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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.

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Zhiyong Meng & Fuqing Zhang Texas A&M University

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  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

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