Post on 19-Jan-2016
description
Current Status and Plans of Ensemble Prediction System at KMA
Current Status and Plans of Ensemble Prediction System at KMA
Seung-Woo Lee
Numerical Model Development Division
Korea Meteorological Administration
GIFS-TIGGE WG 11th meeting, Exeter, UK
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ContentsContents
• 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 TIGGEBrief history of KMA EPSG for TIGGE
2006.07.~2010.12.2006.07.~2010.12. 2010.12~2011.052010.12~2011.05 2011.5~2012.6.2011.5~2012.6. 2012.6~2013.62012.6~2013.6 2013.7.~2013.7.~
Model Base GDAPS (JMA) UM (UKMO, ver7.5) UM ver7.7 UM ver7.9 UM ver7.9
AssimilationMethod
3D‐Var 4D‐Var 4D‐Var 4D-Var Hybrid Ensemble Hybrid Ensemble 4D‐Var 4D‐Var
HorizontalResolution
T213 (Gausian grid) 0.5625 degree in lat/lon
N320 (~40km)0.5625 in lon/ 0.375 in lat.
N320 (~40km)0.5625 in lon/ 0.375 in lat.
N320 (~40km)0.5625 in lon/ 0.375 in lat.
N320 (~40km)0.5625 in lon/ 0.375 in lat.
Vertical levels / top of model
40 / ~0.4 hPa 50 / ~63 km 70 / ~80 km 70 / ~80 km 70 / ~80 km
InitialTimes
00,12 00, 12 00,12 00,1200, 12 (06, 18 for cycled hybrid)
LeadTime
10 days 10 days 10 days 10 days 12 days12 days
OutputFrequency
6h 6h 6h 6h 6h to 240h,12h to 288
No. ofMembers (+control)
15+1 23+1 23+1 23+1 23+1
CoupledOcean
No No No No No
InitialPerturbations
Breeding + factor rotation
ETKF ETKF ETKF ETKF
ModelPerturbations
No RP, SKEB2 RP, SKEB2 RP, SKEB2 RP, SKEB2
SurfacePerturbations
No No No SST Perturbation SST Perturbation
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Major change in EPSG in 2012~13Major change in EPSG in 2012~13
Trim obstore
OPS
ETKFETKF
UMUMN320L70N320L70
Varobsobstore
-6 hour-6 hourEPSG cycleEPSG cycle
-6 hour-6 hourEPSG cycleEPSG cycle
OPS background
OPS backgroundETKF background
SST SST statisticsstatistics
SST SST statisticsstatistics
GDPS(N512L70)GDPS(N512L70)OPS, VAR, UMOPS, VAR, UM
GDPS(N512L70)GDPS(N512L70)OPS, VAR, UMOPS, VAR, UM
Initial DumpReconfiguration
+6 hour+6 hourEPSG cycleEPSG cycle
+6 hour+6 hourEPSG cycleEPSG cycle
Trimmed obstore
Varobs,modelobs
Perts(u,v,p,q,t)
N320L70 T+0
Perts(SST)
Perts(u,v,p,q,t,SST)
ETKF backgroundFieldCalc
VarSCR_UMFileUnit
N512L70 T+0
VAR backgroundVAR background
GDPS(N512L70)GDPS(N512L70)4DVAR4DVAR
GDPS(N512L70)GDPS(N512L70)4DVAR4DVAR
2012. 6.2012. 6.
2013. 7.2013. 7.
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Sensitivity to ensemble membersSensitivity to ensemble members
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
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0
1
2
3
4
5
6
7
8
2012
0802
00
2012
0803
00
2012
0804
00
2012
0805
00
2012
0806
00
2012
0807
00
2012
0808
00
2012
0809
00
2012
0810
00
2012
0811
00
2012
0812
00
2012
0813
00
2012
0814
00
2012
0815
00
2012
0816
00
2012
0817
00
2012
0818
00
2012
0819
00
2012
0820
00
2012
0821
00
2012
0822
00
2012
0823
00
2012
0824
00
2012
0825
00
2012
0826
00
2012
0827
00
2012
0828
00
2012
0829
00
2012
0830
00
thet
a
Avg RMS of Perturbations
t_oper
t_m22
t_m44
Stable after 36 hours
Sensitivity to ensemble membersSensitivity 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.
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NH Z500 error against with observation
Sensitivity to ensemble membersSensitivity 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 membersSensitivity 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
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Impact on typhoon 4-day forecast (GDPS)Impact on typhoon 4-day forecast (GDPS)OPER M22
Analysis M44
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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
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
Data size: operation(2 times/day), M22/44(4 times/day)x ERLY/LATE
Considerations for implementationConsiderations for implementation
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Sensitivity to cycle strategySensitivity to cycle strategy
ERLY LATE ERLY LATE ERLY LATE ERLY LATE
ERLY ERLY(10d)
ERLY ERLY(10d)
GDAPS
EPSF
06 UTC 12 UTC 18 UTC 00 UTCType 4
ERLY LATE ERLY LATE ERLY LATE ERLY LATE
LATE ERLY(10d)
LATE LATE ERLY(10d)
LATE
GDAPS
EPSG
06 UTC 12 UTC 18 UTC 00 UTCType 3
ERLY LATE ERLY LATE ERLY LATE ERLY LATE
LATE ERLY(10d)
LATE LATE ERLY(10d)
LATE
GDAPS
EPSG
06 UTC 12 UTC 18 UTC 00 UTCType 2
ERLY LATE ERLY LATE ERLY LATE ERLY LATE
ERLY LATE ERLY(10d)
LATE ERLY LATE ERLY(10d)
LATE
GDAPS
EPSG
06 UTC 12 UTC 18 UTC 00 UTCType 1
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Number of ingested observationsNumber 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.
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Difference between each variant and 1st variant (Type 1)
RMSE and Spread RMSE and Spread
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Relative performancesRelative 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
1 4 3 3 3 3 2 4
1 4 3 1 4 4 3 3
2 4 1 3 3 4 3 4
15
0
1
2
3
4
5
6
7
8
RMSE RMSE RMSE RMSE SPREADSPREADSPREADSPREAD
NH SH TR AR NH SH TR AR
ratio
average of the difference ratio against the best for Z500
1안
2안
3안
4안
Type 1
Type 2
Type 3
Type 4
0
1
2
3
4
5
RMSE RMSE RMSE RMSE SPREADSPREADSPREADSPREAD
NH SH TR AR NH SH TR AR
ratio
average of the difference ratio against the best for t850
1안
2안
3안
4안
Type 1
Type 2
Type 3
Type 4
Verification against with observationVerification 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.
3/1 4 2 1 3 3 2 4 3 1/4 3 1 3 3 2 4
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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 EPSsFuture 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)
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SummarySummary
• 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.