Sato and Ise (submitted) Open Data

______________________________________________________Sato and Ise (2021) Open DataAuthor: Hisashi SATO (JAMSTEC) hsatoscb_(at)_gmail.comDate: 22 Feb 2022URLs:https://ebcrpa.jamstec.go.jp/~hsato/Sato_and_Ise_2020 Corresponding publication: H. Sato and T. Ise (2021) Predicting global terrestrial biomes with the LeNet convolutional neural network. Geoscientific Model Development 2022 Vol. 15 Issue 7 Pages 3121-3132. DOI: 10.5194/gmd-15-3121-2022______________________________________________________1. Folder "1.VCDs"It contains Visualized Climate Environments (VCEs) for training and testing the Convolutional-Neural-Network (CNN) model. Names of compressed files (*.tar.gz) correspond to experiments. Each compressed file contains 4 to 16 folders.Naming rule of folders:The strings "CRU," "NCEP," "HadGEM," and "Miroc" stand for that the files are made from CRU_TS4.0, NCEP/NCAR reanalysis, Had2GEM-ESM, and Miroc-ESM climate datasets, respectively. The strings "AMeans" and "MMeans", respectively, stand for the annual-mean and monthly-mean climates, which are represented by VCEs in the compressed file.The string "EachYear" means that the compressed files contains VCEs of each year climate from 1971 to 1980, otherwise the compressed files contains VCEs of averaged climate over 10 years. The strings "hist", "RCP26", and "RCP85" mean that the compressed files contain VCEs of climate averaged over 1971-1980, 2091-2100@RCP2.6, and 2091-2100@RCP8.5, respectively.MainSimulation_training.tar.gzVCEs for training the CNN model for the main simulation and dependency test of climatic datasets for training and reconstructing performances.MainSimulation.tar.gzVCEs for testing the CNN model for the main simulation.CombinationSelection_Amean.tar.gzVCEs for an experiment to find optimal combination of climatic variables (annual means) represented by the VCEs. In the VCE of the RGB color tile, up to three climate variables can be represented by RGB channels. To find the optimal combination of climatic variables, we systematically evaluated the model performance of 14 combinations of climatic variable experiments for annual means. It's result is presented in the Table S3.Extracting this compressed fire results in "CRU", "NCEP", "HadGEM", and "Miroc" folders. Each folder contains 14 subfolders named with sequential numbers, which corresponds to model numbers in the Table S3.CombinationSelection_Mmean.tar.gzSame as the CombinationSelection_Amean.tar.gz except made with monthly mean climates, and related information is available in the Table S4.ScalerSelection.tar.gzVCEs for evaluating the influences of different transformations of climatic variables on the resulting accuracy. It's result is presented in the Table S5.Extracting this compressed fire results in "CRU", "NCEP", "HadGEM", and "Miroc" folders. Each folder contains 4 subfolders named with sequential numbers. "Scaler1" stands for log transformed, "Scaler2" stands for no transformed (linear), "Scaler3" stands for Sigmoid(gain=5) transformed, "Scaler4" stands for Sigmoid(gain=10) transformed.ColorAssignExperiment.tar.gzVCEs for evaluate the influences of assignment patterns of air temperature and precipitation to RGB color channels of the VCE. It's result is presented in the Table S6. Extracting this compressed fire results in "CRU", "NCEP", "HadGEM", and "Miroc" folders. Each folder contains 6 subfolders named with sequential numbers, which indicates model number in the Table S6.AverageExtentExperiment.tar.gzVCEs for sensitivity test, where training and test accuracies are compared among models that are trained by monthly climate averaged over three different periods: 10 years (1971-1980; Control), 20 years (1961-1980), and 30 years (1951-1980).Individual VCE shows climatic condition of each half degree grid cell. According to the ISLSCP2 data, VCEs are classified by their their potential vegetation type of the grid. Number of deepest folder names correspond vegetation code of the ISLSCP2.Following is the vegetation code. 01: Tropical Evergreen Forest/Woodland 02: Tropical Deciduous Forest/Woodland 03: Temperate Broadleaf Evergreen Forest/ Woodland 04: Temperate Needleleaf Evergreen Forest/Woodland 05: