roof), water, and green vegetation. The overall accuracy of urban land cover map was 87.9 per cent. According to the results, an accurate large-scale urban land cover map can be generated from the fusion of multispectral and hyperspectral images. We presented that certain roof types have significant effect on surface temperature, which is strongly connected to the urban heat island phenomenon, and influences population health.

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Urban heat island patterns and their dynamics based on an urban climate measurement network

Cited by 25 publications

References 2 publications

Modeling of urban heat island using adjusted static database

Modeling of urban heat island using adjusted static database

Identifying hot and cool spots in the city centre based on bicycle measurements: The case of Olomouc, Czech Republic

The evaluation and application of an urban land cover map with image data fusion and laboratory measurements

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