| Summary: | DeepSurge is a newly presented deep-learning approach to modeling the storm surge generated by a tropical cyclone (TC). This dataset is a collection of DeepSurge outputs for synthetic TCs in the North Atlantic generated by the HighResMIP project (Haarsma et al. 2016) for a simulated historical (1950-2014) and future (2015-2050) climate under the climate scenario SSP585. The data generation process and data analysis is detailed in an upcoming publication. The storm surge data presented here intentionally does not include the effects of sea level rise, rainfall, or other factors, in order to isolate the effects of changing TC climatology on future storm surge risk. Dataset format The data comes in the form of maximum surge levels at 2846 near-coastal locations for each synthetic TC. Each TC is defined by the corresponding track in the HighResMIP TempestExtremes dataset (Roberts 2019). The data is presented in NetCDF format, with two dimensions: 'nodes', the number of near-coastal locations, always 2846. 'tracks', the number of tracks in the simulation, which is different in each file. There are 6 variables in each file: 'lons' and 'lats', the coordinates of the nodes in degrees North and East respectively. 'track_valid' is a binary indicator (zero for false, one for true) indicating whether the TC occurs within the region of interest (HighResMIP tracks are global, but we only simulate those in the North Atlantic) 'track_done' is another binary indicator for whether the track has been simulated. It should indicate true for all tracks for which 'track_valid' is true. 'max_zeta' provides the predicted maximum surge height, in meters, for each storm at all 2846 nodes. This data is only valid in entries for which the corresponding 'track_done' and 'track_valid' indicators are true. 'years' is the year in which each simulated TC occurs. This work was supported by the Multisector Dynamics and Regional and Global Model Analysis program areas of the U.S. Department of Energy (DOE), Office of Science, Office of Biological ...
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