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http://dx.doi.org/10.5467/JKESS.2021.42.2.135

Evaluation of the Urban Heat Island Intensity in Seoul Predicted from KMA Local Analysis and Prediction System  

Byon, Jae-Young (National Institute of Meteorological Sciences, Korea Meteorological Administration)
Hong, Seon-Ok (National Institute of Meteorological Sciences, Korea Meteorological Administration)
Park, Young-San (National Institute of Meteorological Sciences, Korea Meteorological Administration)
Kim, Yeon-Hee (National Institute of Meteorological Sciences, Korea Meteorological Administration)
Publication Information
Journal of the Korean earth science society / v.42, no.2, 2021 , pp. 135-148 More about this Journal
Abstract
The purpose of this study was to evaluate the urban heat island (UHI) intensity and the corresponding surface temperature forecast obtained using the local data assimilation and prediction system (LDAPS) of the Korea Meteorological Administration (KMA) against the AWS observation. The observed UHI intensity in Seoul increases during spring and winter, while it decreases during summer. It is found that the diurnal variability of the UHI intensity peaks at dawn but reaches a minimum in the afternoon. The LDAPS overestimates the UHI intensity in summer but underestimates it in winter. In particular, the model tends to overestimate the UHI intensity during the daytime in summer but underestimate it during the nighttime in winter. Moreover, surface temperature errors decrease in summer but increase in winter. The underestimation of the winter UHI intensity appears to be associated with weak forecasting of urban temperature in winter. However, the overestimated summer UHI intensity results from the underestimation of the suburban temperature forecast in summer. In order to improve the predictability of the UHI intensity, an urban canopy model (MORUSES) that considers urban effects was combined with LDAPS and used for simulation for the summer of 2017. The surface temperature forecast for the city was improved significantly by adopting MORUSES, and there were remarkable improvements in urban surface temperature morning forecasts. The urban canopy model produced an improvement effect that weakened the intensity of the UHI, which showed an overestimation during summer.
Keywords
Urban heat island; local data assimilation and prediction system; LDAPS; urban canopy model; MORUSES;
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