Skip to content
DraconiticResearch
Search Sources API
203,203 studies31 sourcesUpdated 15 min ago
Peer-reviewedCC BY 4.0Scientific Data

Tropical cyclone-driven storm surge and wave database for the US North Atlantic and Gulf coastlines

  • Mithun Deb1,2
  • Karthik Balaguru1
  • Julian Rice1
  • Tim McPherson3
  • W. Brent Daniel3
  1. 1Coastal Sciences Division, Energy and Environment Directorate, Pacific Northwest National Laboratory, Seattle, WA 98109 USA
  2. 2Present Address: Environmental Hydrology and Design, Environmental Science Associates, San Francisco, CA 94105 USA
  3. 3Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, WA 99354 USA
Read full textDOICite

Abstract

Historical datasets of tropical cyclone-driven storm surge and waves at moderate coastal resolution are scarce, limiting coastal hazard analysis and AI/ML-based surrogate model development where field observations remain sparse. We present a publicly available hindcast database of surge and wave conditions for 232 U.S. landfalling and impactful storms (1981-2021). We applied the coupled ADCIRC+SWAN system across the entire U.S. North Atlantic and Gulf coastline on a coastal-refined unstructured mesh achieving practical nearshore resolution (~100 − 500 m) for computational feasibility. We forced simulations with parametric wind fields from the Generalized Asymmetric Holland Model fitted to NOAA best-track data. For each event, we provide hourly NetCDF files containing water-surface elevation, significant wave height, and peak wave period. Users can apply these fields as boundary conditions for higher-resolution local models, train ML model predictors, and conduct coastwide extreme-value analyses. We validated simulations against numerous NOAA tide gauges and NDBC buoys, demonstrating robust water level skill with documented wave biases. This comprehensive basin-scale database enables coastal flood hazard assessment across multiple decades of historical storm surge events.

Related

  1. Skillful Seasonal Tropical Cyclone Prediction with AI Weather--Climate Models Forced by Dynamically Predicted SSTs
    Hiroyuki Murakami, Baoqiang Xiang
    EarthArXivPreprintPublisher termsFull textAtmospheric science & meteorologyEarthArXiv
  2. Tibetan Plateau uplift increased western North Pacific tropical cyclone frequency
    Tingyu Zhang, Tianjun Zhou, Linqiang He +3
    Nature CommunicationsPeer-reviewedCC BY-NC-ND 4.0Full textClimate scienceCrossref
  3. When anticipation fails: A forensic analysis of Tropical Cyclone Ditwah reveals whole-system performance limits in a small-island flood-risk system
    Chamal Perera, Murugesu Sivapalan, Nadee Peiris +12
    EGUspherePreprintCC BY 4.0General earth scienceEGUsphere
  4. Tropical Cyclone-Following Dataset for Limited-Area Machine Learning Applications
    Minh-Khanh Luong, Chanh Kieu
    arXivPreprintCC BY 4.0Full textAtmospheric science & meteorologyarXiv
  5. Revisiting the mechanisms for the observed decrease in inner-core tropical cyclone precipitation
    Wenchao Chu, Jorge L. García-Franco, Suzana J. Camargo +2
    npj Climate and Atmospheric SciencePeer-reviewedCC BYFull textAtmospheric science & meteorologyDOAJ

Cite

BibTeXRIS
BibTeX
@article{deb2026tropical,
  title = {{Tropical cyclone-driven storm surge and wave database for the US North Atlantic and Gulf coastlines}},
  author = {Mithun Deb and Karthik Balaguru and Julian Rice and Tim McPherson and W. Brent Daniel},
  journal = {Scientific Data},
  year = {2026},
  month = jun,
  doi = {10.1038/s41597-026-07635-z},
  url = {https://doi.org/10.1038/s41597-026-07635-z},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Tropical cyclone-driven storm surge and wave database for the US North Atlantic and Gulf coastlines
AU  - Mithun Deb
AU  - Karthik Balaguru
AU  - Julian Rice
AU  - Tim McPherson
AU  - W. Brent Daniel
PY  - 2026
DA  - 2026/06/26
JO  - Scientific Data
DO  - 10.1038/s41597-026-07635-z
UR  - https://doi.org/10.1038/s41597-026-07635-z
AB  - Historical datasets of tropical cyclone-driven storm surge and waves at moderate coastal resolution are scarce, limiting coastal hazard analysis and AI/ML-based surrogate model development where field observations remain sparse. We present a publicly available hindcast database of surge and wave conditions for 232 U.S. landfalling and impactful storms (1981-2021). We applied the coupled ADCIRC+SWAN system across the entire U.S. North Atlantic and Gulf coastline on a coastal-refined unstructured mesh achieving practical nearshore resolution (~100 − 500 m) for computational feasibility. We forced simulations with parametric wind fields from the Generalized Asymmetric Holland Model fitted to NOAA best-track data. For each event, we provide hourly NetCDF files containing water-surface elevation, significant wave height, and peak wave period. Users can apply these fields as boundary conditions for higher-resolution local models, train ML model predictors, and conduct coastwide extreme-value analyses. We validated simulations against numerous NOAA tide gauges and NDBC buoys, demonstrating robust water level skill with documented wave biases. This comprehensive basin-scale database enables coastal flood hazard assessment across multiple decades of historical storm surge events.
SN  - 2052-4463
ER  -