Initialization and Numerical Forecasting of a Supercell Storm Observed during STEPS

Abstract

The feasibility of initializing a numerical cloud model with single-Doppler observations and predicting the evolution of thunderstorms has been tested using an observed case of a supercell storm during the Severe Thunderstorm Electrification and Precipitation Study (STEPS). Single-Doppler observations from the Weather Surveillance Radar-1988 Doppler (WSR-88D) at Goodland, Kansas, are assimilated into a cloud-scale numerical model using a four-dimensional variational data assimilation (4DVAR) scheme. A number of assimilation and short-range numerical prediction experiments are conducted. Both the assimilation and prediction results are compared with those of a dual-Doppler synthesis. The prediction results are also verified with reflectivity observations. It is shown that the analysis of the wind field captures the major structure of the storm as revealed by the dual-Doppler synthesis. Thermodynamical and microphysical features retrieved through the dynamical model show consistency with expectations for a deep convective storm. The predicted storm evolution represented by the reflectivity field correlates well with the observations for a 2-h prediction period. The relative importance of the initial fields on the subsequent prediction of the storm evolution is examined by alternately removing the perturbation in each of the initial fields. It is shown that the prediction is most sensitive to the initialization of wind, water vapor, and temperature perturbations. A number of sensitivity experiments for initialization are conducted to show how the initial analysis depends on the application of a cycling procedure, the weights of the smoothness constraint, and the relative importance between the radial velocity and the reflectivity observations. It is found that the application of the cycling procedure improves the analysis and the subsequent forecast. Greater smoothness coefficients of the penalty term in the cost function result in a larger rms difference in the wind analysis, but help spread the information out and improve the forecast slightly. The radial velocity observations play a more important role than the reflectivity in terms of the wind analysis and the subsequent precipitation forecast.