Use of Three-Dimensional Reflectivity Structure for Automated Detection and Removal of Nonprecipitating Echoes in Radar Data

Abstract

This study aims at assessing the potential of anomalous propagation conditions to occur, reviews past attempts to mitigate ground clutter contamination of radar data resulting from anomalous signal propagation, and presents a new algorithm for radar data quality control. Based on a 16-yr record of operational sounding data, the likelihood of atmospheric conditions to occur across the United States that potentially lead to anomalous propagation of radar signals is estimated. Anomalous signal propagation may lead to a significant contamination of radar data from ground echoes normally not seen by the radar, which could result in serious rainfall overestimates, if not recognized and treated appropriately. Many different approaches have been proposed to eliminate the problem of regular ground clutter close to the radar and temporary clutter resulting from anomalous signal propagation. None of the reported approaches, however, satisfactorily succeeds in the case of anomalous propagation ground returns embedded in precipitation echoes, a problem that remains a challenge today for radar data quality control. Taking strengths and weaknesses of past approaches into consideration, a new automated procedure has been developed that makes use of the three-dimensional reflectivity structure. In particular, the vertical extent of radar echoes, their spatial variability, and vertical gradient of intensity are evaluated by means of a decision tree. The new algorithm appears to work equally well in situations where anomalous propagation ground returns are either separated from or embedded within precipitation echoes. Moreover, sea clutter echoes are identified as not raining and successfully removed.