Using a continental-scale data quality monitoring framework to evaluate a new non-weather filter for radar observations


Removing non-weather echoes is a critical component of the quality control (QC) chain used in the context of radar data assimilation for numerical weather prediction, Quantitative Precipitation Estimation (QPE), and nowcasting applications. Recent studies show that using a simple QC method based on the depolarization ratio (DR) performs remarkably well in many situations. Nonetheless, this method may misclassify echoes in regions affected by non-uniform beam filling or melting particles. This study presents an updated version of this QC used to remove non-weather echoes that use the DR-based classification together with a set of physically-based rules for correcting misclassifications of hail, non-uniform beam filling, and melting particles. The potential of the new QC is evaluated using a continental-scale monitoring framework that compares the radar observations after QC with the precipitation occurrence derived from meteorological aerodrome reports (METARs). For this evaluation, the study uses the radar data and the METARs available over North America during the summer of 2019 and winter of 2020. In addition, the study demonstrates the usefulness of the monitoring framework to determine the optimal QC configuration. Some practical limitations of using the METAR-derived precipitation to assess radar data quality are also discussed.