RAFT synthetic tropical cyclones dataset for Balaguru et al. 2022 - Science Advances (Q6708222)

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. 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. intensity_model_output_corrVMPI_11vars_alltcs_cutoff15_CMIP6_{PERIOD} _{MODEL}.mat To read a .mat file in Python, one can use scipy.io.loadmat. 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: lat: Storm latitude; lon: Storm longitude; year: year; jday_syn: Julian day in the year; vs0_syn: maximum surface wind (knot). 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.