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.
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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 Contents • Outline of KMA operational EPS (KMA EPSG) • Sensitivity test of KMA Hybrid Ensemble-4dVAR • Future plans of KMA EPSs • Summary 2 Brief history of KMA EPSG for TIGGE 2006.07.~2010.12. 2010.12~2011.05 2011.5~2012.6. 2012.6~2013.6 2013.7.~ Model Base GDAPS (JMA) UM (UKMO, ver7.5) UM ver7.7 UM ver7.9 UM ver7.9 Assimilation Method 3D‐Var 4D‐Var 4D-Var Hybrid Ensemble 4D‐Var Horizontal Resolution N320 (~40km) N320 (~40km) T213 (Gausian grid) 0.5625 in lon/ 0.375 0.5625 in lon/ 0.5625 degree in lat/lon in lat. 0.375 in lat. N320 (~40km) 0.5625 in lon/ 0.375 in lat. N320 (~40km) 0.5625 in lon/ 0.375 in lat. 4D‐Var Vertical levels / top 40 / ~0.4 hPa of model 50 / ~63 km 70 / ~80 km 70 / ~80 km 70 / ~80 km Initial Times 00,12 00, 12 00,12 00,12 00, 12 (06, 18 for cycled hybrid) Lead Time 10 days 10 days 10 days 10 days 12 days Output Frequency 6h 6h 6h 6h 6h to 240h,12h to 288 No. of 15+1 Members (+control) 23+1 23+1 23+1 23+1 Coupled Ocean No No No No No Initial Perturbations Breeding + factor rotation ETKF ETKF ETKF ETKF Model Perturbations No RP, SKEB2 RP, SKEB2 RP, SKEB2 RP, SKEB2 Surface 3 Perturbations No No No SST Perturbation SST Perturbation Major change in EPSG in 2012~13 GDPS(N512L70) OPS, VAR, UM SST statistics 4 Trim obstore N512L70 T+0 Trimmed obstore Initial Dump Reconfiguration OPS N320L70 T+0 2012. 6. Varobs obstore Varobs,modelobs OPS background -6 hour EPSG cycle FieldCalc ETKF ETKF background Perts(SST) VarSCR_UMFileUnit Perts(u,v,p,q,t) Perts(u,v,p,q,t,SST) UM N320L70 GDPS(N512L70) 4DVAR +6 hour EPSG cycle OPS background ETKF background VAR background 2013. 7. Sensitivity to ensemble members 5 OPER M22 M44 Observations KMA ODB KMA ODB KMA ODB Data assimilation 4dVar Hybrid Ens. 4dVar Hybrid Ens. 4dVar Ensemble members excluding control 23 22 44 Model version UM 7.9 UM 7.9 UM 7.9 Background error Statistical BE 0.8*Statistical_BE + 0.5*Ens_BE 0.8*Statistical_BE + 0.5*Ens_BE 6 • Test period : 2012. 8. 3. 12Z -2012. 8. 3. 29. 12Z 2012083000 2012082900 2012082800 2012082700 6 2012082600 Stable after 36 hours 2012082500 8 2012082400 2012082300 2012082200 2012082100 2012082000 2012081900 2012081800 2012081700 2012081600 2012081500 2012081400 2012081300 2012081200 2012081100 2012081000 2012080900 2012080800 2012080700 2012080600 2012080500 2012080400 7 2012080300 2012080200 theta Sensitivity to ensemble members RMS averaged for all perturbation members and levels Avg RMS of Perturbations Unstable in model dynamics due to gravity wave drag parameterization. 5 4 t_oper 3 t_m22 2 t_m44 1 0 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. 7 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 8 Impact on typhoon 4-day forecast (GDPS) OPER Analysis 9 M22 M44 Considerations for implementation RUN TIME (minute) Operation M22 M44 Trim 3 3 3 OPS 6 6 10 ETKF 5 5 10 Reconfiguration 2 2 2 SST 1 1 1 Forecast (10d/9h) 70 70/6 70/6 Data size: operation(2 times/day), M22/44(4 times/day)x ERLY/LATE 10 Operation M22 M44 Trim 200M 200M 200M OPS 6G 6G 12G ETKF+SST 20G 20G 36G Reconfiguration 3.5G 3.5G 3.5G UM Forecast(10d/9h) 124G 131G/46G 265G/90G TOTAL(1day) 308G 776G 1,484G Sensitivity to cycle strategy Type 1 06 UTC GDAPS ERLY LATE EPSG ERLY LATE Type 2 GDAPS EPSG 11 06 UTC ERLY LATE LATE 12 UTC ERLY ERLY (10d) LATE ERLY LATE LATE ERLY LATE 12 UTC ERLY ERLY (10d) 18 UTC LATE LATE 18 UTC ERLY LATE LATE 00 UTC ERLY ERLY (10d) LATE GDAPS LATE EPSG 00 UTC ERLY ERLY (10d) Type 3 Type 4 06 UTC ERLY LATE LATE 06 UTC LATE GDAPS ERLY LATE EPSF ERLY LATE 12 UTC ERLY ERLY (10d) LATE ERLY (10d) ERLY LATE 12 UTC ERLY 18 UTC LATE LATE LATE 18 UTC ERLY ERLY LATE 00 UTC ERLY ERLY (10d) LATE LATE 00 UTC ERLY ERLY (10d) LATE 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. 12 RMSE and Spread Difference between each variant and 1st variant (Type 1) 13 Relative performances 1 4 3 3 3 3 2 4 1 4 3 1 4 4 3 3 2 4 1 3 3 4 3 4 • 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 14 Verification against with observation average of the difference ratio against the best for t850 average of the difference ratio against the best for Z500 5 8 7 4 6 3 Type 1 1안 4 Type 2 2안 3 ratio ratio 5 Type 1안 1 Type 2안 2 2 Type 3안 3 Type 3 3안 2 Type 4 4안 Type 4안 4 1 1 0 0 RMSE RMSE RMSE RMSE SPREADSPREADSPREAD SPREAD RMSE RMSE RMSE RMSE SPREADSPREADSPREAD SPREAD NH SH TR AR NH SH TR AR NH SH TR AR NH SH TR AR 3/1 4 2 1 3 3 2 4 3 1/4 3 1 3 3 2 4 • 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. 15 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 16 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. 17