Echoes from normal propagation ground clutter (NP) as well as anomalous propagation ground clutter (AP) contaminate weather echoes so that precipitation estimates based such contaminated echoes are biased. Typically ground clutter filters can be applied to clutter contaminated radar data to alleviate this problem. Ground clutter maps are constructed when the atmosphere is precipitation free so that the clutter filters can be selectively applied to only those areas that are affected by NP clutter echoes. This avoids applying clutter filters everywhere which not only removes the contaminating clutter echoes but also zero velocity weather echoes. AP clutter presents a more difficult problem since by its nature, it is impossible to construct an a priori clutter map.

Currently, Fuzzy Logic based algorithms are used to identify radar data contaminated by AP clutter and then this data is censored so that precipitation estimates are not biased but this leaves data blanks. Radar operators can subsequently turn on clutter filters in those areas where there is AP clutter. This is tedious and prone to human error and is thus operationally impractical.

With the advent of fast digital radar receiver/processors, it is possible to identify clutter contaminated radar data and then apply a clutter filter to the contaminated data in real time. After the application of the clutter filter, radar moments are then calculated. In this way all clutter contaminated data is filtered but zero velocity weather data is retained for optimum data quality.

This paper describes a real time Fuzzy Logic algorithm for identifying and mitigating AP as well as NP clutter termed CMD (Clutter Mitigation Decision). Clutter contaminated radar data is first identified and subsequently a clutter filter is applied only to those clutter contaminated radar gates. Experimental data collected by S-Pol, NCAR's (National Center for Atmospheric Research) polarimetric S-Band research radar, are used to illustrate the CMD. The CMD algorithm is evaluated in term of its ability to distinguish clutter from weather echo for various CSR (clutter to signal ratios).