ASOS와 ERA5 자료를 이용한 기계학습 기반의 폭염지수 예측가능성 평가 - 1979~2020년 우리나라의 사례연구 -
Assessment of the Predictability of Heatwave Index Using ASOS and ERA5 Data with Machine Learning: Case Study of South Korea, 1979-2020
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Heatwaves can affect human health and vegetation growth and bring about energy problems and socioeconomic damages, so the analysis and prediction of the heatwave is a crucial issue under a warming climate. This paper examines the production of STCI (Standard Temperature Condition Index) using ASOS (Automated Synoptic Observing System) in-situ observation data for the period of 1979-2020, and an STCI predictability assessment with an RF (Random Forest) model using ERA5 (ECMWF Reanalysis 5) meteorological variables. The accuracy was quite high with the MAE (Mean Absolute Error) of 0.365 and the CC (Correlation Coefficient) of 0.873, which corresponded to 7% to 10% difference for the range of STCI<1.5, and to 1% to 3% difference for the range of STCI>1.5, in terms of the probability density function. Also, we produced gridded maps for the summer STCI from 1979 to 2020 by utilizing the ERA5 raster data for the RF prediction model, which enables the spatial expansion of the ASOS point-based STCI to a continuous grid nationwide. The proposed method can be applied to forecasting of STCI by adopting future meteorological or climatic datasets.
Abstract
Heatwaves can affect human health and vegetation growth and bring about energy problems and socioeconomic damages, so the analysis and prediction of the heatwave is a crucial issue under a warming climate. This paper examines the production of STCI (Standard Temperature Condition Index) using ASOS (Automated Synoptic Observing System) in-situ observation data for the period of 1979-2020, and an STCI predictability assessment with an RF (Random Forest) model using ERA5 (ECMWF Reanalysis 5) meteorological variables. The accuracy was quite high with the MAE (Mean Absolute Error) of 0.365 and the CC (Correlation Coefficient) of 0.873, which corresponded to 7% to 10% difference for the range of STCI<1.5, and to 1% to 3% difference for the range of STCI>1.5, in terms of the probability density function. Also, we produced gridded maps for the summer STCI from 1979 to 2020 by utilizing the ERA5 raster data for the RF prediction model, which enables the spatial expansion of the ASOS point-based STCI to a continuous grid nationwide. The proposed method can be applied to forecasting of STCI by adopting future meteorological or climatic datasets.
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