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Corey Potvin and Lou Wicker, NSSL Storm-Scale Radar Data Assimilation Workshop

Comparisons of kinematical retrievals within a simulated supercell: Dual-Doppler analysis (DDA) vs. EnKF radar data assimilation. Corey Potvin and Lou Wicker, NSSL Storm-Scale Radar Data Assimilation Workshop October 17-18, 2011 Norman, OK. Motivation.

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Corey Potvin and Lou Wicker, NSSL Storm-Scale Radar Data Assimilation Workshop

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  1. Comparisons of kinematical retrievals within a simulated supercell: Dual-Doppler analysis (DDA) vs. EnKF radar data assimilation Corey Potvin and Lou Wicker, NSSL Storm-Scale Radar Data Assimilation Workshop October 17-18, 2011 Norman, OK

  2. Motivation • High-resolution radar datasets (e.g., VORTEX-2) enable fine-scale supercell wind retrievals that are vital to illuminating supercell/tornado dynamics • Knowledge of error characteristics of different wind retrieval techniques is needed to: • Select the most accurate method for a given dataset • Determine how much confidence to place in the analyses • Design mobile radar deployment and scanning strategies that mitigate wind retrieval errors • Maximizing the scientific value of kinematical/dynamical retrievals from mobile radar datasets requires understanding of limitations of different methods under different scenarios

  3. Primary Questions • When dual-radar data are available, does EnKF produce better wind estimates than DDA? • Or do model/IC/EnKF errors obviate advantage of constraining analysis with NWP model? • When only single-radar data are available, can EnKF produce DDA-quality analyses? • Or is the solution too underdetermined? • Does EnKF produce reliable wind estimates outside radar data region?

  4. Previous Work • DDA vs. EnKF comparisons have been made for real tornadic supercells: • Dowell et al. (2004) - 88D; Arcadia, OK, 1981 • Marquis et al. (2010) – DOW; Goshen Co., 2009 • Marquis et al. (2011) – DOW; Argonia, KS, 2001 • Low-level wind/ζanalyses from 1- or 2-radar EnKF qualitatively similar to DDA • True wind field unknown, so difficult to evaluate EnKF vs. DDA performance

  5. OSS Experiments • Simulate supercell using NSSL Collaborative Model for Multiscale Atmospheric Simulation (NCOMMAS) • Synthesize mobile radar Vobs, Zobs • Input observations to: • NCOMMAS EnKF system • 3D-VAR DDA technique • Compare wind analyses from the two methods • 2 vs. 1 radar • good vs. poor radar positioning • perfect (2-moment) vs. imperfect (LFO) microphysics • various scanning strategies

  6. Radar Emulation • Compute Vobs, Zobs on radar grids using realistic beam weighting (Wood et al. 2009) • ΔR = 150 m, ΔΦ = 1° • Vobscomputed only where Zobs> 5 dBZ • Random errors: σV = 2 m/s, σZ = 2 dBZ

  7. Supercell simulation • NCOMMAS: nonhydrostatic, compressible numerical cloud model • Roughly 100 × 100 × 20 km domain; Δ = 200 m • ZVDH microphysics (Mansell et al. 2010) dBZ, ζ dBZ, w t = 60 min, z = 1 km θ'

  8. EnKF configuration • Ensemble square root filter • 40 members • Δ = 600 m (model error) • Data interpolated to 2-km grid on conical scan surfaces, then advection-corrected (1-D) • Data assimilated every 2 min from t = 30 min to t= 84 min • Covariance localization: 6 km (horiz), 3 km (vert) • Filter uses σV =2 m/s, σZ = 5 dBZ

  9. EnKF configuration (cont.) • Ensemble initialization: • sounding u, v perturbed at top/bottom • randomized thermal bubbles • Ensemble spread maintenance: • additive noise (Dowell and Wicker 2009) • bubbles added where Zobs - Zens > 30 dBZ

  10. DDA Technique • 3D-VAR method (Shapiro et al. 2009; Potvin et al. 2011) • Data, mass conservation, smoothness constraints weakly satisfied • Impermeability exactly satisfied at surface • Δ = 600 m; domain = 40 × 40 × 13.8 km (matches verification domain) • Same advection correction as in EnKF • DDA errors for this case examined in Potvin et al.(JTECH, in review)

  11. Experiment Domains t= 84 min verification domain (moves with storm) t= 30 min “poor radar placement” experiments default experiments

  12. Reflectivity Assimilation • Only helped single-radar EnKF; only Vobsassimilated in remaining experiments • Improvement diminished by • model errors • non-Gaussian-distributed errors

  13. Considerations • Representativeness of OSSE model/observational errors imprecisely known • Wind analysis errors sensitive to tunable parameters and methodological choices • Each EnKF mean analysis is only a single realization of the distribution of possible analyses • Need to treat small error differences with caution

  14. Deep VCPverified where data available from both radars t = 60 min t = 36 min t = 84 min u w

  15. wat t = 60 min, z = 1.2 km 2-radar EnKF-LFO DDA TRUTH 1-radar EnKF-LFO 2-radar EnKF 1-radar EnKF 1-radar EnKF

  16. Max updraft • Except for 1-radar EnKF-LFO, EnKF generally damps main updraft less than DDA at middle/upper levels t = 36 min t = 60 min t = 84 min

  17. Non-simultaneity errors • EnKF displacement errors and (at later times) pattern errors are much smaller than DDA errors aloft v at t = 60 min, z = 9.6 km TRUTH DDA 2-radar EnKF 1-radar EnKF-LFO

  18. Deep VCP t = 84 min t = 36 min u w

  19. t = 36 min t = 84 min u w

  20. t = 36 min t = 84 min u w

  21. Impact of dual-Doppler ceiling DDA DEEP DDA SHALLOW t = 36 min, z = 5.4 km TRUTH 2-radar EnKF SHALLOW 2-radar EnKF DEEP

  22. Impact of data edge (w)t = 36 min, z = 0.6 km

  23. Impact of data edge (v)t = 36 min, z = 0.6 km

  24. Impact of poor cross-beam angle T=84 min T=30 min 30° lobe

  25. Impact of poor cross-beam angle Poor placement Good placement • 3-D winds much less constrained by Vobsalone • EnKF far less degraded than DDA • EnKF better than DDA even at low levels later in DA period w w 36 min w w 84 min

  26. Impact of sounding error 2 km 3 km 1 km

  27. Impact of sounding error t = 36 min t = 84 min u w

  28. NO sounding error t = 36 min t = 84 min u w

  29. Conclusions: 2-radar case • When might EnKF help? • Poor radar positioning • Rapid flow advection/evolution • Outside dual-Doppler region • When might EnKF (mildly) hurt? • At lowest levels • Very early in DA period (e.g., storm maturation) • Errors in microphysics and/or low-level wind profile had small impact

  30. Conclusions: 1-radar case • 1-radar EnKF analyses much more sensitive to microphysics/sounding errors • Thus, conclusions more tentative • 1-radar EnKF may produce DDA-quality (or better) analyses: • At upper levels if flow rapidly translating/evolving • At middle & upper levels later in DA period • At all levels later in DA period if cross-beam angle is poor and model errors small • Analyses outside data region probably not good enough for some applications

  31. Future Work • Compare trajectories, vorticity & vorticity tendency analyses • 29 May 2004 Geary, OK case • VORTEX-2 case(s) • Longer term: OSSEs to compare storm-scale analyses and forecasts (WoF)obtained from different schemes (e.g., 3DVAR, EnKF, hybrid)

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