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Probabilistic Hurricane Storm Surge Model Workflow

This is a Python based workflow for getting probabilistic results from an ensemble of storm surge simulations during tropical cyclone events.

TO BE COMPLETED...

References

  • Daneshvar, F., et al. Tech Report (TODO)

  • Pringle, W. J., Mani, S., Sargsyan, K., Moghimi, S., Zhang, Y. J., Khazaei, B., Myers, E. (January 2023). Surrogate-Assisted Bayesian Uncertainty Quantification for Hurricane-Surge Coastal Flood Model Hindcasts [Conference presentation]. American Meteorological Society 103rd Annual Meeting 2023, Denver, CO

  • Moghimi, S., Seroka, G., Funakoshi, Y., Mani, S., Yang, Z., Velissariou, P., Pringle, W. J., Khazaei, B., Myers, E., Pe'eri S. (January 2023). NOAA National Ocean Service Storm Surge Modeling Infrastructure: An update on the research, research-to-operation and operational activities [Conference presentation]. American Meteorological Society 103rd Annual Meeting 2023, Denver, CO

  • Mani, S., Moghimi, S., Cui, L., Wang, Z., Zhang, Y. J., Lopez, J., Myers, E, Cockerill, T., Pe’eri, S. (2022). On-demand automated storm surge modeling including inland hydrology effects (NOAA Technical Memorandum NOS CS 52). United States. Office of Coast Survey. Coast Survey Development Laboratory (U.S.). https://repository.library.noaa.gov/view/noaa/47926

  • Mani, S., Moghimi, S., Zhang, Y. J., Cui, L., Wang, Z., Lopez, J., Myers E., Pe’eri S., Cockerill T. (Decemeber 2022). Multiplatform Automated On-demand Modeling System for Coastal Storm Surge Including Inland Hydrology Extremes [Poster session]. American Geophysical Union Fall Meeting 2022, Chicago, IL

  • Mani, S., Calzada, J., Moghimi, S., Zhang, Y. J., Lopez, J., MacLaughlin, T., Snyder, L., Myers, E., Pe'eri, S., Cockerill, T., Stubbs , J., Hammock, C. (February 2022). On-Demand On-Cloud Automated Mesh Generation for Coastal Modeling Applications [Conference online presentation]. Ocean Sciences Meeting 2022, Hawaii

  • Mani, S., Calzada, J. R., Moghimi, S., Zhang, Y. J., Myers, E., Pe’eri, S. (2021) OCSMesh: a data-driven automated unstructured mesh generation software for coastal ocean modeling (NOAA Technical Memorandum NOS CS 47). Coast Survey Development Laboratory (U.S.). https://doi.org/10.25923/csba-m072

This repository is a scientific product and is not official communication of the National Oceanic and Atmospheric Administration, or the United States Department of Commerce. All NOAA GitHub project code is provided on an ‘as is’ basis and the user assumes responsibility for its use. Any claims against the Department of Commerce or Department of Commerce bureaus stemming from the use of this GitHub project will be governed by all applicable Federal law. Any reference to specific commercial products, processes, or services by service mark, trademark, manufacturer, or otherwise, does not constitute or imply their endorsement, recommendation or favoring by the Department of Commerce. The Department of Commerce seal and logo, or the seal and logo of a DOC bureau, shall not be used in any manner to imply endorsement of any commercial product or activity by DOC or the United States Government.