Convective Storms & NWP Seminar Series presents...
State Estimation of Convective Storms with Two-Moment Microphysics Scheme and Ensemble Kalman Filter: Experiments with Simulated Radar Data
Youngsun Jung
Center for Analysis and Prediction of Storms (CAPS)
The University of Oklahoma School of Meteorology
21 November 2008, 4:00 PM
National Weather Center, Room 5720
120 David L. Boren Blvd.
University of Oklahoma
Norman, OK
Directions to the NWC (.pdf, 60 kb)
Several past studies suggest that supercell thunderstorms simulated using a two- or higher-moment microphysics scheme is more realistic. We explore the capability of the EnKF system in estimating state variables, including both the water/ice mixing ratios (third moment of DSD) and the total number concentrations (zeroth moment of DSD), which are also state variables when using a two-moment (DM) microphysics scheme. Several sets of experiments are performed to test the performance of EnKF system in the presence of model errors in both forecast model and observation operator. The impact of PR data on an analysis employing a DM scheme is also investigated. The results show that these state variables can be accurately estimated using both Vr and ZH observations with a perfect prediction model. In this case, additional polarimetric variables have a small and generally positive impact on the quality of analysis, partly because the analysis obtained using Vr and ZH is already very good. Imperfect model experiments with the forecast model error and with/without observation operator error were also performed to test the filter performance. Two types of model errors were considered: microphysical parameterization error due to incorrectly assumed DSD shape and a misrepresentation of the scattering properties of hydrometeors. The results showed that model error can noticeably deteriorate the estimates of microphysical state variables. Perturbing the shape parameter α of microphysical DSDs and using them in different ensemble members was found to improve overall analysis; doing so increases the ensemble spread of the state variables directly related to those species.