Post-processed data and scripts for "Ocean eddies lower the global-mean, maximum intensity of tropical cyclones in a one-year global, coupled simulation"

This dataset contains the post-processed data and the python scripts used to obtain the post-processed data and generate the plots for "Ocean eddies lower the global-mean, maximum intensity of tropical cyclones in a one-year global, coupled simulation (submitted for publication)". The dataset is subdivided into five folders: Modules, TC_tracking, Eddy_identification, Postprocessed_data and Plots. Modules contains .py files with various functions that are imported in the other python scripts. TC_tracking contains three scripts: search_for_candidate_points.py, stitching_tracks.ipynb and Along_track_diagnostics.py. The first two are used to obtain the tropical cyclone tracks. The latter is required to compute additional diagnostics along the TC tracks, including sea-surface cooling and surface latent heat flux. Eddy_identification contains two files: calc_Okubo_Weiss_param.ipynb and Eddy_algorithm.py. The former derives the Okubo-Weiss parameter from raw ICON output and is required for the latter script, which identifies ocean eddies at a given snapshot in time. Postprocessed_data contains four subfolders: 1h_candidate_TCpoints, 1h_eddy_data, 1h_tracks and 1h_track diagnostics. As their names suggest, the candidate tropical cyclone points for the tracking algorithm are stored in h_candidate_TCpoints as csv files. The resulting 56 tracks are stored in 1h_tracks and the additional diagnostics, such as sea-surface cooling, are located in 1h_track_diagnostics as csv files. In 1h_eddy_data, there is a .npy file with the eddy data for each of the 56 tropical cyclones. Finally, Plots contains three scripts. TC_statistics.ipynb generates the global map of tropical cyclone tracks and the monthly TC-frequency plot in Figure 1. The plot for Figure 2 is generated using the script, eddy_TC_statistics.ipynb. Figure S2 in the supplementary materials section is created with TC_eddy_example_plot.ipynb.

本数据集汇集了经后处理的原始数据以及用于获取该后处理数据并绘制“在一年全球耦合模拟中，海洋涡旋降低热带气旋全球平均最大强度（投稿待刊）”所使用之Python脚本。数据集细分为五个子目录：模块（Modules）、热带气旋追踪（TC_tracking）、涡旋识别（Eddy_identification）、后处理数据（Postprocessed_data）和图表（Plots）。模块目录包含多个功能函数的.py文件，这些函数被导入至其他Python脚本中。热带气旋追踪目录包含三个脚本：search_for_candidate_points.py、stitching_tracks.ipynb和Along_track_diagnostics.py。其中前两个脚本用于获取热带气旋轨迹，而后者则用于计算热带气旋轨迹上的额外诊断，包括海面冷却和地表潜热通量。涡旋识别目录包含两个文件：calc_Okubo_Weiss_param.ipynb和Eddy_algorithm.py。前者从原始ICON输出中推导出Okubo-Weiss参数，该参数是后者脚本识别特定时间点海洋涡旋所必需的。后处理数据目录包含四个子目录：1小时候选热带气旋点（1h_candidate_TCpoints）、1小时涡旋数据（1h_eddy_data）、1小时轨迹（1h_tracks）和1小时轨迹诊断（1h_track diagnostics）。如目录名称所示，追踪算法的候选热带气旋点以csv文件形式存储于1h_candidate_TCpoints中，生成的56条轨迹存储于1h_tracks中，额外的诊断数据，如海面冷却，则位于1h_track_diagnostics目录中以csv文件形式存在。在1h_eddy_data中，有一个包含56个热带气旋涡旋数据的.npy文件。最后，图表目录包含三个脚本。TC_statistics.ipynb脚本生成了热带气旋轨迹的全球地图和图1所示的月度TC频率图。图2的图表则由eddy_TC_statistics.ipynb脚本生成。补充材料部分的图S2则是通过TC_eddy_example_plot.ipynb脚本创建的。