Comparisons of kinematical retrievals within a simulated supercell

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

Motivation • High-resolution radar datasets (e. g. , VORTEX-2) enable finescale 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

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

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

OSS Experiments • Simulate supercell using NSSL Collaborative Model for Multiscale Atmospheric Simulation (NCOMMAS) • Synthesize mobile radar Vobs, Zobs • Input observations to: – NCOMMAS En. KF system – 3 D-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

Radar Emulation • Compute Vobs, Zobs on radar grids using realistic beam weighting (Wood et al. 2009) • ΔR = 150 m, ΔΦ = 1° • Vobs computed only where Zobs > 5 d. BZ • Random errors: σV = 2 m/s, σZ = 2 d. BZ VCP ΔT (min) Max θ Δθ DEEP 2. 5 33. 5° 1° - 3° SHALLOW 1. 5 12. 5° 1° RAPID 0. 5 12. 5° 1°

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

En. KF 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 d. BZ • •

En. KF 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 d. BZ

DDA Technique • 3 D-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 × 13. 8 km (matches verification domain) • Same advection correction as in En. KF • DDA errors for this case examined in Potvin et al. (JTECH, in review)

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

Reflectivity Assimilation • Only helped singleradar En. KF; only Vobs assimilated in remaining experiments • Improvement diminished by – model errors – non-Gaussiandistributed errors

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

Deep VCP verified where data available from both radars t = 36 min u w t = 60 min t = 84 min

w at t = 60 min, z = 1. 2 km TRUTH 2 -radar En. KF DDA 2 -radar En. KF-LFO 1 -radar En. KF-LFO

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

Non-simultaneity errors • En. KF 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 En. KF 1 -radar En. KF-LFO

VCP DEEP SHALLOW t = 36 min w Max θ 2. 5 33. 5° Deep VCP RAPID u ΔT (min) Δθ 1° - 3° 1. 5 12. 5° 1° 0. 5 12. 5° 1° t = 84 min

VCP ΔT (min) Max θ Δθ DEEP 2. 5 33. 5° 1° - 3° SHALLOW 1. 5 12. 5° 1° RAPID 0. 5 12. 5° 1° t = 36 min u w t = 84 min

VCP ΔT (min) Max θ Δθ DEEP 2. 5 33. 5° 1° - 3° SHALLOW 1. 5 12. 5° 1° RAPID 0. 5 12. 5° 1° t = 36 min u w t = 84 min

Impact of dual-Doppler ceiling t = 36 min, z = 5. 4 km DDA DEEP DDA SHALLOW TRUTH 2 -radar En. KF DEEP 2 -radar En. KF SHALLOW

Impact of data edge (w) t = 36 min, z = 0. 6 km

Impact of data edge (v) t = 36 min, z = 0. 6 km

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

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

Impact of sounding error 2 km 3 km 1 km

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

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

Conclusions: 2 -radar case • When might En. KF help? – Poor radar positioning – Rapid flow advection/evolution – Outside dual-Doppler region • When might En. KF (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

Conclusions: 1 -radar case • 1 -radar En. KF analyses much more sensitive to microphysics/sounding errors • Thus, conclusions more tentative • 1 -radar En. KF 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

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 (Wo. F) obtained from different schemes (e. g. , 3 DVAR, En. KF, hybrid)