The Ensemble Prediction Systems at NMC/CMA

Transcription

1 The Ensemble Prediction Systems at NMC/CMA Xiaoli Li, Jing Chen, Guo Deng, Hua Tian Numerical Prediction Center/ National Meteorological Center, CMA May Maryland, U.S.A

2 Overview Operational EPSs (GEPS and REPS) Research and Development of EPSs Future Plan

3 The operational GEPS ( implemented in Nov. 2006) Configurations of GEPS Model Resolution Daily frequency Forecast length Ensemble Size Initial perturbation method Model perturbation Spectral model T degree ; 31 levels 00 and 12 UTC 10 days 15 ( 14 perturbed member + control run) Breeding of Growth modes (BGM) None

4 Probability EPS Meteogram

5 The Operational REPS (implemented in July 2010) The Development Status of the REPS : the development and testing of the REPS based on B08RDP, and real-time running in the summer experimental periods 2008: on the trial with the operational suite for Beijing Olympic Games since July 2008, 2009: the experiments for extending simulation domain covering (70 o E- 135 o E and 15 o N to 55 o N), and including surface parameter perturbations. 2010: pre-operationally running since July 2010

6 The flowchart of GEPS and REPS GEPS T213L31 WRF REPS Obs SSI 3DVAR Obs WRF 3DVAR Breeding Perturbation Cntl Run Breeding Perturbation Cntl Run IC1 IC2-... IC1 IC2- Ensemble forecasting Cumulus Micro Phys PBL Land surface Multi physics LBCs for REPS Ensemble forecasting Probabilistic Product Generation and output

7 The technical progress of REPS during B08RDP and operational run Model WRF-ARW V2.1 15km ; 35 levels (B08RDP target domain ) WRF-ARW V2.1 15km ; 35 levels (B08RDP target domain ) WRF-ARW V2.2 15km ; 35 levels (B08RDP target domain: ) WRF-ARW V2.2 15km ; 35 levels ( extending domain) Member size IC WRF-3DVAR WRF-3DVAR WRF-3DVAR( with more observation assimilated) WRF-3DVAR( with more observation assimilated) IC pertur. BGM BGM BGM BGM; perturbed surface parameters Model pertur. Multi-physics Multi-physics Multi-physics (optimized) Multi-physics (optimized) LBCs GEPS with OI system GEPS with OI system GEPS with SSI 3D-VAR system. GEPS, with SSI 3D-VAR system. Simulation domain during Simulation domain for operational run in 2010

8 The multi-physics scheme used in the REPS Ens. mem Microphysics scheme Convective scheme PBL scheme Ctrl. Lin scheme Betts MYJ Pair 1 Lin scheme KF YSU Pair 2 Lin scheme Betts-Miller YSU Pair 3 Lin scheme Betts-Miller-Janjic YSU Pair 4 Lin scheme KF MYJ Pair 5 WSM6 Betts-Miller MYJ Pair 6 WSM3 Betts MYJ Pair 7 WSM3 Betts YSU

9 REPS products Types Names Variables EPS products Thresholds 3D (100,150,200,250,300,400,500,600,700,850,925,1000 hpa) HGT Geopotential height Ensemble mean/spread QVAPOR Specific humidity RH Relative humidity UV U V wind components, TCTD Temp. and dew temp. THETASE Potential pseudo-equivalent temp. DBZ Radar reflectivity (reference 30) Ensemble mean/spread; prob. 2D RAIN_3HR RAIN_6HR 3h accum. precip. 6h accum. Precip. Ensemble mean/spread ; Prob. RAIN_12HR 12h accum. Precip. RAIN_24HR 24h accum. precip. RAINC_3HR 3h accum. convective precip. RH2M 2m relative humidity Ensemble mean/spread SAUN Sauna index CIN Convective inhibition SLP Sea level pressure T2M 2m temp. Ensemble mean/spread ; Prob. CAPE Convective available potential energy Ensemble mean/spread, Prob. >1,>10,>30 >0.1,>5,>15,>25,>50(mm) >35,>38(J/kg) >800,>1000(J/kg) UV10M 10m wind speed Ensemble mean/spread, Prob. >8m/s, >12, >16 (m/s) Severe convection param. RISK_PRB Convective risk prob. Prob.

10 The advantages of the REPS for the precipitation forecast

11 Pr eci pi t at i on (mm) An Example of application of REPS for severe weather forecast Zhouqu Debris Flow on Aug This disaster was caused by the very strong short-range precipitation, (more than 77.3 mm/0.5h, this record broke historical extreme), and more than 1478 people were killed Observed hourly precipitation in Dongshan village, Zhouqu from 7 to 8 Aug mm/3h 77.3 mm/0.5h Ti me( ddhh, Bei j i ng t i me)

12 The real-time forecasts from the REPS (48h ahead) Observation in Zhouqu (50mm/24h) 60% probability more than 10mm/24h in Zhouqu

13 Precipitation forecast from ensemble members of the REPS

14 The Ongoing Development for the Operational GEPS The change of initial perturbation method: BGM ET increase ensemble member experimental running for typical season, and evaluation The extended-range forecasts (15 days) of GEPS the experiment of extension to 15 days the development of ensemble products for day 8 to day 15 (probability for temperature anomaly) The random perturbation for model physics adding random perturbation to the temperature tendency of cumulus parameterization

15 CRPS( dm) CRPS( K) SP/ RM( m) The Implementation of ET technique into GEPS and evaluation Experiment : ET- 24member (ET24) ET- 14member (ET14) BGM-14member (BGM14) Experimental period: Nov.10-Nov.23,2007 ET 24 > ET 14 > BGM CRPS 500hPa geo. height For ecast l ead t i me( days) bgm et 14 et 24 Ensemble spread and ensemble mean error 500hPa geo. height For ecast l ead t i me( days) CRPS 850 hpa temp For ecast l ead t i me( days) CONTROL- RM ET14- RM ET14- SP ET- 24- RM ET- 24- SP BGM14- RM BGM14- SP bgm et 14 et 24

16 The development of EPS based on GRAPES model Singular vector (SVs) technique for initial perturbation The calculation of SVs based on the GRAPES-4DVAR system The experiment of the REPS with GRAPES-Meso model by using SV perturbation The stochastic perturbation for model physics The introduction of stochastic process based on first-order Markov chain (3D,correlated in space and time) into GRAEPS-Meso model Applying stochastic perturbation to the model physical tendencies

17 Future Plan The pre-operational implementation of ET into the GEPS by the end of GEPS operationally running up to 15 days, and issue the extended probability forecast for day 8 to day 15. Including the random perturbations into operational GEPS Conducting the experiments for SV-based ensemble forecasting with GRAPES model