기상청 기후예측시스템 개선에 따른 월별 앙상블 예측자료 성능평가
Performance Assessment of Monthly Ensemble Prediction Data Based on Improvement of Climate Prediction System at KMA
2
조회
0
다운로드
5
인용
0
참고문헌
키워드
초록
The purpose of this study is to introduce the improvement of current operational climate prediction system of KMA and to compare previous and improved that. Whereas the previous system is based on GloSea5GA3, the improved one is built on GloSea5GC2. GloSea5GC2 is a fully coupled global climate model with an atmosphere, ocean, sea-ice and land components through the coupler OASIS. This is comprised of component configurations Global Atmosphere 6.0 (GA6.0), Global Land 6.0 (GL6.0), Global Ocean 5.0 (GO5.0) and Global Sea Ice 6.0 (GSI6.0). The compositions have improved sea-ice parameters over the previous model. The model resolution is N216L85 (~60 km in mid-latitudes) in the atmosphere and ORCA0.25L75 (0.25o on a tri-polar grid) in the ocean. In this research, the predictability of each system is evaluated using by RMSE, Correlation and MSSS, and the variables are 500 hPa geopotential height (h500), 850 hPa temperature (t850) and Sea surface temperature (SST). A predictive performance shows that GloSea5GC2 is better than GloSea5GA3. For example, the RMSE of h500 of 1-month forecast is decreased from 23.89 gpm to 22.21 gpm in East Asia. For Nino3.4 area of SST, the improvements to GloSeaGC2 result in a decrease in RMSE, which become apparent over time. It can be concluded that GloSea5GC2 has a great performance for seasonal prediction.
Abstract
The purpose of this study is to introduce the improvement of current operational climate prediction system of KMA and to compare previous and improved that. Whereas the previous system is based on GloSea5GA3, the improved one is built on GloSea5GC2. GloSea5GC2 is a fully coupled global climate model with an atmosphere, ocean, sea-ice and land components through the coupler OASIS. This is comprised of component configurations Global Atmosphere 6.0 (GA6.0), Global Land 6.0 (GL6.0), Global Ocean 5.0 (GO5.0) and Global Sea Ice 6.0 (GSI6.0). The compositions have improved sea-ice parameters over the previous model. The model resolution is N216L85 (~60 km in mid-latitudes) in the atmosphere and ORCA0.25L75 (0.25o on a tri-polar grid) in the ocean. In this research, the predictability of each system is evaluated using by RMSE, Correlation and MSSS, and the variables are 500 hPa geopotential height (h500), 850 hPa temperature (t850) and Sea surface temperature (SST). A predictive performance shows that GloSea5GC2 is better than GloSea5GA3. For example, the RMSE of h500 of 1-month forecast is decreased from 23.89 gpm to 22.21 gpm in East Asia. For Nino3.4 area of SST, the improvements to GloSeaGC2 result in a decrease in RMSE, which become apparent over time. It can be concluded that GloSea5GC2 has a great performance for seasonal prediction.
참고문헌 (33)
- Ahn, J. -B., "Study of Climate Drift in Atmosphere and Ocean GCMs", Korean Meteorological Society, 33, 33, pp.509~520 (1997)
- Alberto Arribas, "The GloSea4 Ensemble Prediction System for Seasonal Forecasting", Monthly Weather Review, 139(6), pp.1891~1910 (2011), American Meteorological Society[DOI]
- Bowler, N. E., "The local ETKF and SKEB:Upgrades to the MOGREPS short-range ensemble prediction system", Q. J. Roy. Meteor. Soc, 135, pp.767~776 (2009)
- Jung Choi, "Seasonal-to-Interannual Prediction Skills of Near-Surface Air Temperature in the CMIP5 Decadal Hindcast Experiments", Journal of Climate, 29(4), pp.1511~1527 (2016), American Meteorological Society[DOI]
- D. P. Dee, "The ERA-Interim reanalysis: configuration and performance of the data assimilation system", Quarterly Journal of the Royal Meteorological Society, 137(656), pp.553~597 (2011), Wiley[DOI]
- L. Goddard, "A verification framework for interannual-to-decadal predictions experiments", Climate Dynamics, 40(1-2), pp.245~272 (2013), Springer Science and Business Media LLC[DOI]
