Extended Abstract (344K)10.7
Impacts of Initial Analyses and Observations on the Convective-Scale Data Assimilation with an Ensemble-Kalman Filter
Fuqing Zhang, Texas A&M University, College Station, TX; and C. Snyder and J. Sun
The ensemble Kalman filter (EnKF) uses an ensemble of short-range forecasts to estimate the flow-dependent background error covariances required in data assimilation. This method has attracted considerable recent interest in the fields of atmospheric and oceanic sciences and hydrology. In perfect-model experiments using simulated observations of radial velocity from a supercell storm, Snyder and Zhang (2002) demonstrated the feasibility of the EnKF for convective-scale data assimilation. The present study seeks to explore further the potential and behavior of the EnKF at convective scales by considering more realistic initial analyses and variations in the availability and quality of the radar observations. We find that EnKF assimilations using radial-velocity observations (every 5 minutes and where reflectivity is greater than 12 dBZ) and 20 ensemble members prove to be successful in most realistic observational scenarios for supercell thunderstorms, although because of our assumption of a perfect forecast model these are undoubtedly upper bounds on the performance to be expected with real observations and an imperfect model. Even though the filter converges toward the truth simulation faster from a better initial estimate, an experiment with the initial estimate of the supercell displaced by 10 km still yields an accurate estimate of the storm for both observed and unobserved variables within an hour. Similarly, radial-velocity observations below 2km are certainly beneficial to capturing the storm (especially the detailed cold pool structure) but in their absence the assimilation scheme can still achieve a comparably accurate estimate of the state of the storm given a slightly longer assimilation period. An experiment with radar observations only above 4 km fails to assimilate the storm properly but, with the addition of a dense network of wind and temperature observations at the surface, the EnKF can again provide an accurate estimate of the storm. We can also successfully assimilate the storm in the case of radar observations only below 4 km (such as those from the ground-based mobile radars).
Session 10, Data Assimilation I
Thursday, 15 August 2002, 8:00 AM-9:58 AM
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