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

기상청 현업 기후예측시스템(GloSea5)에서의 극한예측지수를 이용한 여름철 폭염 예측 성능 평가

  • 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)
  • 허솔잎 (국립기상과학원 지구시스템연구과) ;
  • 현유경 (국립기상과학원 지구시스템연구과) ;
  • 류영 (국립기상과학원 지구시스템연구과) ;
  • 강현석 (기상청 수치모델링센터 수치모델개발과) ;
  • 임윤진 (국립기상과학원 기후연구과) ;
  • 김윤재 (국립기상과학원 지구시스템연구과)
  • Received : 2019.03.13
  • Accepted : 2019.07.29
  • Published : 2019.09.30

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

Acknowledgement

Grant : 장기예측시스템 개발

Supported by : 국립기상과학원

References

  1. Bauer, P., A. Thorpe, and G. Brunet, 2015: The quiet revolution of numerical weather prediction. Nature, 525, 47-55, doi:10.1038/nature14956. https://doi.org/10.1038/nature14956
  2. Best, M. J., and Coauthors, 2011: The Joint UK Land Environment Simulator (JULES), model description - Part 1: Energy and water fluxes. Geosci. Model Dev., 4, 677-699, doi:10.5194/gmd-4-677-2011. https://doi.org/10.5194/gmd-4-677-2011
  3. Bowler, N. E., A. Arribas, S. E. Beare, K. R. Mylne, and G. J. Shutts, 2009: The local ETKF and SKEB: Upgrades to the MOGREPS short-range ensemble prediction system. Q. J. Roy. Meteor. Soc., 135, 767-776. https://doi.org/10.1002/qj.394
  4. Brown, A., S. Milton, M. Cullen, B. Golding, J. Mitchell, and A. Shelly, 2012: Unified modeling and prediction of weather and climate: A 25-year journey. Bull. Amer. Meteor. Soc., 93, 1865-1877, doi:10.1175/BAMS-D-12-00018.1. https://doi.org/10.1175/BAMS-D-12-00018.1
  5. Dee, D. P., and Coauthors, 2011: The ERA-interim reanalysis: Configuration and performance of the data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553-597, doi:10.1002/qj.828. https://doi.org/10.1002/qj.828
  6. Dutra, E., M. Diamantakis, I. Tsonevsky, E. Zsoter, F. Wetterhall, T. Stockdale, D. Richardson, and F. Pappenberger, 2013: The extreme forecast index at the seasonal scale. Atmos. Sci. Lett., 14, 256-262, doi:10.1002/asl2.448. https://doi.org/10.1002/asl2.448
  7. Elsner, J. B., and C. P. Schmertmann, 1994: Assessing forecast skill through cross validation. Wea. Forecasting, 9, 619-624. https://doi.org/10.1175/1520-0434(1994)009<0619:AFSTCV>2.0.CO;2
  8. Guan, H., and Y. Zhu, 2017: Development of verification methodology for extreme weather forecasts. Wea. Forecasting, 32, 479-491, doi:10.1175/WAF-D-16-0123.1 https://doi.org/10.1175/WAF-D-16-0123.1
  9. Ham H., D. Won, and Y.-S. Lee, 2017: Performance assessment of weekly ensemble prediction data at seasonal forecast system with high resolution, Atmosphere, 27, 261-276, doi:10.14191/Atmos.2017.27.3.261 (in Korean with English abstract). https://doi.org/10.14191/Atmos.2017.27.3.261
  10. Hamill, T. M., G. T. Bates, J. S. Whitaker, D. R. Murray, M. Fiorino, T. J. Galarneau Jr., Y. Zhu, and W. Lapenta, 2013: NOAA's second-generation global medium-range ensemble reforecast dataset. Bull. Amer. Meteor. Soc., 94, 1553-1565, doi:10.1175/BAMS-D-12 00014.1. https://doi.org/10.1175/BAMS-D-1200014.1
  11. Hamill, T. M., and Coauthors, 2014: A recommended reforecast configuration for the NCEP Global Ensemble Forecast System. NOAA White Paper, 24 pp.
  12. Hunke, E. C., and W. H. Lipscomb, 2010: CICE: The Los Alamos sea ice model documentation and software user's manual, Version 4.1, LA-CC-06-012 Technical report, Los Alamos National Laboratory, N. M, 76 pp.
  13. IPCC, 2014: Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. In The Core Writing Team et al. Eds., IPCC, 151 pp.
