Handout (604.6 kB)
Bias in the differential reflectivity (Zdr) may be introduced by 3 different elements: 1) differences in the transmitted power between the vertical and the horizontal channels, 2) differences in the receiver gain of each channel and 3) impact of the radome structure or near-range obstacles. This last element introduces and spatial variability in the Zdr bias and consequently azimuth- and elevation-dependent Zdr bias curves have to be generated. Such curves are obtained by observing the daily Zdr average of rainfall with a reflectivity between 20 and 22 dBZ, which, if the radar were well calibrated, should be 0.2 dB. This technique, along with the one using the Zdr sun signature and the one exploiting data collected at 90° (a tilt that is routinely revisited every 15'), have been operational for more than one year in the radar network and long-term statistics are now available which provide a robust overview of the stability and precision that can reasonably be achieved currently with the French polarimetric radars. In a word, with the current systems and calibration procedures, the Zdr stability is about +/- 0.3 dB, close to, but still below, the +/- 0.2 dB requirement.
Absolute calibration of horizontal reflectivity (Zh) is a much more complex issue. The technique currently under development is based on the self-consistency method described in Gourley et al. (2009). This method is based on the fact that rainfall is relatively well represented by two parameters and polarimetric radars provide more than two variables. Zh, Zdr and specific differential phase Kdp can thus be considered as redundant in rain. Knowing the value of two of them allows estimating the 3rd one. If Zdr is well calibrated (see previous paragraph), then its combination with the measured Zh leads to a Kdp, which after range integration, provides a theoretical value of the differential phase Φdp that can be compared with the observed Φdp. In case of mismatch, then all the steps are repeated after adding a bias on Zh. The final estimated bias on Zh is the one that leads to the best agreement with theoretical and measured Φdp.
In practice, there are several factors that affect the performance of such technique and therefore robust constraints on the data used must be applied. In particular, the self-consistency only holds for rainfall and therefore other types of echoes (such as hail or ground clutter) must be eliminated from the dataset, the measurements may be affected by attenuation (even more so at higher frequency bands) and there is an intrinsic variability due to noise. An extensive sensitivity study has been carried out to determine the optimal constraints that should be imposed on the data for its operational implementation. The method is currently being validated by 1) looking at the results in known partially blocked azimuth (assuming that Zdr is not impacted by partial beam blocking), 2) looking at the results in known wet-radome attenuation cases, 3) looking at cases of known changes in the radar receiving chain, 4) using the temporal series of reflectivity bias measurements with sun power measurements (which provide the bias introduced by the receiver). The impact of the miscalibration correction on the rainfall rate estimation is also being analysed.