A typical storm-scale ensemble Kalman filter (EnKF) analysis/forecast system is shown to introduce imbalances into the ensemble posteriors that generate acoustic waves in subsequent integrations. When the EnKF is used to research storm-scale dynamics, the resulting spurious pressure oscillations are large enough to impact investigation of processes driven by nonhydrostatic pressure gradient forces. Fortunately, thermodynamic retrieval techniques traditionally applied to dual-Doppler wind analyses can be adapted to diagnose the balanced portion of an EnKF pressure analysis, thereby eliminating the fast-mode pressure oscillations. The efficacy of this approach is demonstrated using a high-resolution supercell thunderstorm simulation as well as EnKF analyses of a simulated and a real supercell. 1. Introduction The EnKF [1] has become a popular and valuable tool for storm-scale research [2–12]. Particularly when dual-Doppler radar data are available, EnKF data assimilation can provide reliable analyses of wind and, to a lesser degree, temperature and microphysical variables in convective storms. EnKF analyses of pressure, on the other hand, are subject to severe errors, at least with some compressible model configurations (the first tests of the EnKF with a compressible model were performed by Tong and Xue [5]). This problem is illustrated in Figure 1 using output from the National Severe Storms Laboratory Collaborative Model for Multiscale Atmospheric Simulation (NCOMMAS; [13, 14]) ensemble square root filter [15]. Similar behavior occurs using the Data Assimilation Research Testbed [16] EnKF with the Advanced Research Weather Research and Forecasting (WRF-ARW; [17]) model (James Marquis and Thomas Jones, personal communication 2013). The pressure analysis errors severely impede investigation of critical storm processes that are, in part, driven by dynamic pressure gradient forces, including supercell occlusion downdrafts [18], lifting of negatively buoyant air [19], supercell propagation [20], the descending rear inflow and ascending front-to-rear flow in mesoscale convective systems [21], and possibly descending reflectivity cores [22]. Figure 1: True (left column) and EnKF mean posterior (right column) (shading; hPa) and radar reflectivity factor (contoured at 10, 30, and 50？dBZ) at ？km. Fields are valid after (top row) zero, (middle row) one, and (bottom row) fifteen 2？min forecast cycles. The true were filtered and averaged as in Potvin et al. [ 23] to permit more direct comparisons with the (coarser) EnKF . The pressure oscillations shown in Figure 1
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