Current Status and Plans of Ensemble Prediction System at KMA

1. GIFS-TIGGE WG 11th meeting, Exeter, UK Current Status and Plans of Ensemble Prediction System at KMA Seung-Woo Lee Numerical Model Development Division Korea Meteorological Administration

2. Contents • Outline of KMA operational EPS (KMA EPSG) • Sensitivity test of KMA Hybrid Ensemble-4dVAR • Future plans of KMA EPSs • Summary

3. Brief history of KMA EPSG for TIGGE

4. Major change in EPSG in 2012~13 GDPS(N512L70) OPS, VAR, UM Trim obstore Varobs obstore Trimmed obstore N512L70 T+0 Initial Dump Reconfiguration OPS OPS background N320L70 T+0 -6 hour EPSG cycle Varobs,modelobs 2012. 6. FieldCalc ETKF ETKF background SST statistics Perts(SST) +6 hour EPSG cycle VarSCR_UMFileUnit Perts(u,v,p,q,t) OPS background ETKF background UM N320L70 Perts(u,v,p,q,t,SST) VAR background GDPS(N512L70) 4DVAR 2013. 7.

5. Sensitivity to ensemble members

6. Sensitivity to ensemble members • Test period : 2012. 8. 3. 12Z -2012. 8. 3. 29. 12Z RMS averaged for all perturbation members and levels Unstable in model dynamics due to gravity wave drag parameterization. Stable after 36 hours

7. Sensitivity to ensemble members NH Z500 error against with observation • Spread increased significantly in NH and Tropics, while the CRPSS and BSS are not significantly changed.

8. Sensitivity to ensemble members SH Z500 error against observation • Spread decreased significantly only in SH. • M44 is a little better than M22 until T+144 • Only Spread of both M22 and M44 is significant at the critical level=0.05

9. Impact on typhoon 4-day forecast (GDPS) M22 OPER M44 Analysis

10. Considerations for implementation RUN TIME (minute) Data size: operation(2 times/day), M22/44(4 times/day)x ERLY/LATE

11. Sensitivity to cycle strategy

12. Number of ingested observations • Period: 2012. 6. 26. 00 ~ 2012. 7. 11. 18 UTC • About 85~90% of satellite data are ingested in the early cycle experiments.

13. RMSE and Spread Difference between each variant and 1st variant (Type 1)

14. Relative performances • Independent early cycle (Type 3 and 4) showed improved ensemble spread. • Type 1 for NH, Type 4 for SH, and Type 1 or 3 for Tropics • Type 2 reveals poorer performance than other types of hybrid

15. Verification against with observation • Hybrid implementation of type 3 showed improved ensemble spread for Northern and Southern Hemisphere. • Over the tropics and Asian region, type 2 and 4 showed improved performances.

16. Future plans of KMA EPSs • Seamless prediction from medium range to sub seasonal scale • Increased spatial resolution and ensemble members EPSG, which covers forecast range of medium to sub-seasonal scale of 3~4-weeks. • Coupling of ocean model • Implementation of extended EPSG with coupled ocean model (Operation planned in 2014) • - Plans to evolve EPSG covers one-month period of forecast. • Convective scale ensemble prediction system • Developing a convective scale EPS to provide short-range probabilities of high impact weather over local area (Operation planned in 2015) • Data Assimilation • Further optimization of Hybrid Ensemble 4DVAR system (in 2013) • Introducing of 4D Ensemble-Var (next generation EPSG, in 5 years) • - Aiming at direct ensemble data assimilation with 4dVar

17. Summary • KMA has been operating and developing a global EPS. • introducing SST perturbation, hybrid ensemble 4dVar. • sensitivity test shows a minor improvement in 44-members of hybrid ensemble 4dVar, and a similar effect for each configuration of operating strategies. • KMA has plan to operate a global high-resolution EPSG, which has forecast lead times from medium-range up to 3-weeks in 2016. • with the coupling of ocean model and aim at development of one month forecast EPSG. • Research and development for the convective scale ensemble prediction system are conducted. • targeting short-range probabilistic forecast of local high impact weather.

18. Thank You