In this study we examine the utility of a 4DVar adjoint method for assimilating Doppler radar data of a supercell thunderstorm into a numerical model. The storm we have chosen for study is the 17 May 1981 Arcadia, Oklahoma tornadic supercell which was observed by two Doppler radars. Note that this is a joint study with that by Dowell et al. where the Ensemble Kalman Filter method is being applied to the same dataset.

The 4DVar adjoint method attempts to find the best fit of a numerical model solution to a time series of radar data, as well as a background field. The numerical model solves the anelastic equations of motion and uses a simple warm rain microphysical scheme. By running the numerical model forward and its adjoint backwards in time, a cost function, which measures the difference between the model solution and the data/background can be minimized. During this minimization, some of the unobserved fields such as crossbeam velocity, temperature and cloudwater can be retrieved in the initial condition. It has been found that the retrieval and minimization process can be improved by adding penalty terms (such as spatial/temporal smoothness constraints) to the cost function.

A number of 4DVar experiments have been performed on the Arcadia supercell using single Doppler information. We are currently examining these experiments for fit to the data, retrieval of unobserved fields and the skill of the subsequent forecasts. The sensitivity to a number of factors including assimilation time window, cycling procedure, data QC and microphysical parameters is being examined. We also plan to perform experiments using data from both Doppler radars. Finally, the results from the 4DVar assimilation experiments will be compared and contrasted with those produced by the Ensemble Kalman filter method.

Note that this paper is joint with a paper by Dowell et al. entitled "Wind and Thermodynamic Retrievals in a Supercell Thunderstorm, Ensemble Kalman Filter results."