Prediction of Tornado-Like Vortex (TLV) Embedded in the 8 May 2003 Oklahoma City Tornadic Supercell Initialized from the Subkilometer Grid Spacing Analysis Produced by the Dual-Resolution GSI-Based EnVar Data Assimilation System

Abstract

Explicit forecasts of a tornado-like vortex (TLV) require subkilometer grid spacing because of their small size. Most previous TLV prediction studies started from interpolated kilometer grid spacing initial conditions (ICs) rather than subkilometer grid spacing ICs. The tornadoes embedded in the 8 May 2003 Oklahoma City tornadic supercell are used to understand the impact of IC resolution on TLV predictions. Two ICs at 500-m and 2-km grid spacings are, respectively, produced through an efficient dual-resolution (DR) and a single-coarse-resolution (SCR) EnVar ingesting a 2-km ensemble. Both experiments launch 1-h forecasts at 500-m grid spacing. Diagnostics of data assimilation (DA) cycling reveal DR produces stronger and broader rear-flank cold pools, more intense downdrafts and updrafts with finer scales, and more hydrometeors at high altitudes through accumulated differences between two DA algorithms. Relative differences in DR, compared to SCR, include the integration from higher-resolution analyses, the update for higher-resolution backgrounds, and the propagation of ensemble perturbations along higher-resolution model trajectory. Predictions for storm morphology and cold pools are more realistic in DR than in SCR. The DR-TLV tracks match better with the observed tornado tracks than SCR-TLV in timing of intensity variation, and in duration. Additional experiments suggest 1) the analyzed kinematic variables strongly influence timing of intensity variation through affecting both low-level rear-flank outflow and midlevel updraft; 2) potential temperature analysis by DR extends the second track’s duration consistent with enhanced low-level stretching, delayed broadening large-scale downdraft, and (or) increased near-surface baroclinic vorticity supply; and 3) hydrometeor analyses have little impact on TLV predictions.

Rapid Update with EnVar Direct Radar Reflectivity Data Assimilation for the NOAA Regional Convection-Allowing NMMB Model over the CONUS: System Description and Initial Experiment Results

“…The reflectivity is quality-controlled within the MRMS system. The standard deviation of the reflectivity is assumed to be 5 dBZ, which is consistent with the previous storm-scale DA studies [22,23,49,50,62]. The observed reflectivity lower than 5 dBZ is reset to 0 dBZ [22,23,30,49,63].…”

“…The standard deviation of the reflectivity is assumed to be 5 dBZ, which is consistent with the previous storm-scale DA studies [22,23,49,50,62]. The observed reflectivity lower than 5 dBZ is reset to 0 dBZ [22,23,30,49,63]. These 0 dBZ observations are considered "no precipitation" observations and assimilated to suppress spurious convections [22,36,49].…”

“…Additionally, as suggested by the results in Section 4.1, a scale-dependent localization of ensemble covariances [75,76] is ideally preferred to estimate all scales by assimilating all available observations simultaneously. Furthermore, as the quality of ensemble-based BECs may be degraded by a zero or small variance of hydrometeor mixing ratios for radar DA, an additive noise inflation [22,23,36,49] and/or properly constructed convective-scale static BECs can efficiently address the under-dispersive ensemble spread in storm scales [82]. These approaches remain to be tested in the CONUS EnVar settings in the future.…”

“…Recently, a hybrid ensemble variational (EnVar) method has increasingly attracted attention with expectations of preserving benefits and eliminating limitations of the traditional variational system (Var, or its specific implementations 3DVar and 4DVar) and ensemble Kalman filter (EnKF) [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. Various hybrid EnVar methods, therefore, have been used for regional numerical weather prediction (NWP) applications [7,8,13,[15][16][17][18][19][20][21][22][23] and adopted in several operational regional modeling and prediction systems, e.g., the North American Mesoscale Forecast System (NAM [24]), the Rapid Refresh (RAP [25]), the High-Resolution Rapid Refresh (HRRR [26,27]), the NAM Rapid Refresh system (NAMRR [28]), and the operational Application of Research to Operations at Mesoscale (AROME)-France NWP system [29].…”

“…For example, Johnson et al [50] used the horizontal localization scales of 700 (20) km for the synopticmesoscale (storm-scale) observations, while Yussouf et al [49] applied the horizontal localization scales of 574 (12) km for the mesoscale (storm-scale) assimilation. In terms of the ensemble inflation method, Johnson et al [50] used a factor of 0.95 to inflate the posterior ensemble spread back to 95% of the prior ensemble spread in their nocturnal convection studies; however, a different factor of 0.9 was applied in [22,23,51] for the tornadic supercell studies. In this study, the impact of the localization and inflation configuration is further examined on ten retrospective cases featuring high-impact convective storms during the warm season of 2015-2016.…”