Overview of the New Version 3 NASA Micro-Pulse Lidar Network (MPLNET) Automatic Precipitation Detection Algorithm
| dc.contributor.author | Lolli, Simone | |
| dc.contributor.author | Vivone, Gemine | |
| dc.contributor.author | Lewis, Jasper R. | |
| dc.contributor.author | Sicard, Michaël | |
| dc.contributor.author | Welton, Ellsworth J. | |
| dc.contributor.author | Campbell, James R. | |
| dc.contributor.author | Comerón, Adolfo | |
| dc.contributor.author | D’Adderio, Leo Pio | |
| dc.contributor.author | Tokay, Ali | |
| dc.contributor.author | Giunta, Aldo | |
| dc.contributor.author | Pappalardo, Gelsomina | |
| dc.date.accessioned | 2020-02-06T18:07:30Z | |
| dc.date.available | 2020-02-06T18:07:30Z | |
| dc.date.issued | 2019-12-24 | |
| dc.description.abstract | Precipitation modifies 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 for low-intensity precipitation) within global scale models is crucial. In addition to improving our modeling of the hydrological cycle, this will reduce the associated uncertainty of global climate models in correctly forecasting future scenarios, and will enable the application of mitigation strategies. In this manuscript we present a proof of concept algorithm to automatically detect precipitation from lidar measurements obtained from the National Aeronautics and Space Administration Micropulse lidar network (MPLNET). The algorithm, once tested and validated against other remote sensing instruments, will be operationally implemented into the network to deliver a near real time (latency <1.5 h) rain masking variable that will be publicly available on MPLNET website as part of the new Version 3 data products. The methodology, based on an image processing technique, detects only light precipitation events (defined by intensity and duration) such as light rain, drizzle, and virga. During heavy rain events, the lidar signal is completely extinguished after a few meters in the precipitation or it is unusable because of water accumulated on the receiver optics. Results from the algorithm, in addition to filling a gap in light rain, drizzle, and virga detection by radars, are of particular interest for the scientific community as they help to fully characterize the aerosol cycle, from emission to deposition, as precipitation is a crucial meteorological phenomenon accelerating atmospheric aerosol removal through the scavenging effect. Algorithm results will also help the understanding of long term aerosol–cloud interactions, exploiting the multi-year database from several MPLNET permanent observational sites across the globe. The algorithm is also applicable to other lidar and/or ceilometer network infrastructures in the framework of the Global Aerosol Watch (GAW) aerosol lidar observation network (GALION). | en_US |
| dc.description.sponsorship | This research was funded by the Italian Research Council (CNR) Short Term Mobility Program. The NASA Micro-Pulse Lidar Network is supported by the NASA Earth Observing System (S. Platnick) and Radiation Sciences Program (H. Maring). | en_US |
| dc.description.uri | https://www.mdpi.com/2072-4292/12/1/71 | en_US |
| dc.format.extent | 16 pages | en_US |
| dc.genre | journal articles | en_US |
| dc.identifier | doi:10.13016/m2l3ku-bngc | |
| dc.identifier.citation | Lolli, Simone; Vivone, Gemine; Lewis, Jasper R.; Sicard, Michaël; Welton, Ellsworth J.; Campbell, James R.; Comerón, Adolfo; D’Adderio, Leo Pio; Tokay, Ali; Giunta, Aldo; Pappalardo, Gelsomina; Overview of the New Version 3 NASA Micro-Pulse Lidar Network (MPLNET) Automatic Precipitation Detection Algorithm; Remote Sensing 12(1), 71 (2019); https://www.mdpi.com/2072-4292/12/1/71 | en_US |
| dc.identifier.uri | https://doi.org/10.3390/rs12010071 | |
| dc.identifier.uri | http://hdl.handle.net/11603/17228 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | MDPI | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Joint Center for Earth Systems Technology | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | |
| dc.rights | Public Domain Mark 1.0 | * |
| dc.rights | This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law. | |
| dc.rights.uri | http://creativecommons.org/publicdomain/mark/1.0/ | * |
| dc.title | Overview of the New Version 3 NASA Micro-Pulse Lidar Network (MPLNET) Automatic Precipitation Detection Algorithm | en_US |
| dc.type | Text | en_US |