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.本仓库包含用于配合论文《基于卷积神经网络的分割式多像素云光学厚度反演》（Segmentation-Based Multi-Pixel Cloud Optical Thickness Retrieval Using a Convolutional Neural Network）的数据文件。请按以下方式引用该论文：Nataraja, V.、Schmidt, S.、Chen, H.、Yamaguchi, T.、Kazil, J.、Feingold, G.、Wolf, K. 与 Iwabuchi, H.：《基于卷积神经网络的分割式多像素云光学厚度反演》，Atmos. Meas. Tech. Discuss. [预印本]，https://doi.org/10.5194/amt-2022-45，待刊，2022年。 本次发布的6个HDF5（Hierarchical Data Format 5）文件由Hong Chen开发的名为EaR³T的工具生成，该工具基于苏禄海（Sulu Sea）区域的大涡模拟（Large Eddy Simulations, LES）数据（Yamaguchi et al., 2019）。每个HDF5文件均包含6个数据场： 1. cot_inp_3d：COT输入场：直接源自大涡模拟数据的柱积分云光学厚度（Cloud Optical Thickness, COT）； 2. rad_mca_1d：MCARaTS一维辐射亮度场：采用MCARaTS的IPA模式，基于上述COT输入场计算得到的辐射亮度； 3. rad_mca_3d：MCARaTS三维辐射亮度场：采用MCARaTS的三维模式，基于上述COT输入场计算得到的辐射亮度； 4. rad_ret_1d：基于输入COT的辐射亮度：通过预计算的COT与辐射亮度对应关系，由COT输入场计算得到的辐射亮度； 5. cot_ret_1d：基于MCARaTS一维辐射亮度的COT反演结果：通过预计算的COT与辐射亮度对应关系，由MCARaTS一维辐射亮度反演得到的云光学厚度； 6. cot_ret_3d：基于MCARaTS三维辐射亮度的COT反演结果：通过预计算的COT与辐射亮度对应关系，由MCARaTS三维辐射亮度反演得到的云光学厚度。