4D.2 Incorporating Rainfall into Storm Surge Forecasting for Hurricane Irma

Monday, 16 April 2018: 4:15 PM
Heritage Ballroom (Sawgrass Marriott)
Kyra Bryant, Tennessee State Univ., Nashville, TN; and A. Alghamdi, A. Musinguzi, and M. Akbar

As increasing sea surface temperatures pave the way for more powerful hurricanes, and population growth remains unwavered in low-elevation coastal zones, the time is certainly ripe for accurate hurricane storm surge prediction. Emergency management officials need a reliable model to properly minimize loss of life, which also benefits authorities in preventing and limiting risks when designing coastal structure protection. A reliable model accurately portrays each parameter associated with hurricane storm surge.

As witnessed with Hurricane Harvey, a hurricane extends far beyond the wind-based Saffir-Simpson scale. The most powerful havoc occurred at its weakest moment as a storm. Tropical Storm Harvey’s rainfall wreaked widespread flooding in southeast Texas, with up to $180 billion in overall damages. Yet some hurricane storm surge modeling programs lack a rain source term. This study employs rain, wind speed, and pressure data from the numerical weather prediction system Weather Research and Forecasting (WRF) with the Computation and Modeling Engineering Laboratory (CaMEL) to model Hurricane Irma’s storm surge. First, the model simulates Hurricane Irma without a rainfall parameter, and an ADvanced CIRCulation (ADCIRC) simulation is reproduced for validation. Second, Hurricane Irma is simulated in CaMEL applying a rain input from WRF. All three cases are compared to observational data collected from various NOAA stations along the Puerto Rico and Florida coasts. Hurricane Harvey and Hurricane Maria will eventually be simulated as well. Since hurricanes cannot be tamed, a more accurate model that includes rain is the only path to avoiding their destruction.

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