Skip to main content

ORIGINAL RESEARCH article

Front. Remote Sens.
Sec. Lidar Sensing
Volume 5 - 2024 | doi: 10.3389/frsen.2024.1477503

Spaceborne Lidar Measurement of Global Cloud Properties Through Machine Learning

Provisionally accepted
Xiaomei Lu Xiaomei Lu 1*Karen Hu Karen Hu 2
  • 1 Langley Research Center, National Aeronautics and Space Administration, Hampton, United States
  • 2 Governor’s School of Engineering, Hampton, VA, United States

The final, formatted version of the article will be published soon.

    With a large footprint size, multiple scattering measurements of clouds from spaceborne lidar provide useful information about cloud physical properties, such as cloud optical depths and cloud droplet size, both during daytime and nighttime. A neural network algorithm, with a subset of cloud backscatter profiles of dual polarization and dual wavelength CALIPSO lidar measurements during daytime as input variables and cloud physical properties derived from collocated MODIS multispectral measurements as output, is developed and evaluated with an independent subset of the collocated CALIPSO and MODIS measurements. The study suggests that with the 110 m receiver footprint size, CALIPSO lidar measurements are sensitive to liquid phase cloud optical depth variations from 0 to 25. A larger footprint size, thus more multiple scattering, is required for lidar to have sensitivities to all liquid phase clouds. The technique can be applied to all 17 years of CALIPSO daytime and nighttime measurements and thus provide useful information about global distributions of cloud physical properties both day and night.

    Keywords: Cloud optical depth, cloud effective droplet size, spaceborne Lidar, CALIPSO, multiple scattering

    Received: 07 Aug 2024; Accepted: 16 Sep 2024.

    Copyright: © 2024 Lu and Hu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Xiaomei Lu, Langley Research Center, National Aeronautics and Space Administration, Hampton, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.