113 The Analyses and Prediction of a Tornadic Supercell Storm using a Combined 3DVAR-EnKF Data Assimilation Approach

Thursday, 29 September 2011
Grand Ballroom (William Penn Hotel)
Nusrat Yussouf, CIMMS/Univ. of Oklahoma/NSSL, Norman, OK; and J. Gao, D. M. Wheatley, and D. J. Stensrud

The advantages and disadvantages of variational method and EnKF method are widely discussed in recent studies (Lorenc, 2003; Kalnay et al. 2007). As demonstrated by Caya et al. (2005), the EnKF is better than variational method after the initial several data assimilation cycles, presumably because of better handling of flow-dependent background error covariance. However, during the initial time of storm-development, the variational method is better because the EnKF method needs time to spin-up storm cells. To investigate further, a storm-scale ensemble is incorporated within a complex 36-member WRF based mesoscale ensemble system and evaluated using 8 May 2003 Oklahoma City supercell tornadic event. Routinely available observations from surface stations, marine, radiosondes and aircrafts are assimilated into the mesoscale ensemble on an hourly basis using the Data Assimilation Research Testbed (DART) ensemble Kalman filter (EnKF) system and provide the initial and boundary conditions for the storm-scale ensemble. Radial velocity and reflectivity observations from doppler radar are assimilated into the storm-scale ensemble at 5-min interval for less than an hour. To examine the feasibility of obtaining an improved first guess field and background error covariance for EnKF data assimilation, experiments will be conducted in which a three dimensional variational approach (3DVAR) is applied at the beginning of the EnKF data assimilation cycle. The results from this combined 3DVAR-EnKF analysis will be compared to that from pure EnKF analysis. Preliminary results will be presented at the conference.


Caya, C, J. Sun, and C. Snyder, 2005: A Comparison between the 4DVAR and the Ensemble Kalman Filter Techniques for Radar Data Assimilation, Mon. Wea. Rev., 133, 3081-3094. Kalnay, E., H. Li, T. Miyoshi, S.-C. Yang and J. Ballabrera, 2007: 4D-Var or Ensemble Kalman Filter? Tellus A, 59, 758–773. Lorenc, A. C., 2003: The potential of the ensemble Kalman filter for NWP – a comparison with 4D-Var. Quart. J. Roy. Meteor. Soc., 129, 3183-3203.

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