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 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

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?

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/ ζ analyses from 1- or 2-radar EnKF qualitatively similar to DDA True wind field unknown, so difficult to evaluate EnKF vs. DDA performance

OSS Experiments Simulate supercell using NSSL Collaborative Model for Multiscale Atmospheric Simulation (NCOMMAS) Synthesize mobile radar V obs, Z obs 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

Radar Emulation Compute V obs, Z obs on radar grids using realistic beam weighting (Wood et al. 2009) ΔR = 150 m, ΔΦ = 1° V obs computed only where Z obs > 5 dBZ Random errors: σ V = 2 m/s, σ Z = 2 dBZ VCPΔT (min)Max θΔθΔθ DEEP °1° - 3° SHALLOW °1° RAPID °1°

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

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 σ V = 2 m/s, σ Z = 5 dBZ

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 Z obs - Z ens > 30 dBZ

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)

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

Reflectivity Assimilation Only helped single- radar EnKF; only V obs assimilated in remaining experiments Improvement diminished by – model errors – non-Gaussian- distributed errors

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

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

w at t = 60 min, z = 1.2 km TRUTH DDA 1-radar EnKF 2-radar EnKF-LFO 2-radar EnKF 1-radar EnKF 1-radar EnKF-LFO

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

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 TRUTHDDA2-radar EnKF 1-radar EnKF-LFO

Deep VCP t = 36 min t = 84 min u w VCPΔT (min)Max θΔθΔθ DEEP °1° - 3° SHALLOW °1° RAPID °1°

t = 36 mint = 84 min u w VCPΔT (min)Max θΔθΔθ DEEP °1° - 3° SHALLOW °1° RAPID °1°

t = 36 mint = 84 min u w VCPΔT (min)Max θΔθΔθ DEEP °1° - 3° SHALLOW °1° RAPID °1°

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

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=30 min T=84 min 30° lobe

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

Impact of sounding error 1 km 2 km 3 km

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

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

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

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

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)