Forecasting tropical cyclones with greater precision

Challenge

Tropical cyclones pose significant threats to coastal communities due to the water levels, waves, and winds they generate. With rising sea levels, climate change, and growing coastal populations, the risks associated with these storms are increasing. Although forecasting hurricane paths and intensities has improved significantly in the past decade, uncertainty still exists about their specific impacts as they make landfall.   

Response

Led by CCS Associate Director Maitane Olabarrieta, the Coupled Ocean Atmosphere Waves Sediment Transport–Waves, Sediment, Surge, and Structure Response (COAWST–WSSSR) team is one of nine research groups participating in the Hurricane Coastal Impacts program, sponsored by the National Oceanographic Partnership Program (NOPP). The program aims to improve the understanding and prediction of coastal impacts of hurricanes to better protect coastal communities. By advancing forecasts of how tropical cyclones affect infrastructure, coastal erosion and water quality, the team is helping to develop more efficient and accurate warning systems. 

• Coordinate activities of COAWST-WSSSR project
• Develop the COAWST-WSSSR forecasting system for the Gulf of Mexico and U.S. East Coast
• Verify the forecasting system
• Develop the graphical user interface to visualize and disseminate project results

Funding

This Coupled Ocean Atmosphere Waves Sediment Transport – Waves, Sediment, Surge and Structure Response (COAWST – WSSSR) project is funded by the NOPP through the Office of Naval Research.

Publications and Reports

A case study and parametric analysis of predicting hurricane-induced building damage using data-driven machine learning approach

Development and application of an infragravity wave (InWave) driver to simulate nearshore processes

Warner, J.C., Olabarrieta, M., Using Directional Wave Spectra to improve extreme storm forecasts: Hurricane Idalia 2023. Ocean Sciences Meeting 2024, AGU, February 2024, 1 page. 

Klepac, S., Subgranon, A. & Olabarrieta. M. Data-driven machine learning predictions of multi-hazard hurricane damage to buildings. Poster presentation. Natural Hazards Research Summit, Washington DC (USA), October 2022, 1 page. 

Warner*, J.C., Olabarrieta, M., Hegermiller, C.A.. Development and Applications of a Coupled Ocean, Infragravity Wave, and Morphological Nearshore Prediction System. Oral presentation.  Ocean Sciences Meeting 2022, AGU, Online, February 2022, 1 page.