Abstract
The use of spectral polarimetric filters in the range-Doppler domain shows great promise for clutter mitigation in weather radar applications. One limitation of these filters is that they cannot deal with situations in which ground clutter and precipitation overlap. In this letter, we propose a new signal recovery technique based on kriging in the spectral domain to recover the precipitation in clutter-contaminated areas. Using synthetic radar data, we test our new method and compare its performance to that of Gaussian model adaptive processing and bilinear interpolation. Our results indicate that kriging is the most accurate and robust technique out of the three.
Highlights
P OLARIMETRIC Doppler weather radar is a very effective remote sensing tool for gaining insights into the dynamics and microphysics of precipitation [1]
Results show that signal recovery based on kriging is more stable and robust than Gaussian model adaptive processing (GMAP), especially in cases where there is a lot of weak precipitation or overlap between precipitation and ground clutter (GC)
2) In the case of GC, spectral polarimetric variable thresholding in combination with the central bin removal is applied to identify the clutter-contaminated precipitation bins remaining after the object-orientated spectral polarimetric (OBSpol) filter
Summary
P OLARIMETRIC Doppler weather radar is a very effective remote sensing tool for gaining insights into the dynamics and microphysics of precipitation [1]. The main limitation of spectral polarimetric filters such as MDsLDR and OBSpol is that they cannot deal well with cases in which clutter and precipitation overlap When this occurs, the spectral polarimetric filters tend to keep the clutter-contaminated precipitation, resulting in biased estimates. Many versions of GMAP assume that GC occupies a fixed spectral width over the whole range domain, which is not necessarily true for frequency modulation continuous wave radars where spectral leakage means GC tends to have larger spectral widths in the range bins near the radar [8] To alleviate these problems, we propose an alternative and more flexible approach to signal recovery than GMAP. Results show that signal recovery based on kriging is more stable and robust than GMAP, especially in cases where there is a lot of weak precipitation or overlap between precipitation and GC
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