A new clutter censoring technique—Updates on radar refractivity retrieval

Over the last year, radar-derived refractivity has been continuously estimated at the University of Oklahoma using two Oklahoma WSR-88D radars (KTLX, KFDR). Refractivity is calculated using phase measurements derived from stationary clutter targets in the radar's domain, which is typically limited to 40–60 km due to earth curvature. Previous work has shown the utility of short-term refractivity changes as a proxy for low-level moisture perturbations. These may in turn be used as a predictor for focal points of convection initiation. Unfortunately, using current quality control (QC) methods, the domain used for refractivity can contain clutter points with poor phase coherency, which ideally would be censored prior to computing refractivity. The algorithm interpolates and spatially filters the data due to the inherent statistical uncertainty and the general sparseness of the phase measurements, resulting in a spatial resolution of approximately 4 km. If clutter points with questionable phase data are not censored, surrounding data points (up to a distance of 4 km) may be impacted and the estimates of phase and refractivity will have degraded quality. This occurs most frequently near the edge of the clutter domain or where clutter signals may be dominated by trees. It can be shown that the existing algorithm had deteriorated refractivity quality when used on windy days, when these clutter targets were moving irregularly and introducing error-prone phase data into the algorithm. By determining which targets move in these situations, their spectral characteristics and phase coherency can be used to censor these points and improve estimates of the refractivity field. An extensive statistical study will be presented using the Oklahoma Mesonet data as ground truth. In addition, an improved phase QC methodology will be proposed, which significantly improves the ultimate quality of the refractivity data.