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http://dx.doi.org/10.14191/Atmos.2017.27.3.261

Performance Assessment of Weekly Ensemble Prediction Data at Seasonal Forecast System with High Resolution  

Ham, Hyunjun (Global Environment System Research Division, National Institute of Meteorological Research)
Won, Dukjin (Meteorological Service Policy Division, Meteorological Service Promotion Bureau, Korea Meteorological Administration)
Lee, Yei-sook (Forecast Technology Division, Forecast Bureau, Korea Meteorological Administration)
Publication Information
Atmosphere / v.27, no.3, 2017 , pp. 261-276 More about this Journal
Abstract
The main objectives of this study are to introduce Global Seasonal forecasting system version5 (GloSea5) of KMA and to evaluate the performance of ensemble prediction of system. KMA has performed an operational seasonal forecast system which is a joint system between KMA and UK Met office since 2014. GloSea5 is a fully coupled global climate model which consists of atmosphere (UM), ocean (NEMO), land surface (JULES) and sea ice (CICE) components through the coupler OASIS. The model resolution, used in GloSea5, is N216L85 (~60 km in mid-latitudes) in the atmosphere and ORCA0.25L75 ($0.25^{\circ}$ on a tri-polar grid) in the ocean. In this research, we evaluate the performance of this system using by RMSE, Correlation and MSSS for ensemble mean values. The forecast (FCST) and hindcast (HCST) are separately verified, and the operational data of GloSea5 are used from 2014 to 2015. The performance skills are similar to the past study. For example, the RMSE of h500 is increased from 22.30 gpm of 1 week forecast to 53.82 gpm of 7 week forecast but there is a similar error about 50~53 gpm after 3 week forecast. The Nino Index of SST shows a great correlation (higher than 0.9) up to 7 week forecast in Nino 3.4 area. It can be concluded that GloSea5 has a great performance for seasonal prediction.
Keywords
GloSea5; sub-seasonal prediction; seasonal prediction; weekly ensemble; weighted ensemble;
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