A Prototype Regional GSI-based EnKF-Variational Hybrid Data Assimilation System for the Rapid Refresh Forecasting System:Dual-Resolution Implementation and Testing Results
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摘要
A dual-resolution(DR) version of a regional ensemble Kalman filter(EnKF)-3D ensemble variational(3DEnVar) coupled hybrid data assimilation system is implemented as a prototype for the operational Rapid Refresh forecasting system. The DR 3DEnVar system combines a high-resolution(HR) deterministic background forecast with lower-resolution(LR) EnKF ensemble perturbations used for flow-dependent background error covariance to produce a HR analysis. The computational cost is substantially reduced by running the ensemble forecasts and EnKF analyses at LR. The DR 3DEnVar system is tested with 3-h cycles over a 9-day period using a 40/13-km grid spacing combination. The HR forecasts from the DR hybrid analyses are compared with forecasts launched from HR Gridpoint Statistical Interpolation(GSI) 3D variational(3DVar)analyses, and single LR hybrid analyses interpolated to the HR grid. With the DR 3DEnVar system, a 90% weight for the ensemble covariance yields the lowest forecast errors and the DR hybrid system clearly outperforms the HR GSI 3DVar.Humidity and wind forecasts are also better than those launched from interpolated LR hybrid analyses, but the temperature forecasts are slightly worse. The humidity forecasts are improved most. For precipitation forecasts, the DR 3DEnVar always outperforms HR GSI 3DVar. It also outperforms the LR 3DEnVar, except for the initial forecast period and lower thresholds.
A dual-resolution(DR) version of a regional ensemble Kalman filter(EnKF)-3D ensemble variational(3DEnVar) coupled hybrid data assimilation system is implemented as a prototype for the operational Rapid Refresh forecasting system. The DR 3DEnVar system combines a high-resolution(HR) deterministic background forecast with lower-resolution(LR) EnKF ensemble perturbations used for flow-dependent background error covariance to produce a HR analysis. The computational cost is substantially reduced by running the ensemble forecasts and EnKF analyses at LR. The DR 3DEnVar system is tested with 3-h cycles over a 9-day period using a 40/13-km grid spacing combination. The HR forecasts from the DR hybrid analyses are compared with forecasts launched from HR Gridpoint Statistical Interpolation(GSI) 3D variational(3DVar)analyses, and single LR hybrid analyses interpolated to the HR grid. With the DR 3DEnVar system, a 90% weight for the ensemble covariance yields the lowest forecast errors and the DR hybrid system clearly outperforms the HR GSI 3DVar.Humidity and wind forecasts are also better than those launched from interpolated LR hybrid analyses, but the temperature forecasts are slightly worse. The humidity forecasts are improved most. For precipitation forecasts, the DR 3DEnVar always outperforms HR GSI 3DVar. It also outperforms the LR 3DEnVar, except for the initial forecast period and lower thresholds.
A dual-resolution(DR) version of a regional ensemble Kalman filter(EnKF)-3D ensemble variational(3DEnVar) coupled hybrid data assimilation system is implemented as a prototype for the operational Rapid Refresh forecasting system. The DR 3DEnVar system combines a high-resolution(HR) deterministic background forecast with lower-resolution(LR) EnKF ensemble perturbations used for flow-dependent background error covariance to produce a HR analysis. The computational cost is substantially reduced by running the ensemble forecasts and EnKF analyses at LR. The DR 3DEnVar system is tested with 3-h cycles over a 9-day period using a 40/13-km grid spacing combination. The HR forecasts from the DR hybrid analyses are compared with forecasts launched from HR Gridpoint Statistical Interpolation(GSI) 3D variational(3DVar)analyses, and single LR hybrid analyses interpolated to the HR grid. With the DR 3DEnVar system, a 90% weight for the ensemble covariance yields the lowest forecast errors and the DR hybrid system clearly outperforms the HR GSI 3DVar.Humidity and wind forecasts are also better than those launched from interpolated LR hybrid analyses, but the temperature forecasts are slightly worse. The humidity forecasts are improved most. For precipitation forecasts, the DR 3DEnVar always outperforms HR GSI 3DVar. It also outperforms the LR 3DEnVar, except for the initial forecast period and lower thresholds.
引文
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Barker,D.M.,W.Huang,Y.-R.Guo,A.J.Bourgeois,and Q.N.Xiao,2004:A three-dimensional variational data assimilation system for MM5:Implementation and initial results.Mon.Wea.Rev.,132,897-914,https://doi.org/10.1175/1520-0493(2004)132<0897:ATVDAS>2.0.CO;2.
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Benjamin,S.G.,and Coauthors,2016:A north American hourly assimilation and model forecast cycle:The rapid refresh.Mon.Wea.Rev.,144,1669-1694,https://doi.org/0.1175/MWR-D-15-0242.1.
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Buehner,M.,and A.Mahidjiba,2010:Sensitivity of global ensemble forecasts to the initial ensemble mean and perturbations:Comparison of En KF,singular vector,and 4D-var approaches.Mon.Wea.Rev.,138,3886-3904,https://doi.org/10.1175/2010MWR3296.1.
Buehner,M.,and A.Shlyaeva,2015:Scale-dependent backgrounderror covariance localisation.Tellus A,67,28027,https://doi.org/10.3402/tellusa.v67.28027.
Buehner,M.,P.L.Houtekamer,C.Charette,H.L.Mitchell,and B.He,2010a:Intercomparison of variational data assimilation and the ensemble Kalman filter for global deterministic NWP.Part I:Description and single-observation experiments.Mon.Wea.Rev.,138,1550-1566,https://doi.org/10.1175/2009MWR3157.1.
