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

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 self-consistency 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.