Radiance and Cloud Optical Thickness from Large Eddy Simulations over the Sulu Sea

This repository contains the data files to accompany the paper "Segmentation-Based Multi-Pixel Cloud Optical Thickness Retrieval Using a Convolutional Neural Network". Please cite the paper as follows:

Nataraja, V., Schmidt, S., Chen, H., Yamaguchi, T., Kazil, J., Feingold, G., Wolf, K., and Iwabuchi, H.: Segmentation-Based Multi-Pixel Cloud Optical Thickness Retrieval Using a Convolutional Neural Network, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2022-45, in review, 2022.

The 6 HDF5 files were generated using a tool called EaR³T developed by Hong Chen using Large Eddy Simulations over the Sulu Sea (Yamaguchi et al., 2019). Each hdf5 file contains 6 fields: 

cot_inp_3d: COT Input: column integrated COT directly from LES data;

rad_mca_1d: MCARaTS 1D Radiance: radiance calculated from COT Input using MCARaTS in IPA mode;

rad_mca_3d: MCARaTS 3D Radiance: radiance calculated from COT Input using MCARaTS in 3D mode;

rad_ret_1d: Radiance from Input COT: radiance calculated from COT Input using a pre-calculated COT vs Radiance relationship;

cot_ret_1d: COT from MCARaTS 1D Radiance: COT obtained from MCARaTS 1D Radiance using a pre-calculated COT vs Radiance relationship;

cot_ret_3d: COT from MCARaTS 3D Radiance: COT obtained from MCARaTS 3D Radiance using a pre-calculated COT vs Radiance relationship.