DUAL-DOPPLER VS . ENKF WIND ANALYSES OF THE 29-30 MAY 2004 GEARY , OKLAHOMA , SUPERCELL

Dual-Doppler mobile radar datasets enable the creation of 3-D wind retrievals and subsequent analyses (e.g., of parcel trajectories) that are critical to advancing our understanding of supercell thunderstorms. Maximizing the scientific value of these datasets requires advances to our knowledge of the expected errors for different analysis methods under different observational scenarios. Toward that end, Potvin et al. (2012b) examined errors in 3DVAR dual-Doppler wind analyses (hereafter, simply “dual-Doppler analyses”, or DDAs) of a simulated supercell thunderstorm observed using storm-topping vs. shallow radar scanning strategies and optimal vs. narrow radar cross-beam angles. Using the same supercell simulation, Potvin and Wicker (2012; hereafter, PW12) examined errors in wind analyses obtained using ensemble Kalman filter (EnKF; Evensen 1994) radar data assimilation and compared them to DDA errors. Though the EnKF ideally improves wind analyses by optimally combining radar observations with short-term (for storm-scale applications, typically 2-5 min) numerical weather prediction (NWP) model forecasts, the results of PW12 suggest model errors and violations of the optimality conditions for the EnKF can produce low-level wind analyses inferior to those obtained from DDA if the radar cross-beam angles are sufficiently large. On the other hand, the study also identified several scenarios where the EnKF wind analysis may generally be superior to DDA, such as when the radar cross-beam angles are small or the wind field changes rapidly between successive volume scans. While assimilating dualrather than single-radar observations generally improved the EnKF wind retrievals and subsequent vorticity and trajectory analyses, all the single-radar wind analyses were qualitatively accurate once enough data had been assimilated, and in many instances were commensurate with the dual-radar analyses. However, substantially larger errors occurred in vorticity stretching fields and circulation time series computed from the single-radar wind analyses. The present study continues the exploration of dual-Doppler and EnKF wind analysis errors using real storm-scale radar observations of the 29 May 2004 Geary, Oklahoma, tornadic supercell. The observing system simulation experiment (OSSE) framework adopted in PW12 permitted rigorous evaluation and comparison of analysis errors. However, the realism of the errors in that study was uncertain given the artificiality of the errors input to the EnKF model and pseudo-observations. For example, observation errors were assumed to be normally distributed with known, spatiotemporally constant standard deviations; only a subset of the sources of typically large model error was simulated; and the representativeness of the model errors that were simulated was questionable. The converse tradeoff holds in the present, real-data, study: the model and observational errors pose a more realistic, and presumably greater, challenge to the EnKF, but analysis verification is limited by uncertainty in the true wind field. We have chosen to investigate the problem within both OSSE and (in the present study) real-data frameworks to draw more confident conclusions about the characteristics of storm-scale dual-Doppler and EnKF wind analysis errors. The following hypotheses from PW12 are further tested herein: (1) Assimilating radar reflectivity factor (hereafter, “reflectivity”) observations in addition to Doppler velocity observations generally degrades one-radar EnKF wind (hereafter, “1-EnKF”) analyses. (2) Two-radar EnKF wind (“2-EnKF”) analyses, but not 1-EnKF analyses, are relatively insensitive to errors in the initialization sounding and (if reflectivity observations are not assimilated) microphysical parameterization (MP) scheme. (3) Two-radar EnKF analyses generally do not substantially improve upon DDAs near the ground when radar data coverage and radar cross-beam angles are favorable. (4) Once enough data have been assimilated, 1EnKF wind analyses are qualitatively nearly as accurate as 2-EnKF wind analyses, but locally large wind errors occur in the 1-radar case that can substantially degrade subsequent analyses that use derivatives of the wind field (e.g., circulation time series). The uncertainty in the true wind field fundamentally limits verification of our analyses. Fortunately, this does not hinder confirmation of hypotheses 2 and 3 (above) since they are concerned not with analysis errors, but with the magnitudes of differences between analyses obtained using different methods. Imprecise knowledge of the true wind field poses a greater challenge to the testing of hypotheses 1 and 4 since they make claims ________________________________________