Applications of the New V3 NASA MPLNET Rain-Masking Algorithm: Aerosol–Cloud Interaction Studies

In this study we illustrate the development of an algorithm to automatically detect precipitation from lidar measurements obtained from the National and Aeronautics Space Administration (NASA) Micropulse lidar network (MPLNET) observations. The algorithm delivers in Near Real Time (latency <1.5 hr) a new rain masking variable that will be eventually publicly available on MPLNET website as part of the new Version 3 data products. The methodology, based on an image processing technique can detect only light to moderate precipitation events (defined by intensity and duration) as the morphological filters used through the detection process are applied on the volume depolarization ratio variable composite images obtained from the NASA MPLNET Level 1.5 NRB product. 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 fully characterize the aerosol cycle, from emission to deposition, as precipitation is a crucial meteorological phenomena accelerating the atmospheric aerosol removal through the wet scavenging effect. As an example, in this study we prove, for the first time to our knowledge, how rain detection from ground-based lidar observations are effective in showing a strong negative correlation between the Aerosol Optical Depth (AOD) and precipitation.