An automatic light rain detection algorithm on NASA MPLNET lidar observations in the frame of WMO GALION project

The water cycle strongly influences life on Earth. In particular, the precipitation modifies the 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 low-intensity precipitation) at global scale, bedsides improving our understanding of the hydrological cycle, it is crucial to reduce the associated uncertainty of the global climate models to correctly forecast future scenarios, i.e. to apply fast mitigation strategies. In this study we developed an algorithm to automatically detect precipitation from lidar measurements obtained by the National and Aeronautics Space Administration (NASA) Micropulse lidar network (MPLNET) permanent observational site in Goddard. The algorithm, once full operational, will deliver in Near Real Time (latency 1.5h) a new rain mask product that will be publicly available on MPLNET website as part of the new Version 3 Level 1.5 data. The methodology, based on an image processing technique, can detect only light precipitation events (defined by intensity and duration) as the morphological filters used through the detection process are applied on the lidar volume depolarization ratio range corrected composite images, i.e. heavy rain events are unusable as the lidar signal is completely extinguished after few meters in the precipitation or no signal detected because of the water accumulated on the receiver optics. 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 better understand long-term aerosol-cloud interactions and aerosol atmospheric removal (scavenging effect) by rain as multi-year database being available for several MPLNET permanent observational sites across the globe. Moreover, we developed the automatic algorithm at Universitat Politecnica de Catalunya (UPC) Barcelona, the unique permanent observation station member of MPLNET and the European Aerosol Lidar Network (EARLINET) In the future the algorithm can be then easily applied to any other lidar and/or ceilometer network infrastructure in the frame of World Meteorological Organization (WMO) Global Aerosol Watch (GAW) aerosol lidar observation network (GALION)

[1]  Simone Lolli,et al.  Principal Component Analysis Approach to Evaluate Instrument Performances in Developing a Cost-Effective Reliable Instrument Network for Atmospheric Measurements , 2015 .

[2]  M. Löffler-Mang,et al.  An Optical Disdrometer for Measuring Size and Velocity of Hydrometeors , 2000 .

[3]  Soo Chin Liew,et al.  Applying Advanced Ground-Based Remote Sensing in the Southeast Asian Maritime Continent to Characterize Regional Proficiencies in Smoke Transport Modeling , 2016 .

[4]  L. Sauvage,et al.  EZ Lidar: A new compact autonomous eye-safe scanning aerosol Lidar for extinction measurements and PBL height detection. Validation of the performances against other instruments and intercomparison campaigns , 2011 .

[5]  P. Di Girolamo,et al.  Rain Evaporation Rate Estimates from Dual-Wavelength Lidar Measurements and Intercomparison against a Model Analytical Solution , 2017 .

[6]  Yu Gu,et al.  Daytime Cirrus Cloud Top-of-Atmosphere Radiative Forcing Properties at a Midlatitude Site and their Global Consequence. , 2016, Journal of applied meteorology and climatology.

[7]  M. Vaughan,et al.  Unusually Deep Wintertime Cirrus Clouds Observed over the Alaskan Sub-Arctic. , 2017, Bulletin of the American Meteorological Society.

[8]  Leo Pio D'Adderio,et al.  Evolution of drop size distribution in natural rain , 2018 .

[9]  Yu Gu,et al.  Impact of varying lidar measurement and data processing techniques in evaluating cirrus cloud and aerosol direct radiative effects , 2018 .

[10]  Simone Lolli,et al.  Evaluating Light Rain Drop Size Estimates from Multiwavelength Micropulse Lidar Network Profiling , 2013 .

[11]  Ellsworth J. Welton,et al.  Global monitoring of clouds and aerosols using a network of micropulse lidar systems , 2001, SPIE Asia-Pacific Remote Sensing.

[12]  Robin J. Hogan,et al.  Estimating drizzle drop size and precipitation rate using two-colour lidar measurements , 2010 .

[13]  R. Koster,et al.  Variance and Predictability of Precipitation at Seasonal-to-Interannual Timescales , 2000 .

[14]  Muhammad Bilal,et al.  A Simplified and Robust Surface Reflectance Estimation Method (SREM) for Use over Diverse Land Surfaces Using Multi-Sensor Data , 2019, Remote. Sens..

[15]  Luciano Alparone,et al.  Haze Correction for Contrast-Based Multispectral Pansharpening , 2017, IEEE Geoscience and Remote Sensing Letters.

[16]  Michaël Sicard,et al.  Vertically Resolved Precipitation Intensity Retrieved through a Synergy between the Ground-Based NASA MPLNET Lidar Network Measurements, Surface Disdrometer Datasets and an Analytical Model Solution , 2018, Remote. Sens..

[17]  Soo Chin Liew,et al.  Daytime Top-of-the-Atmosphere Cirrus Cloud Radiative Forcing Properties at Singapore , 2017 .

[18]  Pierre H. Flamant,et al.  0.355-micrometer direct detection wind lidar under testing during a field campaign in consideration of ESA's ADM-Aeolus mission , 2013 .

[19]  Jasper R. Lewis,et al.  Overview of MPLNET Version 3 Cloud Detection. , 2016, Journal of atmospheric and oceanic technology.

[20]  T. J. Boyd,et al.  Aerosol meteorology of Maritime Continent for the 2012 7SEAS southwest monsoon intensive study - Part 2: Philippine receptor observations of fine-scale aerosol behavior , 2016 .

[21]  M. Ciofini,et al.  Diffractive optical components for high power laser beam sampling , 2003 .