Comparison of Single-Parameter and Multiparameter Ensembles for Assimilation of Radar Observations Using the Ensemble Kalman Filter

Abstract

bservational studies indicate that the densities and intercept parameters of hydrometeor distributions can vary widely among storms and even within a single storm. Therefore, assuming a fixed set of microphysical parameters within a given microphysics scheme can lead to significant errors in the forecasts of storms. To explore the impact of variations in microphysical parameters, Observing System Simulation Experiments are conducted based on both perfect- and imperfect-model assumptions. Two sets of ensembles are designed using either fixed or variable parameters within the same single-moment microphysics scheme. The synthetic radar observations of a splitting supercell thunderstorm are assimilated into the ensembles over a 30-min period using an ensemble Kalman filter data assimilation technique followed by 1-h ensemble forecasts. Results indicate that in the presence of a model error, a multiparameter ensemble with a combination of different hydrometeor density and intercept parameters leads to improved analyses and forecasts and better captures the truth within the forecast envelope compared to single-parameter ensemble experiments with a single, constant, inaccurate hydrometeor intercept and density parameters. This conclusion holds when examining the general storm structure, the intensity of midlevel rotation, surface cold pool strength, and the extreme values of the model fields that are most helpful in determining and identifying potential hazards. Under a perfect-model assumption, the single- and multiparameter ensembles perform similarly as model error does not play a role in these experiments. This study highlights the potential for using a variety of realistic microphysical parameters across the ensemble members in improving the analyses and very short-range forecasts of severe weather events.