Zusammenfassung
In this study, I test the hypothesis that sufficient information can be extracted from a time-series of single-Doppler observations to obtain a dynamically consistent analysis of a deep-convective storm. This is accomplished by developing a single-Doppler analysis/retrieval procedure and applying it to a real-data case of an Oklahoma supercell thunderstorm. Recognizing that the most sophisticated retrieval/analysis techiuques (such as 4DVAR) are not practicable at this time, 1 have sought to incorporate key attributes of these techniques (such as the use of dynamic constraints and time-tendency information) into a simpler, more efficient method. The new method features the sequential application of a single-Doppler velocity retrieval (SDVR) and a Gal-Chen type thermodynamic retrieval. By utilizing simpler subsets of the full dynamic equations in this manner, some of the difficulties associated with full-model adjoints (solution non-uniqueness and extreme computational expense) can be circumvented. The two main sequential retrieval components are augmented by a wind blending procedure, a variational wind adjustment, and a simple method for specifying moisture fields. For the SDVR, Shapiro's two-scalar technique is adapted for use on deep convective storms by applying it in a moving reference frame. Two possible moving reference frames (both calculated 6om the radar data) are tested: one that follows the storm motion and one that follows the mean wind. Verification of the retrieved wind field
against a corresponding dual-Doppler analysis indicates significantly improved results for both moving reference frames compared with the fixed frame.
The SDVR skill scores are especially good at low-levels; however, most of the retrieved azimuthal velocity is associated with polar vorticity. Missing from the retrieved fields (compared to the dual-Doppler analysis) is the low-level azimuthal convergence. Consistent with this, the SDVR only retrieves about half of the 55 m s \grq updraft present in the dual-Doppler analysis. Following the velocity blending and adjustment steps, the thermodynamic retrieval is applied to both the single- and dual-Doppler-derived wind fields, yielding realisuc looking and qualitatively similar perturbation potential temperature fields. An analysis of the terms in the vertical momentum equation is performed to document their contribution to the retrieved potential temperature. Comparisons with predictions from simple linear theory indicate a good agreement. The perturbation pressure and vorticity fields are also found to be in reasonable agreement with linear theory predictions for deep-convection in sheared flow. Following the moisture specification step, short-range numerical predictions are initiated from both the single- and dual-Doppler-derived fields. In both cases, the general storm evolution and deviant storm motion are reasonably well predicted for a period of about 35 minutes. Sensitivity experiments indicate that the predicted storm evolution is strongly dependent on the initial moisture fields, with rainwater exerting the greatest influence.
Following the moisture specification step, short-range numerical predictions are initiated from both the single- and dual-Doppler-derived fields. In both cases, the general storm evolution and deviant storm motion are reasonably well predicted for a period of about 35 minutes. Sensitivity experiments indicate that the predicted storm evolution is strongly dependent on the initial moisture fields, with rainwater exerting the greatest influence. Based on the results from this study, I conclude that for this particular case the timeseries of single-Doppler observations contain sufficient information to obtain a dynamically consistent analysis suitable for initializing a storm-scale numerical prediction model. Furthermore, the Arcadia storm appears to exhibit at least some degree of predictability. Based on these encouraging findings, continued development of this and other single-Doppler retrieval techniques is recommended.
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