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).
Highlights
IntroductionPrecipitation is a key-player in pairing the Earth–atmosphere water and energy cycle, through modulating atmospheric column latent heat and affecting cloudiness and cloud lifetime
Human life is strongly dependent on the water cycle [1]
The algorithm results are compared against rain intensity measurements obtained from a co-located disdrometer, an in-situ measurement device designed to measure the drop size distribution (DSD) [10], represented as the number of drops per unit of volume and per unit of raindrop diameter
Summary
Precipitation is a key-player in pairing the Earth–atmosphere water and energy cycle, through modulating atmospheric column latent heat and affecting cloudiness and cloud lifetime. For this reason, long-term precipitation datasets are needed to analyze spatial and temporal trends and variability, especially at the global scale [2]. Isolated case studies using lidar data (together with ancillary instrumentation) to quantitatively assess the atmospheric profile of precipitation micro-physical and optical characteristics are shown in [5,7,8,9,10] These efforts, due to their intrinsic complexity, are not suitable to be operationally implemented in a network of instruments
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
