RAFT synthetic tropical cyclones dataset for Balaguru et al. 2022 - Science Advances

This is the RAFT synthetic tropical cyclone (TC) dataset generated for the paper "Increased US coastal hurricane risk under climate change" submitted to the journal Science Advances in 2022.<br> Each file contains 50,000 synthetic TCs from RAFT either for the historical period (1980-2014) or the future period (2066-2100) under “SSP585”, and from a CMIP6 global climate model.<br> intensity_model_output_corrVMPI_11vars_alltcs_cutoff15_CMIP6_{PERIOD} _{MODEL}.mat<br> To read a .mat file in Python, one can use “scipy.io.loadmat”.<br> There are several variables included in each file, and all have the same dimension [number of storms, number of timesteps]. Here are a list of variable names and what they represent:<br> ‘lat’: Storm latitude;<br> ‘lon’: Storm longitude;<br> ‘year’: year;<br> ‘jday_syn’: Julian day in the year;<br> ‘vs0_syn’: maximum surface wind (knot).<br> Please note that this version of synthetic TC dataset is only intended for assessing the large-scale change of hurricane risk under climate change (through statistical-dynamical downscaling of CMIP6 GCMs), which is addressed in the above mentioned paper. Due to the model biases in CMIP6 and the low temporal resolution (monthly) used for RAFT inputs, the synthetic TCs' life-time maximum intensity is underestimated. Therefore, the synthetic TCs here should not be treated directly as "example TCs of current or future climate" without bias correction on the TC intensity. The authors plan to release a separate version of RAFT simulated synthetic TCs with proper bias correction for localized TC impact assessment. Please email authors if you have questions.本数据集为2022年提交至《Science Advances》期刊的论文《Increased US coastal hurricane risk under climate change》所生成的RAFT合成热带气旋（Tropical Cyclone, TC）数据集。 每个文件包含50000个来自RAFT的合成热带气旋，对应历史时段（1980-2014年）或SSP585（共享社会经济路径5-8.5）情景下的未来时段（2066-2100年），数据源自CMIP6（耦合模式比较计划第六阶段）全球气候模式。 文件命名格式为：intensity_model_output_corrVMPI_11vars_alltcs_cutoff15_CMIP6_{PERIOD}_{MODEL}.mat 若需在Python中读取.mat格式文件，可使用scipy.io.loadmat工具。 每个文件包含多个变量，所有变量的维度均为[风暴数量, 时间步长数量]。以下为各变量名称及其含义： ‘lat’：风暴纬度； ‘lon’：风暴经度； ‘year’：年份； ‘jday_syn’：年内儒略日； ‘vs0_syn’：最大近地面风速（单位：节）。 请注意，本版本合成热带气旋数据集仅用于通过CMIP6全球气候模式的统计-动力降尺度（statistical-dynamical downscaling）方法评估气候变化下飓风风险的大尺度变化，这也是前述论文的研究主题。受CMIP6模式偏差以及RAFT输入数据采用的低时间分辨率（月度）影响，合成热带气旋的生命史最大强度被低估。因此，未对气旋强度进行偏差校正（bias correction）的情况下，本数据集的合成热带气旋不应直接被视作“当前或未来气候下的典型热带气旋”。作者计划发布另一版经过适当偏差校正的RAFT合成热带气旋数据集，用于局地气旋影响评估。如有疑问，请致信作者。