AHI-CALIOP Collocated Data for Training and Validation of Cloud Masking Neural Networks

Collocated data between AHI at 2km resolution (nadir) and CALIOP 1km cloud product v4.20 used for training and validating cloud identification neural networks. The main training and validation data from 2019 is stored in monthly directories, whilst the collocated dataset used to compare the NN, JMA and BoM cloud mask performances is the file "superdf.h5". All collocated data is stored as .h5 files and was built using the Python Pandas package. In this archive, the data has been stored as compressed directories for each month or as a single compressed file in the case of "superdf.h5" using tar with bzip2 compression or just bzip2 compression respectively.本数据集为2千米分辨率星下点模式的先进葵花成像仪（AHI）与4.20版本1千米分辨率正交偏振云气溶胶激光雷达（CALIOP）云产品之间的协同匹配数据集，用于训练和验证云识别神经网络。2019年的主要训练与验证数据按月度目录存储，而用于对比神经网络（NN）、日本气象厅（JMA）与澳大利亚气象局（BoM）云掩码性能的协同匹配数据集则存储为文件"superdf.h5"。所有协同匹配数据集均以.h5格式存储，且通过Python Pandas库构建。本归档文件中，月度数据采用tar结合bzip2压缩的目录形式存储，而"superdf.h5"则仅通过bzip2压缩为单个压缩文件。