Buehner,M.,P.L.Houtekamer,C.Charette,H.L.Mitchell,and B.He,2010b:Intercomparison of variational data assimilation and the ensemble Kalman filter for global deterministic NWP.Part II:One-month experiments with real observations.Mon.Wea.Rev.,138,1567-1586,https://doi.org/10.1175/2009MWR3158.1.
Candille,G.,C.C?ot′e,P.L.Houtekamer,and G.Pellerin,2007:Verification of an ensemble prediction system against observations.Mon.Wea.Rev.,135,2688-2699,https://doi.org/10.1175/MWR3414.1.
Clayton,A.M.,A.C.Lorenc,and D.M.Barker,2013:Operational implementation of a hybrid ensemble/4D-Var global data assimilation system at the Met Office.Quart.J.Roy.Meteor.Soc.,139,1445-1461,https://doi.org/10.1002/qj.2054.
Descombes,G.,T.Aulign′e,F.Vandenberghe,D.M.Barker,and J.Barr′e,2015:Generalized background error covariance matrix model(GEN BE v2.0).Geoscientific Model Development,8,669-696,https://doi.org/10.5194/gmd-8-669-2015.
Evensen,G.,1994:Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics.J.Geophys.Res.,99,10 143-10 162,https://doi.org/10.1029/94JC00572.
Gandin,L.S.,and A.H.Murphy,1992:Equitable skill scores for categorical forecasts.Mon.Wea.Rev.,120,361-370,https://doi.org/10.1175/1520-0493(1992)120<0361:ESSFCF>2.0.CO;2.
Greybush,S.J.,E.Kalnay,T.Miyoshi,K.Ide,and B.R.Hunt,2010:Balance and ensemble Kalman filter localization techniques.Mon.Wea.Rev.,139,511-522,https://doi.org/10.1175/2010MWR3328.1.
Hamill,T.M.,and C.Snyder,2000:A hybrid ensemble Kalman filter-3D variational analysis scheme.Mon.Wea.Rev.,128,2905-2919,https://doi.org/10.1175/1520-0493(2000)128<2905:AHEKFV>2.0.CO;2.
Hamill,T.M.,J.S.Whitaker,and C.Snyder,2001:Distancedependent filtering of background error covariance estimates in an ensemble Kalman filter.Mon.Wea.Rev.,129,2776-2790,https://doi.org/10.1175/1520-0493(2001)129<2776:DDFOBE>2.0.CO;2.
Hamill,T.M.,J.S.Whitaker,M.Fiorino,and S.G.Benjamin,2011:Global ensemble predictions of 2009’s tropical cyclones initialized with an ensemble Kalman filter.Mon.Wea.Rev.,139,668-688,https://doi.org/10.1175/2010MWR3456.1.
Hu,M.,H.Shao,D.Stark,K.Newman,C.Zhou,and X.Zhang,2016:Grid-Point Statistical Interpolation(GSI)User’s Guide Version 3.5.Developmental Testbed Center,141 pp.[Available online at http://www.dtcenter.org/comGSI/users/docs/index.php]
Kleist,D.T.,and K.Ide,2015:An OSSE-based evaluation of hybrid variational-ensemble data assimilation for the NCEPGFS.Part II:4DEn Var and hybrid variants.Mon.Wea.Rev.,143,452-470,https://doi.org/10.1175/MWR-D-13-00350.1.
Kleist,D.T.,D.F.Parrish,J.C.Derber,R.Treadon,W.-S.Wu,and S.Lord,2009:Introduction of the GSI into the NCEP global data assimilation system.Wea.Forecasting,24,1691-1705,https://doi.org/10.1175/2009WAF2222201.1.
Kuhl,D.D.,T.E.Rosmond,C.H.Bishop,J.Mc Lay,and N.L.Baker,2013:Comparison of hybrid ensemble/4DVar and4DVar within the NAVDAS-AR data assimilation framework.Mon.Wea.Rev.,141,2740-2758,https://doi.org/10.1175/MWR-D-12-00182.1.
Li,Y.Z.,X.G.Wang,and M.Xue,2012:Assimilation of radar radial velocity data with the WRF hybrid ensemble-3DVARsystem for the prediction of hurricane Ike(2008).Mon.Wea.Rev.,140,3507-3524,https://doi.org/10.1175/MWR-D-12-00043.1.
Li,Z.J.,J.C.Mc Williams,K.Ide,and J.D.Farrara,2015:A multiscale variational data assimilation scheme:Formulation and illustration.Mon.Wea.Rev.,143,3804-3822,https://doi.org/10.1175/MWR-D-14-00384.1.
Lin,Y.,and K.E.Mitchell,2005:The NCEP Stage II/IV hourly precipitation analyses:Development and applications.19th Conference on Hydrology,Paper 1.2,American Meteor Society,San Diego,CA.
Liu,H.X.,and M.Xue,2008:Prediction of convective initiation and storm evolution on 12 June 2002 during IHOP 2002.Part I:Control simulation and sensitivity experiments.Mon.Wea.Rev.,136,2261-2283,https://doi.org/10.1175/2007MWR2161.1.
Lorenc,A.C.,1986:Analysis methods for numerical weather prediction.Quart.J.Roy.Meteor.Soc.,112,1177-1194,https://doi.org/10.1002/qj.49711247414.
Lorenc,A.C.,2003:The potential of the ensemble Kalman filter for NWP-a comparison with 4D-Var.Quart.J.Roy.Meteor.Soc.,129,3183-3204,https://doi.org/10.1256/qj.02.132.
Meng,Z.Y.,and F.Q.Zhang,2007:Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation.Part II:Imperfect model experiments.Mon.Wea.Rev.,135,1403-1423,https://doi.org/10.1175/MWR3101.1.
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