Accession Number:
AD1096029
Title:
Overview of the New Version 3 NASA Micro-Pulse Lidar Network (MPLNET) Automatic Precipitation Detection Algorithm
Corporate Author:
NAVAL RESEARCH LAB WASHINGTON DC WASHINGTON United States
Report Date:
2019-12-24
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.
Descriptive Note:
Journal Article - Open Access
Supplementary Note:
Remote Sensing , 12, 71, 01 Jan 0001, 01 Jan 0001, 1 CNR-IMAA, Consiglio Nazionale delle Ricerche, Contrada S. Loja snc, 85050 Tito PZ, Italy2 Science Systems and Applications-NASA GSFC, Inc., Lanham, MD 20706, USA3 Deptartment of Informatic Engineering, Electric Engineering and Applied Mathematics, University of Salerno, 84084 Fisciano, Italy4 JCET-UMBC, Baltimore, MD 21228, USA5 CommSensLab, Deptartment of Signal Theory and Communications, Universitat Politcnica de Catalunya,6 Cincies i Tecnologies de lEspai, Centre de Recerca de lAeronutica i de lEspai/Institut dEstudis Espacialsde Catalunya (CTE-CRAE/IEEC), Universitat Politcnica de Catalunya, 08034 Barcelona, Spain7 NASA GSFC, Code 612, Greenbelt, MD 20771, USA8 Naval Research Laboratory, Monterey, CA 93940, USA9 CNR-ISAC, Consiglio Nazionale delle Ricerche, Roma, Via del Fosso del Cavaliere, 100 Roma RM, Italy
Pages:
0016
Distribution Statement:
Approved For Public Release;
File Size:
5.92MB
