Evaluation of the self-consistency principle for calibration of the CASA radar network using properties of the observed medium

Abstract

The Center for Collaborative and Adaptive Sensing of the Atmosphere (CASA) has deployed a Distributive, Adaptive and Collaborative Sensing (DCAS) network of four radars in central Oklahoma. The radars operate at the X-band frequency and are capable of polarimetric and Doppler measurements. The radar network is being evaluated for Quantitative Precipitation Estimation (QPE). QPE algorithms based on radar power measurements (e.g. Z H and Z DR ) require bias correction. The polarimetric selfconsistency principle is applied to the CASA radar data to estimate any bias in Z H . Results show a Z H calibration accuracy of 0.6 dBZ or less for two the analyzed events. Z H bias estimates from the self-consistency principle in rainfall are compared and validated with Z H bias estimated from the comparison of the X-band and the S-band radars' data.Comparison of the two approaches shows a difference in the Z H bias estimation of 0.61 dBZ or less and validates the use of the self-consistency principle in rainfall for the absolute radar calibration of the CASA radars.