Preprints
https://doi.org/10.5194/amt-2024-87
https://doi.org/10.5194/amt-2024-87
08 May 2024
 | 08 May 2024
Status: this preprint is currently under review for the journal AMT.

Optimal estimation of cloud properties from thermal infrared observations with a combination of deep learning and radiative transfer simulation

He Huang, Quan Wang, Chao Liu, and Chen Zhou

Abstract. While traditional thermal infrared retrieval algorithms based on radiative transfer models (RTM) could not effectively retrieve the cloud optical thickness of thick clouds, machine learning based algorithms were found to be able to provide reasonable estimations for both daytime and nighttime. Nevertheless, stand-alone machine learning algorithms are occasionally criticized for the lack of explicit physical processes. In this study, RTM simulations and a machine learning algorithm are synergistically utilized using the optimal estimation (OE) method to retrieve cloud properties from thermal infrared radiometry measured by Moderate Resolution Imaging Spectroradiometer (MODIS). In the new algorithm, retrievals from a machine learning algorithm are used to provide priori states for the iterative process of OE method, and an RTM is used to create radiance lookup tables that are used in the iteration processes. Compared with stand-alone OE, the cloud properties retrieved by the new algorithm show an overall better performance by using the spatial statistic information obtained by machine learning algorithm. Compared with stand-alone machine-learning based algorithm, the radiances simulated based on retrievals from the new method align more closely with observations, and physical radiative processes are handled explicitly in the new algorithm. Therefore, the new method combines the advantages of RTM-based cloud retrieval methods and machine-learning models. These findings highlight the potential for machine-learning-based algorithms to enhance the efficacy of conventional remote sensing techniques.

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He Huang, Quan Wang, Chao Liu, and Chen Zhou

Status: open (until 19 Jun 2024)

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He Huang, Quan Wang, Chao Liu, and Chen Zhou
He Huang, Quan Wang, Chao Liu, and Chen Zhou

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Short summary
This study introduces a cloud property retrieval method which integrates traditional radiative transfer simulations with a machine-learning method. Retrievals from a machine learning algorithm are used to provide initial guesses, and a radiative transfer model is used to create radiance lookup tables for later iteration processes. The new method combines the advantages of traditional and machine learning algorithms, and is applicable both daytime and nighttime conditions.