CCN Estimation from Space

This research focuses on estimating Cloud Condensation Nuclei (CCN) concentrations using space-borne lidar data.





This paper presents a novel remote sensing algorithm designed to accurately measure cloud condensation nuclei (CCN) concentrations at different altitudes using data from airborne and spaceborne lidar systems. By providing vertically resolved CCN profiles, the study enhances our understanding of cloud formation processes and their impact on climate. This breakthrough allows for more precise climate modeling and better predictions of weather patterns, offering valuable insights for both scientific research and practical applications in meteorology and environmental monitoring. The algorithm represents a significant advancement in atmospheric science, enabling more detailed and accurate observations of cloud microphysics.

Key Points:
  • Innovative Algorithm: A new remote sensing algorithm for vertically resolved CCN measurement using lidar data.
  • Improved Climate Models: Enhances understanding of cloud formation, leading to better climate predictions.
  • Atmospheric Science Advancement: Offers a powerful tool for precise cloud microphysics observation.

Journal Articles

  1. calipso_ccn.png
    A remote sensing algorithm for vertically resolved cloud condensation nuclei number concentrations from airborne and spaceborne lidar observations
    Piyushkumar N. Patel, Jonathan H. Jiang, Ritesh Gautam, Harish Gadhavi, Olga Kalashnikova, Michael J. Garay, Lan Gao, Feng Xu, and Ali Omar
    Atmospheric Chemistry and Physics, 2024
    PDF
  2. ERC
    Cloud condensation nuclei characteristics at the Southern Great Plains site: role of particle size distribution and aerosol hygroscopicity
    Piyushkumar N. Patel, and Jonathan H. Jiang
    Environmental Research Communications, 2021
    PDF