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Center Independent Research & Development: GSFC IRAD

Lidar Laser Development and PBL Detection Techniques

Completed Technology Project
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Project Description

Machine Learning techniques to de-noise total backscatter daytime lidar scenes (top) are showing promise (bottom). Further algorithm development would focus efforts on backscatter lidar PBL detection.

Lidar observations from space provide crucial information on vertical and horizontal distributions of clouds and aerosols that greatly improve our understanding of the climate system. GSFC has a SmallSat elastic backscatter lidar concept designed to measure vertical profiles of aerosols and clouds. For this funded effort, we will perform laser testing and improve the laser-telescope alignment to further advance GSFC's SmallSat lidar concept. Finally, we will implement existing machine learning (ML) techniques to denoise daytime lidar scenes and develop algorithms needed to translate better SNR into improved data products. 

The overarching goals of this project are to (1) advance GSFC’s SmallSat atmospheric lidar concepts and algorithms for consideration by the Aerosols and Cloud, Convection, and Precipitation (ACCP) and Planetary Boundary Layer (PBL) Earth Science Decadal Survey missions and (2) optimize GSFC’s existing airborne wind lidar concept for aerosol and cloud applications.

 

 

 

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