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Proceedings Paper

An automatic light rain detection algorithm on NASA MPLNET lidar observations in the frame of WMO GALION project
Author(s): Simone Lolli; Michaël Sicard; Gemine Vivone; Jasper R. Lewis; Ellsworth J. Welton; Adolfo Comerón
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Paper Abstract

The water cycle strongly influences life on Earth. In particular, the precipitation modifies the atmospheric column thermodynamics through the process of evaporation and serves as a proxy for latent heat modulation. For this reason, a correct precipitation parameterization (especially low-intensity precipitation) at global scale, bedsides improving our understanding of the hydrological cycle, it is crucial to reduce the associated uncertainty of the global climate models to correctly forecast future scenarios, i.e. to apply fast mitigation strategies. In this study we developed an algorithm to automatically detect precipitation from lidar measurements obtained by the National and Aeronautics Space Administration (NASA) Micropulse lidar network (MPLNET) permanent observational site in Goddard. The algorithm, once full operational, will deliver in Near Real Time (latency 1.5h) a new rain mask product that will be publicly available on MPLNET website as part of the new Version 3 Level 1.5 data. The methodology, based on an image processing technique, can detect only light precipitation events (defined by intensity and duration) as the morphological filters used through the detection process are applied on the lidar volume depolarization ratio range corrected composite images, i.e. heavy rain events are unusable as the lidar signal is completely extinguished after few meters in the precipitation or no signal detected because of the water accumulated on the receiver optics. Results from the algorithm, besides filling a gap in precipitation and virga detection by radars, are of particular interest for the scientific community because will help to better understand long-term aerosol-cloud interactions and aerosol atmospheric removal (scavenging effect) by rain as multi-year database being available for several MPLNET permanent observational sites across the globe. Moreover, we developed the automatic algorithm at Universitat Politecnica de Catalunya (UPC) Barcelona, the unique permanent observation station member of MPLNET and the European Aerosol Lidar Network (EARLINET) In the future the algorithm can be then easily applied to any other lidar and/or ceilometer network infrastructure in the frame of World Meteorological Organization (WMO) Global Aerosol Watch (GAW) aerosol lidar observation network (GALION)

Paper Details

Date Published: 9 October 2019
PDF: 7 pages
Proc. SPIE 11152, Remote Sensing of Clouds and the Atmosphere XXIV, 1115208 (9 October 2019); doi: 10.1117/12.2533571
Show Author Affiliations
Simone Lolli, Institute of Methodologies for Environmental Analysis, CNR (Italy)
Science Systems and Applications (United States)
Michaël Sicard, Univ. Politecnica de Catalunya (Spain)
Gemine Vivone, Univ. of Salerno (Italy)
Jasper R. Lewis, NASA Goddard Space Flight Ctr. (United States)
Ellsworth J. Welton, NASA Goddard Space Flight Ctr. (United States)
Adolfo Comerón, Univ. Politecnica de Catalunya (Spain)

Published in SPIE Proceedings Vol. 11152:
Remote Sensing of Clouds and the Atmosphere XXIV
Adolfo Comerón; Evgueni I. Kassianov; Klaus Schäfer; Richard H. Picard; Konradin Weber; Upendra N. Singh, Editor(s)

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