The presence of ground clutter, especially anomalous propagation (AP) ground clutter, presents problems for radar precipitation estimation and model initialization. Clutter filters are able to effectively cancel AP clutter in most situations. However, it is well known that clutter filters introduce a negative reflectivity bias in regions near zero velocity. This bias can have a significant impact on radar precipitation estimates. Much effort has recently been made to alleviate clutter without biasing reflectivity. Towards that end, numerous methods for identifying AP clutter have been developed. These methods will allow the clutter filters to be used only in the areas which contain clutter. However, it is often necessary to apply the clutter filters to weather echoes with near zero velocity. In this case, a reflectivity compensation method may be used to correct for the negative bias introduced by the clutter filter. The simple Gaussian correction model (SGCM) was developed at NCAR by Frank Pratte for this purpose.
In this paper we present an overview of the SGCM method and results of tests on simulated and real radar data. For the simulated data, the Doppler spectrum was approximated by a Gaussian distribution. For the tests with real data, we used archive I data recorded at the Memphis WSR-88D radar using NCAR's A1DA data recorder. In this case, auto-correlation calculations were performed on the in-phase and quadrature data to obtain estimates of the first three moments for both the filtered and unfiltered data. Next the simple Gaussian correction model was applied to obtain the corrected reflectivity. In this way, we were able to evaluate the original reflectivity, the filtered reflectivity and the compensated reflectivity. In the absence of clutter, we were able to quantitatively determine the severity of the bias introduced by the clutter filters, as well as the effectiveness of the SGCM to remove this bias. It should be noted that the uncertainty of reflectivity estimates near zero velocity is still high after compensation, however, removing the bias should yield significant improvements in precipitation estimation