Global modeling of tropical cyclone storm surges using high-resolution forecasts
by N. Bloemendaal (Vrije Universiteit Amsterdam), S. Muis (Vrije Universiteit Amsterdam), R.J. Haarsma (KNMI), M. Verlaan (), M.I. Apecechea ()H. de Moel ()P.J. Ward ()J.C.J.H. Aerts ()
We assess the suitability of ECMWF Integrated Forecasting System (IFS) data for the global modeling of tropical cyclone (TC) storm surges. We extract meteorological forcing from the IFS at a 0.225° horizontal resolution for eight historical TCs and simulate the corresponding surges using the global tide and surge model. Maximum surge heights for Hurricanes Irma and Sandy are compared with tide gauge observations, with R2-values of 0.86 and 0.74 respectively. Maximum surge heights for the other TCs are in line with literature. Our case studies demonstrate that a horizontal resolution of 0.225° is sufficient for the large-scale modeling of TC surges. By upscaling the meteorological forcing to coarser resolutions as low as 1.0°, we assess the effects of horizontal resolution on the performance of surge modeling. We demonstrate that coarser resolutions result in lower-modeled surges for all case studies, with modeled surges up to 1 m lower for Irma and Nargis. The largest differences in surges between the different resolutions are found for the TCs with the highest surges. We discuss possible drivers of maximum surge heights (TC size, intensity, and coastal slope and complexity), and find that coastal complexity and slope play a more profound role than TC size and intensity alone. The highest surges are found in areas with complex coastlines (fractal dimension > 1.10) and, in general, shallow coastlines. Our findings show that using high-resolution meteorological forcing is particularly beneficial for areas prone to high TC surges, since these surges are reduced the most in coarse-resolution datasets.
Bloemendaal, N., S. Muis, R.J. Haarsma, M. Verlaan, M.I. Apecechea, H. de Moel, P.J. Ward and J.C.J.H. Aerts, Global modeling of tropical cyclone storm surges using high-resolution forecasts