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. We explore the potential of spaceborne radar (SR) observationsfrom the Ku-band precipitation radars onboard the Tropical Rainfall MeasuringMission (TRMM) and Global Precipitation Measurement (GPM)satellites as a reference to quantify the ground radar (GR) reflectivitybias. To this end, the 3-D volume-matching algorithm proposed by Schwaller and Morris ( 2011 ) is implemented and applied to 5 years(2012–2016) of observations. We further extend the procedure by a frameworkto take into account the data quality of each ground radar bin. Through thesemethods, we are able to assign a quality index to each matching SR–GRvolume, and thus compute the GR calibration bias as a quality-weightedaverage of reflectivity differences in any sample of matching GR–SR volumes.We exemplify the idea of quality-weighted averaging by using the beamblockage fraction as the basis of a quality index. As a result, we canincrease the consistency of SR and GR observations, and thus the precision ofcalibration bias estimates. The remaining scatter between GR and SRreflectivity as well as the variability of bias estimates between overpassevents indicate, however, that other error sources are not yet fullyaddressed. Still, our study provides a framework to introduce any otherquality variables that are considered relevant in a specific context. Thecode that implements our analysis is based on the wradlib open-sourcesoftware library, and is, together with the data, publicly available tomonitor radar calibration or to scrutinize long series of archived radar databack to December 1997, when TRMM became operational.

Enhancing the consistency of spaceborne and ground-based radar comparisons by using beam blockage fraction as a quality filter