dc.contributor
Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.contributor
Universitat Politècnica de Catalunya. Departament de Física
dc.contributor
Universitat Politècnica de Catalunya. CommSensLab-UPC - Centre de Recerca en Comunicació i Detecció UPC
dc.contributor.author
Lolli, Simone
dc.contributor.author
Lewis, Jasper R.
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Tokay, A.
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Vivone, Gemine
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Campbell, James R.
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Dolinar, Erica K.
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Sicard, Michaël
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Comerón Tejero, Adolfo
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Rodríguez Gómez, Alejandro Antonio
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Muñoz Porcar, Constantino
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Garcia Benadí, Albert
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Udina Sistach, Mireia
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Bech Rustullet, Joan
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Welton, Ellsworth J.
dc.date.issued
2025-10-10
dc.identifier
Lolli, S. [et al.]. Evaluating the NASA MPLNET rain masking algorithm at Goddard Space Flight Center and Barcelona sites: relevance to EarthCARE cloud profiling radar validation. «Atmospheric research», 10 Octubre 2025, vol. 329, núm. article 108535.
dc.identifier
https://hdl.handle.net/2117/445542
dc.identifier
10.1016/j.atmosres.2025.108535
dc.description.abstract
Accurate detection of precipitation on a global scale is essential for advancing our understanding of the hydrological cycle and improving climate models. This study evaluates the performance of the Rain Masking Algorithm (RMA), developed for NASA’s Micropulse Lidar Network (MPLNET), in detecting rainfall events and distinguishing them from non-rain events over multiple years. The RMA’s effectiveness was validated against data from co-located disdrometers at two distinct MPLNET sites: the Goddard Space Flight Center (GSFC) in the United States and Universitat Politècnica de Catalunya (UPC) in Barcelona, Spain. Comparisons were also conducted with precipitation retrievals from the Integrated Multi-Satellite Retrievals for GPM (IMERG) project. Results indicate that the RMA is highly effective at detecting rain events, outperforming IMERG in sensitivity and accuracy at both sites, and demonstrating also unique capability in distinguishing virga, precipitation that evaporated before reaching the ground (not considered in the intercomparison). However, the algorithm shows limitations in identifying low-intensity precipitation and occasionally records false positives due to transient atmospheric artifacts. These results underscore the potential of the RMA in advancing the validation of satellite precipitation data from the ground, which is advantageous for the upcoming ESA-JAXA EarthCARE mission. Although the current analysis does not include EarthCARE data, we present the performance of RMA and a corresponding matchup strategy that are intended to facilitate next validation efforts for EarthCARE’s precipitation data. This work also highlights the RMA as a promising tool for refining global precipitation monitoring and advancing meteorological and climate forecasting accuracy.
dc.description.abstract
The NASA Micro Pulse Lidar Network is supported by the NASA Earth Observing System and the NASA Radiation Sciences Program. This work is also supported by the NASA Earth Science U.S. Partici-pating Investigator program through Grant No. 80NSSC21K0560.
dc.description.abstract
Peer Reviewed
dc.description.abstract
Postprint (published version)
dc.format
application/pdf
dc.relation
https://www.sciencedirect.com/science/article/pii/S0169809525006271
dc.rights
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights
Attribution-NonCommercial-NoDerivatives 4.0 International
dc.subject
Precipitation detection
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Rain Masking Algorithm
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Micropulse Lidar Network
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Disdrometer validation
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Satellite precipitation
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Ground-based observations
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Rainfall events
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Virga detection
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Algorithm performance
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Hydrological cycle
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Climate modeling
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Precipitation monitoring
dc.title
Evaluating the NASA MPLNET rain masking algorithm at Goddard Space Flight Center and Barcelona sites: relevance to EarthCARE cloud profiling radar validation