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http://dx.doi.org/10.14191/Atmos.2019.29.3.257

An Assessment of Applicability of Heat Waves Using Extreme Forecast Index in KMA Climate Prediction System (GloSea5)  

Heo, Sol-Ip (Earth System Research Division, National Institute of Meteorological Sciences)
Hyun, Yu-Kyung (Earth System Research Division, National Institute of Meteorological Sciences)
Ryu, Young (Earth System Research Division, National Institute of Meteorological Sciences)
Kang, Hyun-Suk (Numerical Model Development Division, Numerical Modeling Center, Korea Meteorological Administration)
Lim, Yoon-Jin (Climate Research Division, National Institute of Meteorological Sciences)
Kim, Yoonjae (Earth System Research Division, National Institute of Meteorological Sciences)
Publication Information
Atmosphere / v.29, no.3, 2019 , pp. 257-267 More about this Journal
Abstract
This study is to assess the applicability of the Extreme Forecast Index (EFI) algorithm of the ECMWF seasonal forecast system to the Global Seasonal Forecasting System version 5 (GloSea5), operational seasonal forecast system of the Korea Meteorological Administration (KMA). The EFI is based on the difference between Cumulative Distribution Function (CDF) curves of the model's climate data and the current ensemble forecast distribution, which is essential to diagnose the predictability in the extreme cases. To investigate its applicability, the experiment was conducted during the heat-wave cases (the year of 1994 and 2003) and compared GloSea5 hindcast data based EFI with anomaly data of ERA-Interim. The data also used to determine quantitative estimates of Probability Of Detection (POD), False Alarm Ratio (FAR), and spatial pattern correlation. The results showed that the area of ERA-Interim indicating above 4-degree temperature corresponded to the area of EFI 0.8 and above. POD showed high ratio (0.7 and 0.9, respectively), when ERA-Interim anomaly data were the highest (on Jul. 11, 1994 (> $5^{\circ}C$) and Aug. 8, 2003 (> $7^{\circ}C$), respectively). The spatial pattern showed a high correlation in the range of 0.5~0.9. However, the correlation decreased as the lead time increased. Furthermore, the case of Korea heat wave in 2018 was conducted using GloSea5 forecast data to validate EFI showed successful prediction for two to three weeks lead time. As a result, the EFI forecasts can be used to predict the probability that an extreme weather event of interest might occur. Overall, we expected these results to be available for extreme weather forecasting.
Keywords
GloSea5; extreme weather; ensemble; EFI;
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