Assessment of the Effectiveness of the NASA MPLNET Lidar Network's Rain Mask Algorithm for Global Precipitation Monitoring

Rainfall events constitute a vital component of the hydrogeological cycle, underscoring the significance of monitoring global precipitation patterns. Within this manuscript, we present findings detailing the effectiveness of the rain mask algorithm deployed within the NASA MPLNET lidar network, spanning multiple years of measurement data. The comprehensive assessment was conducted at Goddard Space Flight Center permanent observational site for the MPLNET lidar network, juxtaposed against co-located in situ disdrometers and the NASA Integrated Multi-SatellitE Retrievals for GPS project (IMERG). This comparative analysis serves to validate observations for the upcoming Cloud Profiling Radar of the European Space Agency's EarthCare mission, slated for launch in 2024. The outcomes show the robust performance of the rain mask algorithm, showcasing a global accuracy reaching 80%. Moreover, the intercomparison reveals the algorithm's adeptness in identifying light precipitation and virga that had eluded both the disdrometer and IMERG detections.