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

Yi, Chaeyeon; Kim, Kyu Rang; An, Seung Man; Choi, Young-Jean; Holtmann, Achim; Jänicke, Britta; Fehrenbach, Ute; Scherer, Dieter

doi:10.1002/joc.4363

Cited by 16 publications

(7 citation statements)

References 28 publications

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“…For a given dataset, a non-probabilistic binary-linear classification model is generated to determine how new data are classified. While the generated model is expressed as boundaries of a space where the data exist, the SVM algorithm identifies the broadest boundary among them [42,56,57].…”

Section: Statistical Model and Evaluation Methodsmentioning

confidence: 99%

Heat Exposure Information at Screen Level for an Impact-Based Forecasting and Warning Service for Heat-Wave Disasters

Yang

2020

Atmosphere

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The importance of impact-based forecasting services, which can support decision-making, is being emphasized to reduce the damage of meteorological disasters, centered around the World Meteorological Organization. The Korea Meteorological Administration (KMA) began developing impact-based forecasting technology and warning services in 2018. This paper proposes statistical downscaling and bias correction methods for acquiring high-resolution meteorological data for the heat-wave impact forecast system operated by KMA. Hence, digital forecast data from KMA, with 5 km spatial resolution, were downscaled and corrected to a spatial resolution of 1 km using statistical interpolation methods. Cross-validation indicated the superior performance of the Gaussian process regression model (GPRM) technique with low root mean square error and percent bias values and high CC value. The GPRM technology had the lowest forecast error, especially during the hottest period in Korea. In addition, temperatures for land-use areas with low elevations and high activity, such as the urban, road, and agricultural areas, were high. It is essential to provide accurate heat exposure information at the screen level with high human activity. Spatiotemporally accurate heat exposure information can be used more realistically for risk management in agriculture, livestock and fishery, and for adjusting the working hours of outdoor workers in construction and shipbuilding.

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Section: Statistical Model and Evaluation Methodsmentioning

confidence: 99%

Heat Exposure Information at Screen Level for an Impact-Based Forecasting and Warning Service for Heat-Wave Disasters

Yang

2020

Atmosphere

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“…Several studies have evaluated Seoul's building morphology (Kang et al, 2020; Yi et al, 2016), solar radiation profile (Jee et al, 2017), office building energy consumption (Ahn et al, 2019), and rooftop PV potential (Byrne et al, 2015; Hong et al, 2017). Overall, these assessments identify substantial citywide rooftop solar potential.…”

Section: Case Study Of Eunpyeong‐gu Seoul South Koreamentioning

confidence: 99%

Inferential‐ and measurement‐based methods to estimate rooftop “solar city” potential in megacity Seoul, South Korea

Taminiau¹,

Byrne

Kim

et al. 2022

WIREs Energy & Environment

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Data analysis and collection techniques now allow for detailed inventory‐building of urban rooftops for the purposes of identifying solar energy potential within geographically defined boundaries, including those of cities. The complexity and inherent diversity of a city's building stock has propelled the introduction of many so‐called “solar city” assessment methods that, with varying levels of accuracy, scalability, and ease of use, seek to characterize the citywide solar photovoltaic (PV) resource potential. A review of the landscape of available methods supports a fundamental distinction across two classes of methods. First, “solar city” assessment methods can principally rely on inference to identify and characterize rooftop solar potential. Such inferential methods can establish estimates of citywide solar potential without needing direct insight into rooftop conditions or morphology, Second, measurement‐based methods estimate rooftop solar opportunities based on the direct measurement of rooftop conditions, often conducted through remote sensing. Comparative performance testing of several inferential‐ and measurement‐based methods using case study analysis underscores the importance of measurement‐based methods. In particular, measurement‐based methods are likely better positioned to support the needs of policy‐makers and investors interested in transforming a city or metropolitan area into a sustainable city whose buildings serve as the host of a new solar PV‐powered distributed electricity service system. This article is categorized under: Sustainable Energy > Solar Energy Energy and Power Systems > Energy Infrastructure Cities and Transportation > Smart Cities

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“…We calculated 25 m resolution urban surface parameters using the Climate Analysis Seoul (CAS) workbench. The CAS workbench is a model suitable for computing topographic and land cover parameters and analyzing microclimate (heat and wind) spatial distribution profiles [28][29][30]. First, we parameterized the three-dimensional (3-D) topographical structure from a digital terrain model (DTM) and digital surface model (DSM) and computed 3-D topographic parameters, such as the hollow depth, aspect ratio, dzdx, dzdy, and slope length.…”

Section: Urban Surface Parametersmentioning

confidence: 99%

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

Shin

2019

Atmosphere

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

Cited by 16 publications

References 28 publications

Heat Exposure Information at Screen Level for an Impact-Based Forecasting and Warning Service for Heat-Wave Disasters

Heat Exposure Information at Screen Level for an Impact-Based Forecasting and Warning Service for Heat-Wave Disasters

Inferential‐ and measurement‐based methods to estimate rooftop “solar city” potential in megacity Seoul, South Korea

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

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