Machine Learning Predicted CCN Concentration for ORACLES ACI Study

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Authors: Lan Gao, Jens Redemann, Emily D. Lenhardt
Cited by: 0

Abstract

These files contain machine learning-predicted CCN concentration at the same resolution as High Spectral Resolution Lidar 2 data for observations from the NASA ORACLES campaign. CCN is predicted at a supersaturation of 0.4%, and the variable is called CCN within the HSRL structure in each of these files. All other variables are those from the corresponding date's HSRL-2 file, available here. The machine learning model used to generate this specific dataset used backscatter at 532 and 1064 nm and depolarization ratio at 532 and 1064 nm as input to predict CCN concentration. The method and training code are further described in Redemann and Gao (2024).

Fields of Study & Subjects

Atmospheric aerosols and clouds Atmospheric chemistry and aerosols Meteorological Phenomena and Simulations

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
12 February 2026
Language
EN
Document type
Dataset