- Alexander Sen Gupta, "Climate Drift in the CMIP3 Models", Journal of Climate, 25(13), pp.4621~4640 (2012), American Meteorological Society[DOI]
- Improvement Status of Current Operation Seasonal Forecast System and Verification in 2016, "Ham, H.", NIMR Numerical model development division, 74~, pp.NIMR Numerical model development division Technical report, G901:A-0008885252
- 함현준, "고해상도 장기예측시스템의 주별 앙상블 예측자료 성능 평가", 대기, 27(3), pp.261~276 (2017), 한국기상학회[DOI]
- Horel, J. D., "Planetary-scale atmospheric phenomena associated with the Southern Oscillation", Mon. Wea. Rev, 109, pp.813~829 (1981)
- Jin, E. K., "Current status of ENSO prediction skill in coupled ocean-atmosphere models", Clim. Dynam, 31, pp.647~664 (2008)
- 정영윤, "기상청 계절예측시스템(GloSea5)의 해양성층 강화시기단기 해양예측 정확도 및 대기 - 해양 접합효과", 대기, 26(4), pp.599~615 (2016), 한국기상학회
- 정명일, "GloSea5 모형의 6개월 장기 기후 예측성 검증", 대기, 25(2), pp.323~337 (2015), 한국기상학회
- 정명일, "GloSea5 모형의 성층권 예측성 검증", 대기, 26(1), pp.203~214 (2016), 한국기상학회
- Kang, H. -S., "Introduction to KMA-Met office joint seasonal forecasting system and evaluation of its hindcast ensemble simulations. Science and Technology Infusion Climate Bulletin", 36th NOAA Annual Climate Diagnostics and Prediction Workshop, pp.78~82 (2011), NOAA’s National Weather Service
- 이강진, "원격상관을 이용한 동아시아 6월 강수의 예측", 대기, 26(4), pp.711~716 (2016), 한국기상학회
- Lorenz, E. N., "Deterministic nonperiodic flow", J. Atmos. Sci, 20, pp.130~141 (1963)
- Lue, J. J., "Extended ENSO predictions using a fully coupled ocean-atmosphere model", J. Climate, 21, pp.84~93 (2008)
- C. MacLachlan, "Global Seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system", Quarterly Journal of the Royal Meteorological Society, 141(689), pp.1072~1084 (2015), Wiley[DOI]
- Karen A. McKinnon, "Internal Variability and Regional Climate Trends in an Observational Large Ensemble", Journal of Climate, 31(17), pp.6783~6802 (2018), American Meteorological Society[DOI]
- Megann, A., "GO5.0: the joint NERC-Met Office NEMO global ocean model for use in coupled and forced applications", Geosci. Model Dev, 7, pp.1069~1092 (2014)
- Poveda, G., "Seasonally in ENSO-related precipitation, river discharges soil moisture, and vegetation index in Colombia", Water Resour. Res, 37, pp.2169~2178 (2001)
- J. G. L. Rae, "Development of the Global Sea Ice 6.0 CICE configuration for the Met Office Global Coupled model", Geoscientific Model Development, 8(7), pp.2221~2230 (2015), Copernicus GmbH[DOI]
- Rodo, X., "Variations in seasonal rainfall in Southern Europe during the present century: relationships with the North Atlantic Oscillation and the El Nino-Southern Oscillation", Climate Dyn, 13, pp.275~284 (1997)
- 류용준, "웨이블렛 변환을 적용한 인공신경망에 의한 충주댐 일유입량 예측", 한국수자원학회 논문집, 45(12), pp.1321~1330 (2012), 한국수자원학회
- Adam A. Scaife, "A signal-to-noise paradox in climate science", npj Climate and Atmospheric Science, 1(1) (2018), Springer Science and Business Media LLC[DOI]
- A. A. Scaife, "Skillful long-range prediction of European and North American winters", Geophysical Research Letters, 41(7), pp.2514~2519 (2014), American Geophysical Union (AGU)[DOI]
- Warren J. Tennant, "Using a Stochastic Kinetic Energy Backscatter Scheme to Improve MOGREPS Probabilistic Forecast Skill", Monthly Weather Review, 139(4), pp.1190~1206 (2011), American Meteorological Society[DOI]
- ENDGame: A new dynamical core for seamless atmospheric prediction, "Walters, D.", Met Office, 27~ (2014)
- Walters, D., N., "The Met Office Unified Model Global Atmosphere 6.0", 6.1 and JULES Global Land 6.0, Geosci. Model Dev, pp.2017, 10[DOI]
- D. N. Walters, "The Met Office Unified Model Global Atmosphere 3.0", 3.1 and JULES Global Land 3.0, Geoscientific Model Development, pp.2011, 4(4)[DOI]
- Wang, B., "Advance and prospectus of seasonal prediction: assessment of APCC", CliPAS 14-model ensemble retrospective seasonal prediction (1980~2004), 2004, pp.33, 93~117
- K. D. Williams, "The Met Office Global Coupled model 2.0 (GC2) configuration", Geoscientific Model Development, 8(5), pp.1509~1524 (2015), Copernicus GmbH[DOI]