9B.1
How predictable are severe convective storms? An initial study using EnKF data assimilation
PAPER WITHDRAWN
Louis J. Wicker, NOAA/NSSL, Norman, Oklahoma; and E. R. Mansell
The past decade or more has seen considerable progress on the use of 3DVAR (Hu et al. 2006; Xue et al. 2006; 2007), 4DVAR (Crook and Sun 2004), and ensemble Kalman methodologies (Dowell et al. 2004; 2007) for data assimilation on the convective scale. Lilly's pioneering vision from the early 1990's (Lilly 1990) for storm-scale prediction has moved forward such that the prediction of these storms in a operational setting is now suggested as a real possibility by various investigators perhaps ten to fifteen years from now.
While a number of examples exist in the literature demonstrating storm-scale analysis and “prediction” (see above), almost no recent research has focused on what the limits of the predictability might be for convection. In part this is due to the lack of theoretical understanding regarding the flow dynamics, beyond Lilly's hypotheses regarding supercell longevity (and presumed longer predictability) arising from the helical flow, of the relative roles of the near-storm environment, internal dynamics, surface fluxes, and turbulence in quasi-two dimensional squall lines and three-dimensional supercell storms. Since storm-scale forecasts are now being reported in the literature, it would seem appropriate to attempt to quantify through numerical experiments the inherent limits of predictability within such storms.
This study will examine predictability in two types of convective systems: supercells and derecho squall lines. Both convective storm-type are associated with high-impact weather events; tornadoes and severe straight-line winds. A series of observing system experiments will be designed using an ensemble Kalman square-root filter as the data assimilation method. The OSE study will initially examine the predictability using a perfect model assumption. This study will study the analysis and forecast accuracy and ensemble spread of the convective storms. These will be used as a proxy for “predictability” in the hope of furthering our understanding about convective scale prediction. Experiments will be conducted to determine the sensitivity of the analyses/forecasts to the: a) frequency of the of data assimilation, b) length of assimilation window, and c) time when data is assimilated (evolution dependence). Many of these parameters need to be understood when we consider the new data platforms to be coming online in the next decade (dual-polarization radar and phased array radar). Initial results from imperfect model experiments where the model error is in part represented by errors in the microphysical parameterization will also be presented.
References
Crook, N. A. and J. Sun, 2004: Assimilating Radar, Surface, and Profiler Data for the Sydney 2000 Forecast Demonstration Project. J. Atmos and Ocea. Tech. 19, 888–898.
Dowell, D. C., L. J. Wicker, E. R. Mansell 2007: High-resolution analyses of the 8 May 2003 Oklahoma City storm. Part II: EnKF data assimilation and forecast experiments. To be submitted to Mon. Wea. Rev.
Dowell, D. C., F. Zhang, L. J. Wicker, C. Snyder, and N. A. Crook, 2004: Wind and thermodynamic retrievals in the 17 May 1981 Arcadia, Oklahoma supercell: Ensemble Kalman filter experiments. Mon. Wea. Rev., 132, 1982-2005.
Hu, M., M. Xue, J. Gao and K. Brewster, 2006: 3DVAR and cloud analysis with WSR-88D Level-II data for the prediction of the Fort Worth tornadic thunderstorms. Part II: Impact of radial velocity analysis via 3DVAR. Mon. Wea. Rev., 134, 699-721.
Hu, M. and M. Xue, 2007: Impact of configurations of rapid intermittent assimilation of WSR-88D radar data for the 8 May 2003 Oklahoma City tornadic thunderstorm case. Mon. Wea. Rev., 135, 507–525.
Lilly, D. K. 1990: Numerical prediction of thunderstorms - has its time come? Quart. J. Roy. Meteor. Soc. 116, 779-798.
Xue, M., M. Hu, and M. Tong, 2006: Assimilation of radar data and short-range prediction of thunderstorms using 3DVAR, cloud analysis and ensemble Kalman filter methods. Abstract submitted to the 12th Conf. Aviation Range Aerospace Meteor., 2006, Atlanta, Georgia, Amer. Meteor. Soc.
Session 9B, Numerical Prediction of Convective Systems
Thursday, 28 June 2007, 10:30 AM-12:00 PM, Summit B
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