Basic Convective and Mesoscale Research
NSSL Project 1 – Severe Weather Warning Applications and Development
Funding Type: CIMMS Task II
Develop storm-scale data assimilation methods to be used for “warn on forecast” applications; examine performance of the ensemble Kalman filter for multiple convective modes and for assimilating multiple types of observations into numerical models; examine the sensitivity of the analyses to changes in the implementation of the EnKF algorithm for both practical considerations and to understand better how the assimilation procedure works on the storm scale with a complex convective situation.
This study builds on past work by CIMMS research scientists by presenting a successful application of the ensemble Kalman filter data-assimilation technique using real Doppler radar observations of a mixed mode convective event, which has yet to be presented in the literature. It is shown that the quality of the storm-scale analysis produced by the technique can be at least maintained, if not improved, by using a relatively small localization radius and by implementing a threshold correlation to limit small, presumably noisy, covariances from influencing the analysis. The smaller localization radius has the added benefit of lowering the computation cost, substantially in some cases. It is also shown that the analysis system can accurately reproduce the reflectivity and velocity structures of the observed storms, but the low-level thermodynamic structures are very sensitive to the design of the system.
This project is ongoing.
Coniglio, M. C., D. C. Dowell, and L. J. Wicker, 2007: Ensemble Kalman filter assimilation of Doppler radar data: Analyses of a developing MCS. 22nd Conf. on Weather Analysis and Forecasting/18th Conf. on Numerical Weather Prediction, Park City, UT, Amer. Meteor. Soc., 3B.3.