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Cross-track Infrared Sounder Cloud Fraction Retrieval Using a Deep Neural Network by Qian Liu; Hui Xu; Paul R. Houser; Donglian Sun; Matthew Rice; Likun Wang; Daniel Q. Duffy; Chaowei Yang is a Earth and Planetary Sciences article available to read on EtoBox.

What is Cross-track Infrared Sounder Cloud Fraction Retrieval Using a Deep Neural Network about?

The retrieval of cloud fraction in satellite hyperspectral sounder field of view (FOV) is crucial for numerical weather prediction. This study proposes an innovative cloud fraction retrieval model for the hyperspectral infrared sounder - Cross-track Infrared Sounder (CrIS). The model is trained with a deep neural network (DNN), using the CrIS radiation spectra as the predictors and Visible Infrared Imaging Radiometer Suite (VIIRS) cloud mask as the learning target. An ensemble of randomly selected CrIS and VIIRS data are collocated and used as the training dataset. An optimized 5-layer neural network is built to establish the relationship between the CrIS spectra and the cloud fraction calculated from the VIIRS cloud mask within the CrIS FOV. In order to reduce the number of input predictors to enhance the efficiency of the model, a principal component transformation is performed on the original CrIS spectra and only the top 77 principal component scores are adopted as the final predictors. In general, the cloud fraction retrieved from the proposed DNN model are consistent with truth values calculated from the VIIRS cloud mask product. Further analysis on use cases demonstrates a s

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Author

Qian Liu; Hui Xu; Paul R. Houser; Donglian Sun; Matthew Rice; Likun Wang; Daniel Q. Duffy; Chaowei Yang

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Earth and Planetary Sciences (Physical Sciences)