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

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 ~ ~ ~ ~ ~ Model BaseGDAPS (JMA)UM (UKMO, ver7.5)UM ver7.7UM ver7.9 Assimilation Method 3D Var4D Var 4D-Var Hybrid Ensemble 4D Var Horizontal Resolution T213 (Gausian grid) degree in lat/lon N320 (~40km) in lon/ in lat. N320 (~40km) in lon/ in lat. N320 (~40km) in lon/ in lat. N320 (~40km) in lon/ in lat. Vertical levels / top of model 40 / ~0.4 hPa50 / ~63 km70 / ~80 km Initial Times 00,12 00, 12 (06, 18 for cycled hybrid) Lead Time 10 days 12 days Output Frequency 6h 6h to 240h,12h to 288 No. of Members (+control) Coupled Ocean No Initial Perturbations Breeding + factor rotation ETKF Model Perturbations NoRP, SKEB2 Surface Perturbations No SST Perturbation

4 Major change in EPSG in 2012~13 Trim obstore OPS ETKFETKF UMN320L70UMN320L70 Varobs obstore -6 hour EPSG cycle -6 hour EPSG cycle OPS background ETKF background SSTstatisticsSSTstatistics GDPS(N512L70) OPS, VAR, UM GDPS(N512L70) Initial Dump Reconfiguration Initial Dump Reconfiguration +6 hour EPSG cycle +6 hour EPSG cycle Trimmed obstore Varobs,modelobs Perts(u,v,p,q,t) N320L70 T+0 Perts(SST) Perts(u,v,p,q,t,SST) ETKF background FieldCalc VarSCR_UMFileUnit N512L70 T+0 VAR background GDPS(N512L70)4DVARGDPS(N512L70)4DVAR

5 Sensitivity to ensemble members OPERM22M44 ObservationsKMA ODB Data assimilation4dVarHybrid Ens. 4dVar Ensemble members excluding control Model versionUM 7.9 Background errorStatistical BE0.8*Statistical_BE + 0.5*Ens_BE

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

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

8 SH Z500 error against observation Sensitivity to ensemble members 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) OPER M22 Analysis M44

10 RUN TIME (minute) OperationM22M44 Trim333 OPS6610 ETKF5510 Reconfiguration222 SST111 Forecast (10d/9h)7070/6 OperationM22M44 Trim200M OPS6G 12G ETKF+SST20G 36G Reconfiguration3.5G UM Forecast(10d/9h)124G131G/46G265G/90G TOTAL(1day)308G776G1,484G Data size: operation(2 times/day), M22/44(4 times/day)x ERLY/LATE Considerations for implementation

11 Sensitivity to cycle strategy

12 Number of ingested observations Period: ~ UTC About 85~90% of satellite data are ingested in the early cycle experiments.

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

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 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. 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 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. Future plans of KMA EPSs 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)

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 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.