Extended Abstract (256K)13A.3
Assimilation of polarimetric radar data using ensemble Kalman filter:Experiments with simulated data
Youngsun Jung, University of Oklahoma, Norman, OK; and M. Xue and J. M. Straka
Various combinations of polarimetric radar moments have been shown to be very valuable for quantitative precipitation estimation and for hydrometeor type classification within thunderstorms (Straka et al. 2000), including stratiform regions with mesoscale convective systems. Polarimetric radars also should be very helpful for storm-scale model initialization and data assimilation. There is a plan for polarimetric upgrade to the national WSR-88D radar network. This upgrade should start in the next several years.
Initialization of convective storms using radar data within a numerical model has enjoyed reasonable success in recent years, using methods such as the complex cloud analysis, 4DVAR and more recently the ensemble Kalman filter (EnKF). The recent study of Tong and Xue (2005) shows that the cloud fields, including microphysical species associated with a 3-ice microphysics scheme, can be accurately retrieved using the EnKF method from simulated radial velocity and reflectivity data. It is expected that the results can be further improved when additional polarimetric moments are assimilated such as Zdr, and Kdp and possibly some other moments at the least for hydrometeor classification.
In this study, we examine the impact of assimilating differential reflectivity (Zdr), in addition to radial velocity (Vr) and regular reflectivity (Z), on the analysis and prediction of a simulated thunderstorm. The forward observation operators for the polarimetric radar measurements that are consistent with the microphysics schemes with varying degrees of assumptions are first developed and their sensitivities to the assumptions are examined. The set of operators consistent with the current 3-ice microphysics scheme in the ARPS model is then used in the EnKF data assimilation. Future work will include the assimilation of additional parameters such as specific differential phase. A much more sophisticated microphysics scheme is being ported into ARPS as well.
Reference cited: Tong, M. and M. Xue, 2005: Ensemble Kalman filter assimilation of Doppler radar data with a compressible nonhydrostatic model: OSS Experiments. Mon. Wea. Rev., In press.
Straka, J.M., D. S. Zrnic, and A. V. Ryzhkov, 2000; Bulk hydrometeor classification and quantification using multi-parameter radar data. A synthesis. J. Applied Meteor., 40, 1341-1372.
Session 13A, Data Assimilation I
Thursday, 4 August 2005, 1:30 PM-3:00 PM, Ambassador Ballroom
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