1 / 19

Zhiyong Meng & Fuqing Zhang Texas A&M University

Assimilating Sounding, Surface and Profiler Observations with a WRF-based EnKF for An MCV Case during BAMEX. Zhiyong Meng & Fuqing Zhang Texas A&M University. The MCV event of 10-12 June 2003 (IOP 8 of BAMEX). a). c). b). e). f). d). Model domain. Forecast Model: WRF2.1. D1.

sissy
Télécharger la présentation

Zhiyong Meng & Fuqing Zhang Texas A&M University

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Assimilating Sounding, Surface and Profiler Observations with a WRF-based EnKF for An MCV Case during BAMEX Zhiyong Meng & Fuqing Zhang Texas A&M University

  2. The MCV event of 10-12 June 2003 (IOP 8 of BAMEX) a) c) b) e) f) d)

  3. Model domain Forecast Model: WRF2.1 D1 Two domainswith grid sizes of 90 & 30 km ;one-way nesting Physical parameterizations: The Grell cumulus scheme, the WSM 6-class microphysics scheme with graupel, and YSU PBL scheme Data assimilation is only performed in D2 D2

  4. Ensemble forecast in D2Simulated reflectivity(colored) and MSLP (blue lines, every 2 hPa) 12h 24h 11/00 11/12 30h 36h 11/18 12/00 X X L L L: Observed MCV position at surface X: simulated MCV position at surface

  5. Data to be assimilated Profiler (27) (thinned in vertical) 3-h interval Sounding (31) 12-h interval Half Surface (458) 6-h interval X

  6. A WRF-based EnKF A sequential filter: Whitaker and Hamill (2002), Snyder and Zhang (2003) Covariance localization: Gaspari and Cohn (1999), ROIs : Vertical - 15 levels for all data. Horizontal - 300 km for surface data, 900 km for radiosonde & profiler Assimilated variables: u, v and T (same obs errors as NCEP) Ensemble generation: perturbations sampled from WRF/3Dvar background error covariance(Barker et al. 2003) Ensemble size: 30

  7. WRF-3DVAR Objectivesin here: to generate the initial ensemble be a benchmark for the EnKF. Control variables: stream function, pseudo relative humidity,unbalanced part of velocity potential, temperature, and surface pressure. Background error covariance: NMC method. Minimization: Conjugate gradient method.

  8. Experiment design • Sounding assimilation • Profiler assimilation • Surface assimilation • Sounding + Profiler + Surface assimilation • Model error treatments (with Sounding + Profiler + Surface assimilation) - Covariance relaxation - Multi-scheme ensemble All results are verified with sounding except for otherwise specified

  9. Soundingassimilation - cycling at 12h interval (h) (h) (h)

  10. Profiler EnKF assimilation - cycling at different intervals (h) (h) (h) • The final forecast error decreases from 12-h interval to 3-h interval • Further increase of obs frequency worsens the result in general.

  11. Profilerassimilation - cycling at 3h interval (h) (h) (h)

  12. Surface observationassimilation - Cycling at 6-h interval - Verified with the other half surface (upper) and soundings (lower) (h) (h) (h) (h) (h) (h)

  13. Assimilation of Sounding+Surface+Profiler obs - Cycling at 3-h interval (h) (h) (h)

  14. L L L MCV positions at 36h (00UTC Jun.12)Observed radar echo, simulated reflectivity(colored) and MSLP (blue lines, every 2 hPa) No EnKF OBS SND X X SFC SND+SFC+Profiler Profiler X X L L L X X: Simulated MCV position at surface L: Observed MCV position at surface

  15. Model error treatment (Zhang et al. 2004) • Covariance inflation through relaxation (h) (h) (h)

  16. Model error treatment • Multi-cumulus-scheme-ensemble (Fujita et al. 2006 ; Meng & Zhang 2006) The schemes used in ensemble: KF, Grell, and BM (h) (h) (h)

  17. Model error treatment • Multi-cumulus-scheme-ensemble ( Fujita et al. 2006; Meng & Zhang 2006) (h) (h) (h)

  18. Summary • The WRF-based EnKF behaves well when assimilating real observations. It performs better than 3DVAR for this MCV event. • Sounding and profiler assimilation can improve the analysis significantly. Impact of surface data is rather weak and short-term. The best performance is obtained by assimilating all three data sources. • Higher temporal frequency of profiler may give better performance until down to 3-h intervals. • Covariance relaxation and multi-scheme-ensemble can apparently improve the performance of the EnKF in this MCV event, consistent with Fujita et al. (2006) & Meng and Zhang (2006).

  19. What to do next? • Assimilate surface pressure and humidity in addition to u, v and T. • Dropsonde data assimilation • Higher resolution model • Radar data assimilation

More Related