  14. ICPO, 2011: Data and bias correction for decadal climate predictions, International CLIVAR Project Office, ICPO Publication Series no. 150, 3 pp.
  15. Lalaurette, F., 2003: Early detection of abnormal weather conditions using a probabilistic extreme forecast index. Q. J. Roy. Meteor. Soc., 129, 3037-3057, doi:10.1256/qj.02.152. https://doi.org/10.1256/qj.02.152
  16. Lavers, D. A., F. Pappenberger, D. S. Richardson, and E. Zsoter, 2016: ECMWF Extreme Forecast Index for water vapor transport: A forecast tool for atmospheric rivers and extreme precipitation. Geophys. Res. Lett., 43, 11852-11858, doi:10.1002/2016GL071320. https://doi.org/10.1002/2016GL071320
  17. Lavers, D. A., E. Zsoter, D. S. Richardson and F. Pappenberger, 2017: An assessment of the ECMWF Extreme Forecast Index for water vapor transport during boreal winter. Wea. Forecasting, 32, 1667-1674, doi:10.1175/WAF-D-17-0073.1. https://doi.org/10.1175/WAF-D-17-0073.1
  18. Lea, D. J., I. Mirouze, M. J. Martin, R. R. King, A. Hines, D. Walters, and M. Thurlow, 2015: Assessing a new coupled data assimilation system based on the Met Office coupled atmosphere-land-ocean-sea ice model. Mon. Wea. Rev., 143, 4678-4694, doi:10.1175/MWRD-15-0174.1. https://doi.org/10.1175/MWR-D-15-0174.1
  19. Lee, S.-W., J. Son, S.-O. Moon, and H. Park, 2013: Probabilistic early warnings of severe weather by using the EFI from KMA Ensemble Prediction System. Proc. Abstract, KMS Spring Meeting, Korean Meteorological Society, 16-17.
  20. Jeon, E.-H., S.-O. Moon, and Y.-Y. Park, 2006: EFI analysis of KMA Ensemble Prediction System. Proc. Abstract, KMS Fall Meeting, Korean Meteorological Society, 256-257 (in Korean).
  21. MacLachlan, C., and Coauthors, 2015: Global seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system. Q. J. Roy. Meteor. Soc., 141, 1072-1084, doi:10.1002/qj.2396. https://doi.org/10.1002/qj.2396
  22. Madec, G., 2008: NEMO ocean engine. Note du Pole de Modelisation No. 27 Institut Pierre-Simon Laplace (IPSL), 300 pp.
  23. Mason, I., 1982: A model for assessment of weather forecasts. Aust. Meteor. Mag., 30, 291-303.
  24. Persson, A., 2015: User Guide to ECMWF forecast products, Version 1.2, ECMWF, 129 pp.
  25. Roebber, P. J., 2009: Visualizing multiple measures of forecast quality. Wea. Forecasting, 24, 601-608. https://doi.org/10.1175/2008WAF2222159.1
  26. Taylor, K. E., 2001: Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res., 106, 7183-7192. https://doi.org/10.1029/2000JD900719
  27. Valcke, S., 2013: The OASIS3 coupler: A European climate modelling community software. Geosci. Model Dev., 6, 373-388, doi:10.5194/gmd-6-373-2013. https://doi.org/10.5194/gmd-6-373-2013
  28. Walters, D. N., and Coauthors, 2011: The Met Office Unified Model global atmosphere 3.0/3.1 and JULES global land 3.0/3.1 configurations. Geosci. Model Dev., 4, 919-941, doi:10.5194/gmd-4-919-2011. https://doi.org/10.5194/gmd-4-919-2011
  29. Wilks, D. S., 1995: Statistical Methods in the Atmospheric Sciences: Forecast verification. Academic Press, 704 pp.
  30. Zhu, Y., and B. Cui, 2007: NAEFS mean, spread and probability forecasts. NOAA/NCEP Rep., 4 pp [Available online at http://www.emc.ncep.noaa.gov/gmb/yzhu/imp/i200711/3-Mean_spread.pdf].
  31. Zsoter, E., 2006: Recent developments in extreme weather forecasting. ECMWF Newsletter, 107, 8-17, doi:10.21957/kl9821hnc7. https://doi.org/10.21957/kl9821hnc7