An Extended Kalman Filter Framework for Polarimetric X-Band Weather Radar Data Processing

Abstract

he different quantities measured by dual-polarization radar systems are closely linked to each other. An extended Kalman filter framework is proposed in order to make use of constraints on individual radar observables that are induced by these relations. This new approach simultaneously estimates the specific differential phase on propagation Kdp, the attenuation-corrected reflectivity at horizontal polarization Zh, and the attenuation-corrected differential reflectivity Zdr, as well as the differential phase shift on backscatter δh?. In a simulation experiment it is found that Kdp and δh? can be retrieved with higher accuracy and spatial resolution than existing estimators that solely rely on a smoothed measurement of the differential phase shift ?dp. Attenuation-corrected Zh was retrieved with an accuracy similar to standard algorithms, but improvements were found for attenuation-corrected Zdr. In addition, the algorithm can be used for radar calibration by comparing the directly retrieved differential phase shift on propagation Φdp with the accumulated Kdp estimates. The extended Kalman filter estimation scheme was applied to data collected with an X-band polarimetric radar in the Swiss Alps in 2010. Radome attenuation appears to be significant (up to 5 dB) in moderate to intense rain events and hence needs to be corrected in order to have reliable quantitative precipitation estimates. Measurements corrected for radome and propagation attenuation were converted into rain-rate R with a newly developed relation between R, Kdp, and Zdr. The good agreement between rain-rate values inferred from ground observations and from the radar measurements confirms the reliability of the proposed radar processing technique.