Preprints
https://doi.org/10.5194/amt-2023-266
https://doi.org/10.5194/amt-2023-266
03 Jan 2024
 | 03 Jan 2024
Status: this preprint is currently under review for the journal AMT.

A random forest algorithm for the prediction of cloud liquid water content from combined CloudSat/CALIPSO observations

Richard M. Schulte, Matthew D. Lebsock, John M. Haynes, and Yongxiang Hu

Abstract. A significant fraction of liquid clouds are not captured in existing CloudSat radar-based products because the clouds are masked by surface clutter or have insufficient reflectivities. To account for these missing clouds, we train a random forest regression model to predict cloud optical depth and cloud top effective radius from other CloudSat and CALIPSO observables that do not include the radar reflectivity profile. By assuming a subadiabatic cloud model, we are then able to retrieve a vertical profile of cloud microphysical properties for all liquid-phase oceanic clouds that are detected by CALIPSO’s lidar but missed by CloudSat’s radar. Daytime estimates of cloud optical depth, cloud top effective radius, and cloud liquid water path are robustly correlated with coincident estimates from the MODIS instrument onboard the Aqua satellite. This new algorithm offers a promising path forward for estimating the water contents of thin liquid clouds observed by CloudSat and CALIPSO at night, when MODIS observations that rely upon reflected sunlight are not available.

Richard M. Schulte, Matthew D. Lebsock, John M. Haynes, and Yongxiang Hu

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2023-266', Anonymous Referee #1, 14 Jan 2024
  • RC2: 'Comment on amt-2023-266', Anonymous Referee #2, 19 Jan 2024
Richard M. Schulte, Matthew D. Lebsock, John M. Haynes, and Yongxiang Hu
Richard M. Schulte, Matthew D. Lebsock, John M. Haynes, and Yongxiang Hu

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Short summary
This paper describes a method to improve the detection of liquid clouds that are easily missed by the CloudSat satellite radar. To address this, we use machine learning techniques to estimate cloud properties (optical depth and droplet size) based on other satellite measurements. The results are compared with data from the MODIS instrument on the Aqua satellite, showing good correlations.