Temperate Deciduous Forest/Woodland 06: Boreal Evergreen Forest/Woodland 07: Boreal Deciduous Forest/Woodland 08: Evergreen/Deciduous Mixed Forest 09: Savanna 10: Grassland/Steppe 11: Dense Shrubland 12: Open Shrubland 13: Tundra 14: Desert 15: Polar Desert/Rock/IceThe naming rule for VCEs of 10 years average climate is following:Latitude number + "_" + Longitude number + ".png"The naming rule for VCEs of each year's climate is following:Latitude number + "_" + Longitude number + "_" + Year of climate + ".png"Here, latitude number ranges from 001 to 360, starting from north-latitude-90 to southward with half degree interval. The longitude number ranges from 001 to 720, starting from west-longitude-180 to eastward with half degree interval. On top of this subfolder, there is PicList.zip, which is a compressed file of PicList.txt. This text file is required to classify VCEs with the trained model._____________________________________________2. Folder "2.Result"It contains copies of the image classification results on the Digits screen. Subfolder names correspond to experiment names. Each subfolder contains sub sub-folders "DigitsOutput," "ConfusionMatrix," and "ReconstructedBiomeMap." Naming rules for files are basically same as those of VCEs._____________________________________________3. Folder "3.LearningCurves"It contains screen capture of Digits output, showing learning curve of CNN models. CRU climate data. This folder contains 5 subfolders. Naming rules of these subfolders are same as those of VCEs._____________________________________________4. Folder "5.FigureMaterials" It contains data and codes (in R) for drawing the figures in the manuscript."_____________________________________________________________佐藤与伊势（2021）公开数据集 作者：Hisashi SATO（日本海洋地球科学技术厅（JAMSTEC）），邮箱：hsatoscb_(at)_gmail.com 日期：2022年2月22日 相关URL：https://ebcrpa.jamstec.go.jp/~hsato/Sato_and_Ise_2020 对应发表论文：H. Sato与T. Ise（2021）《基于LeNet卷积神经网络（LeNet convolutional neural network）预测全球陆地生物群系》，刊载于《Geoscientific Model Development》2022年第15卷第7期，页码3121-3132，DOI：10.5194/gmd-15-3121-2022 1. 文件夹“1.VCDs” 该文件夹存储用于训练与测试卷积神经网络（Convolutional-Neural-Network, CNN）模型的可视化气候环境（Visualized Climate Environments, VCEs）数据。压缩文件（*.tar.gz）的命名对应不同实验，每个压缩文件包含4至16个子文件夹。 文件夹命名规则： 字符串“CRU”“NCEP”“HadGEM”“Miroc”分别代表数据集源自CRU_TS4.0、NCEP/NCAR再分析数据、Had2GEM-ESM与Miroc-ESM气候数据集；“AMeans”与“MMeans”分别代表年平均气候与月平均气候，对应压缩文件内的VCEs。“EachYear”表示该压缩文件包含1971年至1980年逐年气候的VCEs，反之则包含10年平均气候的VCEs。“hist”“RCP26”“RCP85”分别代表压缩文件包含1971-1980年平均气候、2091-2100年RCP2.6情景下平均气候、2091-2100年RCP8.5情景下平均气候的VCEs。 各压缩文件详情： - MainSimulation_training.tar.gz：用于主模拟训练与训练数据集气候依赖性测试的CNN模型的VCEs数据。 - MainSimulation.tar.gz：用于主模拟中CNN模型测试的VCEs数据。 - CombinationSelection_Amean.tar.gz：用于筛选最优气候变量组合（年平均尺度）的实验所需VCEs数据。RGB色彩块VCE最多可通过三个RGB通道表示三种气候变量。本研究系统评估了14种年平均气候变量组合的模型性能，相关结果见补充表S3。解压该压缩文件将得到“CRU”“NCEP”“HadGEM”“Miroc”四个文件夹，每个文件夹包含14个以连续序号命名的子文件夹，对应补充表S3中的模型编号。 - CombinationSelection_Mmean.tar.gz：与CombinationSelection_Amean.tar.gz类似，但基于月平均气候数据构建，相关结果见补充表S4。 - ScalerSelection.tar.gz：用于评估不同气候变量变换方式对模型精度的影响，相关结果见补充表S5。解压该压缩文件将得到“CRU”“NCEP”“HadGEM”“Miroc”四个文件夹，每个文件夹包含4个以连续序号命名的子文件夹：“Scaler1”代表对数变换，“Scaler2”代表无变换（线性变换），“Scaler3”代表Sigmoid（增益=5）变换，“Scaler4”代表Sigmoid（增益=10）变换。 - ColorAssignExperiment.tar.gz：用于评估气温与降水分配至VCE的RGB色彩通道的不同模式对模型的影响，相关结果见补充表S6。解压该压缩文件将得到“CRU”“NCEP”“HadGEM”“Miroc”四个文件夹，每个文件夹包含6个以连续序号命名的子文件夹，对应补充表S6中的模型编号。 - AverageExtentExperiment.tar.gz：用于敏感性测试的VCEs数据，该测试对比了基于三种不同时段平均月气候训练的模型的训练与测试精度：10年（1971-1980年；对照组）、20年（1961-1980年）与30年（1951-1980年）。 每个VCE对应一个半度网格单元的气候状况。根据ISLSCP2数据集，VCEs按照对应网格的潜在植被类型进行分类。最深层级文件夹的名称对应ISLSCP2的植被编码，植被编码如下： 01：热带常绿林/林地 02：热带落叶林/林地 03：温带阔叶常绿林/林地 04：温带针叶常绿林/林地 05：温带落叶林/林地 06：北方常绿林/林地 07：北方落叶林/林地 08：常绿/落叶混交林 09：稀树草原 10：草原/干草原 11：稠密灌丛 12：开阔灌丛 13：苔原 14：荒漠 15：极地荒漠/岩石/冰盖 10年平均气候的VCE命名规则：纬度编号 + "_" + 经度编号 + ".png" 逐年气候的VCE命名规则：纬度编号 + "_" + 经度编号 + "_" + 气候年份 + ".png" 其中，纬度编号范围为001至360，从北纬90°向南以半度间隔递增；经度编号范围为001至720，从西经180°向东以半度间隔递增。 该子文件夹根目录下存在PicList.zip，为PicList.txt的压缩文件，该文本文件用于使用训练好的模型对VCEs进行分类。 2. 文件夹“2.Result” 该文件夹存储Digits分类界面上的图像分类结果副本。子文件夹名称对应实验名称，每个子文件夹包含“DigitsOutput”“ConfusionMatrix”与“ReconstructedBiomeMap”三个次级子文件夹。文件命名规则与VCEs基本一致。 3. 文件夹“3.LearningCurves” 该文件夹存储CNN模型学习曲线的数字屏幕截图（基于CRU气候数据），包含5个子文件夹，其命名规则与VCEs一致。 4. 文件夹“5.FigureMaterials” 该文件夹包含绘制论文附图所需的数据与R语言代码。