Automatic detection and removal of ground clutter contamination on weather radars

Radar backscatter from the ground can contaminate weather signals, often resulting in severely biased meteorological estimates. If not removed, these clutter returns tend to bias reflectivity high as well as Doppler velocity and spectrum width toward zero. A ground clutter filter (GCF) can mitigate this contamination and provide unbiased meteorological estimates but typically with reduced quality. Moreover, significant biases could occur if the GCF is applied when clutter is not present and the weather signal has near-zero Doppler velocities. Thus, the overall quality of the meteorological estimates needlessly suffers when a GCF is misapplied. The problem of applying the GCF becomes very complex, especially when considering the dynamic nature of the atmosphere. Anomalous propagation can cause the radar beam to increase contact or overshoot the clutter, giving the appearance that the clutter shifts within or disappears from the radar volume coverage very rapidly. In this dynamic environment, spectral examination of the received echoes provides a means to determine the presence of clutter in real time without having to rely on static clutter maps. However, spectral analysis on a finite number of samples suffers from spectral leakage. To combat spectral leakage, tapered windows are typically applied. Strong clutter returns may require the use of windows with high dynamic ranges, but the use of these windows reduces the quality and resolution of the meteorological estimates. On the other hand, weaker clutter returns may only require low dynamic range windows, which help preserve the quality and resolution of the meteorological estimates. Consequently, a ‘smart' filter is needed that can examine the received radar echoes, apply a tapered window that best suits the conditions, determine the exact number of spectral coefficients affected by clutter contamination, and, only then, apply the GCF.

In this paper, we introduce a spectral GCF capable of satisfying the aforementioned considerations. The filter is referred to as Clutter Environment ANalysis using Adaptive Processing (CLEAN-AP) and performs real-time detection and suppression of ground clutter returns in dynamic atmospheric environments. We characterize the statistical performance of the CLEAN-AP filter with simulated clutter/weather mix and show real weather examples.