Atmosphere (대기)

Korean Meteorological Society (한국기상학회)
권호 표지
DOI QR코드

DOI QR Code

Verification and Comparison of Forecast Skill between Global Seasonal Forecasting System Version 5 and Unified Model during 2014

2014년 계절예측시스템과 중기예측모델의 예측성능 비교 및 검증

  • Lee, Sang-Min (National Institute of Meteorological Sciences, Korea Meteorological Administration) ;
  • Kang, Hyun-Suk (National Institute of Meteorological Sciences, Korea Meteorological Administration) ;
  • Kim, Yeon-Hee (National Institute of Meteorological Sciences, Korea Meteorological Administration) ;
  • Byun, Young-Hwa (National Institute of Meteorological Sciences, Korea Meteorological Administration) ;
  • Cho, ChunHo (National Institute of Meteorological Sciences, Korea Meteorological Administration)
  • Received : 2015.12.03
  • Accepted : 2016.02.01
  • Published : 2016.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

The comparison of prediction errors in geopotential height, temperature, and precipitation forecasts is made quantitatively to evaluate medium-range forecast skills between Global Seasonal Forecasting System version 5 (GloSea5) and Unified Model (UM) in operation by Korea Meteorological Administration during 2014. In addition, the performances in prediction of sea surface temperature anomaly in NINO3.4 region, Madden and Julian Oscillation (MJO) index, and tropical storms in western north Pacific are evaluated. The result of evaluations appears that the forecast skill of UM with lower values of root-mean square error is generally superior to GloSea5 during forecast periods (0 to 12 days). The forecast error tends to increase rapidly in GloSea5 during the first half of the forecast period, and then it shows down so that the skill difference between UM and GloSea5 becomes negligible as the forecast time increases. Precipitation forecast of GloSea5 is not as bad as expected and the skill is comparable to that of UM during 10-day forecasts. Especially, in predictions of sea surface temperature in NINO3.4 region, MJO index, and tropical storms in western Pacific, GloSea5 shows similar or better performance than UM. Throughout comparison of forecast skills for main meteorological elements and weather extremes during medium-range, the effects of initial and model errors in atmosphere-ocean coupled model are verified and it is suggested that GloSea5 is useful system for not only seasonal forecasts but also short- and medium-range forecasts.

Keywords

References

  1. Arribas, A., and Coauthors, 2011: The GloSea4 ensemble prediction system for seasonal forecasting. Mon. Wea. Rev., 139, 1891-1910, doi:http://dx.doi.org/10.1175/2010MWR3615.1.
  2. Alexander, M. A., I. Blade, M. Newman, J. R. Lanzante, N. Lau, and J. D. Scott, 2002: The atmospheric bridge: The influence of ENSO teleconnections on air-sea interaction over the global oceans. J. Climate, 15, 2205-2231. https://doi.org/10.1175/1520-0442(2002)015<2205:TABTIO>2.0.CO;2
  3. Bengtsson, L., M. Botzest, and M. Esch, 1995: Hurricanetype vortices in a general circulation model. Tellus, 47A, 175-196.
  4. Camp, J., M. Roberts, C. MacLachlan, E. Wallace, L. Hermanson, A. Brookshaw, A. Arribas, and A. A. Scaife, 2015: Seasonal forecasting of tropical storms using the Met Office GloSea5 seasonal forecast system. Q. J. R. Meteorol. Soc., 141, 2206-2219, doi:10.1002/qj.2516.
  5. Guilyardi, E., A. Wittenberg, A. Fedorov, M. Collins, C. Wang, A. Capotondi, G. J. van Oldenborgh, and T. Stockdale, 2009: Understanding El Nino in ocean atmosphere general circulation models: Progress and challenges. Bull. Am. Meteorol. Soc., 90, 325-340, doi:10.1175/2008BAMS2387.1.
  6. Luo, J. J., S. Masson, E. Roeckner, G. Madec, and T. Yamagata, 2005: Reducing climatology bias in an ocean-atmosphere CGCM with improved coupling. Physics. J. Climate, 18, 2344-2360, doi:10.1175/JCLI3404.1.
  7. MacLachlan, C., and Coauthors, 2015: Global Seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system. Q. J. R. Meteorol. Soc., 141, 1072-1084, doi:10.1002/qj.2396.
  8. Mogensen, K. S., M. A. Balmaseda, A. Weaver, M. J. Martin, and A. Vidard, 2009: NEMOVAR: A variational data assimilation system for the NEMO ocean model. ECMWF newsletter 120, summer 2009.
  9. Murakami, H., and B. Wang, 2010: Future change of North Atlantic tropical cyclone tracks: Projection by a 20-km-mesh global atmospheric model. J. Climate, 23, 1699-2721.
  10. Smith, D. M., A. A. Scaife, and B. Kirtman, 2012: What is the current state of scientific knowledge with regard to seasonal and decadal forecasting? Environ. Res. Lett., 7, 015602, doi:10.1088/1748-9326/7/1/015602.
  11. Suzuki-Parker, A., 2012: An assessment of uncertainties and limitations in simulating tropical cyclones. Springer Thesis. XIII, 78 pp.
  12. 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
  13. Trenberth, K. E., and J. M. Caron, 2000: The southern oscillation revisited: Sea level pressures, surface temperatures and precipitation. J. Climate, 13, 4358-4365. https://doi.org/10.1175/1520-0442(2000)013<4358:TSORSL>2.0.CO;2
  14. Wilks, D. S., 2006: Statistical methods in the atmospheric sciences. 2nd ed. Elsevier Inc., 627 pp.
  15. WMO, 2013: Sub-seasonal to seasonal prediction research implementation plan. [Available online at https://www.wmo.int/pages/prog/arep/wwrp/new/documents/S2S_Implem_plan_en.pdf].
  16. Xavier, P., R. Rahmat, W. K. Cheong, and E. Wallace, 2014: Influence of madden-julian oscillation on southeast Asia rainfall extremes: Observations and predictability. Geophys. Res. Lett., 41, 4406-4412, doi:10.1002/2014GL060241.