1B.8
Assimilation of simulated polarimetric radar data using ensemble Kalman filter: Observation operators and data impact
Youngsun Jung, University of Oklahoma, Norman, OK; and M. Xue, G. Zhang, and J. M. Straka
An existing data assimilation system based on the ensemble square-root Kalman filter is extended to include the additional capability of assimilating some types of polarimetric radar data (PRD). To do so, observation operators that link the model state variables associated with a single-moment ice microphysics schemes are developed based on the T-matrix method and the Rayleigh and the Mie scattering model. Various PRD variables are represented by the Drop Size Distribution (DSD) parameters and density based scattering calculation in the forward operators. The presence of wet snow/hail in the melting layer is accounted for using a new, relatively simple melting model that defines the water fraction in the melting snow/hail particles. The effect of varying density due to the melting snow/hail is also included.
The analysis errors are reduced more quickly when polarimetric data are assimilated in addition to regular reflectivity whilst the improvement near the end of the 80 minute-long assimilation window of 16 analysis cycles becomes rather small. During the earlier cycles, improvement is found at all model levels even though the polarimetric data, after their thresholds are applied, are mostly limited to the lower levels, indicating the propagation of the data influence through the prediction model. The impact of polarimetric data is also expected to be larger when they are used to retrieve drop size distribution parameters or if a multi-moment microphysics scheme is used.
.Session 1B, Radar Data Assimilation
Tuesday, 26 June 2007, 8:00 AM-10:00 AM, Summit B
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