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Overview of MPLNET Version 3 Cloud Detection.

Jasper R Lewis1, James R Campbell2, Ellsworth J Welton3

  • 1Joint Center for Earth Systems Technology, University of Maryland Baltimore County, Baltimore, Maryland.

Journal of Atmospheric and Oceanic Technology
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The updated National Aeronautics and Space Administration Micropulse Lidar Network cloud detection algorithm improves high-level and multi-layered cloud identification. This advancement enhances atmospheric research by providing more accurate cloud occurrence data.

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Area of Science:

  • Atmospheric Science
  • Remote Sensing
  • Cloud Physics

Background:

  • The National Aeronautics and Space Administration (NASA) Micropulse Lidar Network (MPLNET) provides crucial atmospheric data.
  • Accurate cloud detection is essential for climate modeling and weather forecasting.
  • Previous versions of cloud detection algorithms had limitations, particularly for high-level clouds.

Purpose of the Study:

  • To introduce and evaluate the Version 3 cloud detection algorithm for the MPLNET.
  • To compare the performance of the new algorithm against the previous version.
  • To analyze diurnal and seasonal variations in cloud occurrence frequency.

Main Methods:

  • Utilizing normalized Level 1 signal profiles from micropulse lidar.
  • Employing two complementary methods: vertical signal derivatives for low-level clouds and signal uncertainties for high-level clouds.
  • Implementing a multi-temporal averaging scheme to enhance detection in low signal-to-noise conditions.

Main Results:

  • The Version 3 algorithm shows significant improvements in detecting high-level clouds (above 5 km), increasing occurrence by nearly 6%.
  • Detection of multi-layered cloud profiles increased substantially, from 9% to 20%.
  • Analysis of macrophysical properties and optical depth for cirrus clouds was conducted, identifying retrieval limits.

Conclusions:

  • The updated MPLNET cloud detection algorithm (Version 3) offers enhanced accuracy, especially for high-altitude and multi-layered cloud systems.
  • The algorithm's improvements contribute to more reliable atmospheric measurements for climate and weather studies.
  • Further research can build upon these findings, particularly regarding the retrieval of molecular signals above cirrus clouds.