Impact of the Local Surface Characteristics and the Distance from the Center of Heat Island to Suburban Areas on the Night Temperature Distribution over the Seoul Metropolitan Area

Yi, Chaeyeon; Kim, Kyu-Rang; An, Seung-Man; Choi, Young-Jean

doi:10.11108/kagis.2014.17.1.035

Cited by 7 publications

(5 citation statements)

References 22 publications

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“…Meso‐scale analysis revealed a distinctive urban heat island in the Seoul metropolitan area (Bhang and Park, ). Local land cover within a 200 m radius from each observational site, as well as the distance from the centre of the city, affected temperature difference between the AWSs (Kim et al , ; Yi et al , ). Therefore, we need to investigate the details of the local urban features to determine their thermal and mechanical characteristics.…”

Section: Introductionmentioning

confidence: 99%

Estimating spatial patterns of air temperature at building‐resolving spatial resolution in Seoul, Korea

Yi¹,

Kim

et al. 2015

Intl Journal of Climatology

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ABSTRACT:The thermal environment in urban areas is substantially influenced by local surface properties and their modification through human activities. The effects of urban climate on human health are one of the motivations for the development of various urban climate analysis tools. Climate Analysis Seoul (CAS) is a tool that incorporates a meso-scale atmospheric model and conceptual geographic information system (GIS)-based models to provide gridded air temperature deviations based on high-resolution land cover information. The CAS output was evaluated using air temperatures observed at 18 automatic weather stations (AWS) located in the detail region (DR) Eunpyeong. Correlation analyses were performed to reveal the influence of different land cover characteristics and CAS output variables on measured air temperature deviations. Based on the results of the correlation analyses in the study region (SR) Seoul, a regression model (total air temperature distribution, TD ′ ) was developed. It predicts spatially distributed air temperatures based on morphological parameters and an observed reference temperature. Using the TD ′ model, maps of daily maximum air temperatures were produced for the entire area of Seoul with a horizontal resolution of 25 m, and of 5 m for the DR, the latter allows to assess the impacts of different building and vegetation structures on air temperature by resolving buildings. The TD ′ model was evaluated using measurements at eight AWS. The TD ′ model slightly overestimated daily maximum air temperatures in 2007 and 2011, but performed better for 2011, mainly because land cover data represent the final state of the urban development. The TD ′ model appears to be a suitable tool for estimating air temperature distributions at building-resolving resolutions. It can be used to assess changes in the thermal environment and heat-related hazards through urban development plans already ahead of their realization.KEY WORDS urban climate analysis; urban planning; urban heat island; urban multi-scale temperature distribution

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Section: Introductionmentioning

confidence: 99%

Estimating spatial patterns of air temperature at building‐resolving spatial resolution in Seoul, Korea

Yi¹,

Kim

et al. 2015

Intl Journal of Climatology

Self Cite

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“…The temperature data from the AWSs in the city center clearly reflected the influence of heat generated in relation to urban surface characteristics and physical structures in addition to that from mesoscale phenomena. On a related note, Yi et al [31] confirmed a high correlation between the quantitative distribution of human-made structures, such as buildings and streets, and thermal features identified as a result of microclimatic analysis.…”

Section: Aws Datamentioning

confidence: 93%

“…The temperature rise effect due to sensible heat release from the thermal energy stored during the day in the city's core and the temperature drop effect due to the cold air generated during the night in vegetated areas may be explained by the results of a previous study that describe the large contribution of the quantitative distribution of observed air temperatures at the ground surface [31]. Therefore, as these two effects based on quantitative distributions of temperatures at the ground surface are reflected in the UMIS prediction data, the UMIS prediction accuracy for the daily maximum and minimum temperatures can be said to have improved.…”

Section: Prediction Accuracy Of Daily Maximum and Minimum Temperaturementioning

confidence: 97%

“…Notable spatial differences were observed in the observed daytime temperatures from one AWS to another. Yi et al [31] noted that such spatial differences are ascribable to the different shapes and sizes of urban surface components. In Figure 8, UMIS shows greater spatial differences than LDAPS in the predicted temperature when checked against the corresponding AWS observations.…”

Section: Accuracy Evaluation Of Maximum Temperature During a Heatwavementioning

confidence: 99%

“…AWS-dependent spatial deviations were more conspicuous at dawn than during the daytime. Yi et al [31] noted that such spatial differences are attributable to different shapes and sizes of urban surface components. Earlier studies have attributed such temperature deviations to temperature decline through cold air generation due to higher albedo in vegetated surfaces and the increase in sensible heat release into the atmosphere from building surfaces during the night, depending on the surface area ratio [44,45].…”

Section: Accuracy Evaluation Of Minimum Temperature During a Tropicalmentioning

confidence: 99%

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Statistical Downscaling of Urban-scale Air Temperatures Using an Analog Model Output Statistics Technique

Shin

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This study was conducted to evaluate the suitability of an analog model output statistics (MOS) downscaling technique for urban-scale meteorology research and compares this MOS-Analog technique with the sliding window technique. We downscaled air temperatures forecasted for the Seoul metropolitan area from 1.5 km resolution (using data from the Unified Model-Local Data Assimilation and Prediction System, UM-LDAPS) to 25 m resolution using the analog MOS technique described in the paper. The support vector machine (SVM) technique was employed for empirical computational modeling, using urban surface parameters calculated using the Climate Analysis Seoul (CAS) workbench and automated weather station (AWS) observational data as training data. The comparison of the downscaled prediction results with the AWS observations for the periods of July/August 2016 and 2017 resulted in a lower root mean square error (RMSE) and higher correlation coefficients (CC) than those obtained for the LDAPS prediction results. The prediction performance was also stable for September, during which precipitation episodes and seasonal fluctuations occurred. The results of this study demonstrate that the proposed technique, which overcomes the limitations of the sliding window technique, is applicable to urban-scale meteorology research and potentially applicable other areas.

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Impact of the Local Surface Characteristics and the Distance from the Center of Heat Island to Suburban Areas on the Night Temperature Distribution over the Seoul Metropolitan Area

Cited by 7 publications

References 22 publications

Estimating spatial patterns of air temperature at building‐resolving spatial resolution in Seoul, Korea

Estimating spatial patterns of air temperature at building‐resolving spatial resolution in Seoul, Korea

Statistical Downscaling of Urban-scale Air Temperatures Using an Analog Model Output Statistics Technique

Assessing the impact of green infrastructure on thermal comfort in relation to humidity: A case study in Korea

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