https://doi.org/10.1051/epjconf/202023705006
Fully Automated Light Precipitation Detection from MPLNET and EARLINET Network Lidar Measurements
1 CNR, Institute of Methodologies for Environmental Analysis, 85050 Tito Scalo (PZ), Italy
2 NASA Goddard Flight Space Center, CODE 612, Greenbelt, MD 20771, USA
3 JCET-UMBC, 1000 Hilltop Cir, Baltimore, MD 21250, USA
4 Univ. of Salerno, Dept. of Inform. Engin., Electr. Engin. and App. Maths., 84084 Fisciano (SA), Italy
5 Naval Research Laboratory, 7 Grace Hopper Ave, Monterey, CA 93943, USA
6 RSLAB, Univ. Politec de Catalunya, Carrer de Jordi Girona, 1, 3, 08034 Barcelona, Spain
* Email: simone.lolli@imaa.cnr.it
Published online: 7 July 2020
The water cycle strongly influence life on Earth and precipitation especially modifies the atmospheric column thermodynamics through the evaporation process and serving as a proxy for latent heat modulation. For this reason, a correct light precipitation parameterization at global scale, it is of fundamental importance, bedsides improving our understanding of the hydrological cycle, to reduce the associated uncertainty of the global climate models to correctly forecast future scenarios. In this context we developed a full automatic algorithm based on morphological filters that, once operational, will make available a new rain product for the NASA Micropulse Lidar Network (MPLNET) and the European Aerosol Research Lidar Network (EARLINET) in the frame of WMO GALION Project
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
