Mesoscale Ensemble Data Assimilation: Opportunities and Challenges. Fuqing Zhang Penn State University

Transcription

1 Mesoscale Ensemble Data Assimilation: Opportunities and Challenges Fuqing Zhang Penn State University

2 Mesoscale EnKF: some incomplete background 1 st proposed by Evensen (1994); Houtekamer and Micthell (1998) 1 st for atmospheric applications; Hamil and Snyder (2000) for hybrid; Bishop et al. (2001) for ETKF; Anderson (2001) for EAKF; Whitaker and Hamil (2002) for EnSRF; Houtekamer et al. (2005) for operational NWP 1 st for regional/meso/convective-scale models: Snyder and Zhang (2003) with an anelastic cloud model with synthetic radar obs; Dowell et al. (2004) for real-data LAM application; Caya et al. (2005) LAM comparison with 4DVar; Barker (2005) and Tong and Xue (2005) and Zhang et al. (2006) with LAM PE models; Aksoy et al. (2006ab) for parameter estimation; Fujita et al. (2007) and Meng and Zhang (2007) with multi-physics ensembles; Chen and Snyder (2007) for hurricanes; Torn et al. (2007) for LBC treatment; Meng and Zhang (2008a,b) systematic comparison with 3DVar; Torn and Hakim (2008) for real-time LAM EnKF; Zhang et al. (2009) for cloud-resolving hurricane analysis with radar obs; Wang et al. (2009a,b) hybrid with 3DVar; Hu et al. (2010) for real-data parameter estimation; Zhang et al. (2010) real-data comparison with 4DVar; Zhang and Zhang (2010) coupling with 4DVar Meng and Zhang (2010), a comprehensive LAM EnKF review

3 Mesoscale EnKF: some notable events 2002: NSF-ITR ~$1.7million funding to Snyder, Hamill, Hakim and Zhang 2002: Jeff Anderson officially joined NCAR, new era of EnKF with DART 2003: 1 st EnKF workshop at NCAR convened by Chris Snyder, mostly attended by the NSF-ITR funded groups + OU/NSSL/FSL folks, ~15 people 2006/2008: 2 nd and 3 rd EnKF workshops at Balcony Springs, Texas led by Snyder, Zhang and Dowell; -~35-40 attendees for both 2 nd and 3 rd workshops 2008: WMO EnKF-4DVar intercomparison workshop in Argentina convened by Kalnay, Fillion, Errico and Zhang --- official enshrinement for EnKF : HFIP-TACC real-data EnKF fcst expts with GFS-FIM-WRF-AHW 2010: 4 th EnKF workshop in Rensselaerville, New York by Zhang, Snyder, Dowell and Torn with 75 attendees including those from NWP centers (ECMWF, UKMet, NCEP, JMA, Canada EC, Italian CNMCA) and most US EnKF groups (NCAR, ESRL, NSSL, HRD, NRL, NASA, OU, UMD, TAMU, PSU, Albany, UW, U Wisc, ) major themes: operational/real-time, radar/convective, hurricanes, hybrid, adaptive localization and inflation

4 2009 HFIP realtime GFS-EnKF (Hamill et al. 2010) Track error major findings: (1) Experimental T382 GFS/EnKF beats NCEP operational handily. (2) Experimental T382 GFS/EnKF competitive with ECMWF (3) Experimental T382 GFS/EnKF has overall spread-error calibration. (4) FIM/EnKF not quite as skillful as GFS/EnKF. (5) CMC not as skillful, but calibrated. (6) UKMO not as skillful, under-spread. 4

5 Predicting Typhoon Morakot s Catastrophic Rainfall With a Cloud-Resolving Mesoscale Ensemble Prediction System WRF ensemble: two domains with 13.5/4.5km grid spacing, 60-member initialized with GFS EnKF perturbations running at TACC ranger cluster as part of HFIP Two deterministic WRF forecasts: one initialized with mean GFS EnKF analysis (IC_EDA) and the other with operational GFS GSI analysis (IC_GSI) GFS EnKF: real-time analysis since July 1 at the operational resolution assimilating the same data as in NCEP operational GFS GSI system, also at TACC (Jeff Whitaker) (Zhang et al. 2010a, WAF in press)

