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., Mani, S., Pringle, W. J., Moghimi, S., Myers, E., (2024). Probabilistic Prediction of Tropical Cyclone (TC) Driven Flood Depth and Extent with a User-Friendly Python Module. NOAA Technical Memorandum NOS CS 59. https://repository.library.noaa.gov/view/noaa/66123

• Daneshvar, F., Mani, S., Pringle, W. J., Moghimi, S., Myers, E., ( 2023). Evaluating the effect of inland hydrology and hurricane model on the probabilistic prediction of tropical cyclone (TC) driven coastal flooding. American Geophysical Union (AGU) Fall Meeting, December 2023, San Francisco, CA.

• 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

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