6 Typhoon Morakot 2009 over Taiwan

7 Probabilistic Forecast of Morakot Rainfall by the Cloud-resolving ensemble prediction

8 Assimilate W88D Vr for Humberto with EnKF WRF domains: D1-D2-D3-D4 grid sizes , 13.5, 4.5, 1.5km (movable) Physics: WSM 6-class microphysics; YSU PBL; Grell-Devenyi CPS EnKF (Evensen 1994; Meng & Zhang 2008a,b): - 30-member ensemble - Initialized at 00Z 12 using 3DVar background uncertainty with FNL analysis; GFS forecast used for boundary condition in forecasts - Advantage: flow-dependent background error covariance from ensemble; flow dependent analysis uncertainty for ensemble forecasting Data assimilated: WSR88D at KCRP, KHGX and KLCH radar radial velocity every hour from 09Z to 21Z 12 Sept Data assimilation are performed for all domains; obs err 3m/s D1 KCRP KHGX KLCH

9 Assimilate W88D Doppler Vr for Humberto 05 WRF/EnKF Forecast vs. Observations vs. 3DVAR Min SLP Max wind The WRF/3DVAR (as a surrogate of operational algorithm) assimilates the same radar data but without flow-dependent background error covariance, its forecast failed to develop the storm despite fit to the best-track observation better initially (Zhang et al MWR)

10 Assimilating Airborne Doppler Radar Winds Available for 20+ years but never used in operational models due to the lack of resolution and/or the lack of efficient data assimilation methods SOs: 1. Separate forward and backward scans; 2. removing data with vertical pointing angles greater than 45 degree; 3. treat every 3 adjacent full scans as one fixed-space radar (translation<5km); 4. thinning ---one bin for 5 km in radial distance and 5 degree in scanning angle; 5. use medium as SO after several additional QC criteria checking These SOs are generated on flight of NOAA P3 s; transmitted to ground in realtime

11 WRF/EnKF Performance With airborne Vr obs 30-member ensemble forecast from EnKF posterior uncertainty MinSLP MaxWSP

12 Verification of flight-level wind, T and RH (leg 5) WSP (m/s) Sensitivity to ensemble size: 30 good, >=60 even better T (C) RH (%)

13 Hurricane IKE (2008) Realtime EnKF assimilation of airborne Doppler winds MinSLP MaxWSP

14 Assimilating 88D vs. CASA radar obs Nathan Snook, Ming Xue & Y. Jun Main convective line and trailing stratiform region well represented. Cells in SW portion of domain too weak in the models. Models underestimated intensity of small individual cells ahead of the convective line. Observed and simulated composite radar reflectivity, 02:00 UTC

15 Intercomparison and Coupling of EnKF with 4DVAR Proof-of-concept study with Lorenz 96 model (Zhang, Zhang and Hansen 2009 AAS) Real-data study with WRF (Meng Zhang s dissertation at PSU )

16 Comparison and coupling: DA Configurations over June 2003 WRF-ARW V3.1 (Shamarock et al. 2005) 90-km grids covering North America; 27 vertical levels up to 50 hpa; LBCs interpolated from FNL analysis EnKF (Meng and Zhang 2008a, b) 40-member ensemble with multi-schemes 1800-km influence radius for localization 0.8 relaxation and perturbed LBCs 3D/4D-Var of WRFDA V3.1 (Barker et al. 2004; Huang et al. 2009) NMC background error covariance (B); Var-scale at 3.0 and Length-scale at h assimilation window (covering -3 to +3 h at every analysis time) E4DVAR: coupling EnKF with 4D-Var (Zhang et al. 2009) Perturbations are updated by EnKF, while mean is updated by 4D-Var Ensemble-based B is introduced into cost function via Alpha-control transform ( Lorenc 2003; Wang et al. 2007, 2008a, b) ensemble-b is localized with the influence radius of 1800-km ensemble-b and NMC-B are weighted at,(1.25) 0.8 and 0.2, respectively ens nmc

17 Comparison of EnKF with 3DVAR/4DVAR: 12h RMSE

18 Comparison of EnKF with 3DVAR/4DVAR: 0-72h

19 Intercomparison and Coupling of EnKF with 4DVAR

20 Intercomparison and Coupling of EnKF with 4DVAR

21 Mesoscale EnKF: Key Issues and Challenges Key Issues identified from the 2008/2010 EnKF workshops Model error Covariance inflation or additive noise, going adaptive Multi-physics ensemble; parameter perturbation within one parameterization? Shall we go for multi-model ensemble? Incompatible state elements Sampling error Covariance localization: wide open in terms of optimality, balance issues Localization for multiscales: Adaptive localization, Successive localization Hybrid with VAR: are there real advantages? Useful in model error treatment; sampling error, non local obs localization, balance, Computational consideration: ensemble size, resolution, parallelization Assimilate satellite radiance or radar reflectivity: error in OBS operator H Bias correction for limited-area model without global statistics