The Practical Predictability of Storm Tide from Tropical Cyclones in the Gulf of Mexico

Kathryn R. Fossell National Center for Atmospheric Research, Boulder, Colorado

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David Ahijevych National Center for Atmospheric Research, Boulder, Colorado

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Rebecca E. Morss National Center for Atmospheric Research, Boulder, Colorado

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Chris Snyder National Center for Atmospheric Research, Boulder, Colorado

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Chris Davis National Center for Atmospheric Research, Boulder, Colorado

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Abstract

The potential for storm surge to cause extensive property damage and loss of life has increased urgency to more accurately predict coastal flooding associated with landfalling tropical cyclones. This work investigates the sensitivity of coastal inundation from storm tide (surge + tide) to four hurricane parameters—track, intensity, size, and translation speed—and the sensitivity of inundation forecasts to errors in forecasts of those parameters. An ensemble of storm tide simulations is generated for three storms in the Gulf of Mexico, by driving a storm surge model with best track data and systematically generated perturbations of storm parameters from the best track. The spread of the storm perturbations is compared to average errors in recent operational hurricane forecasts, allowing sensitivity results to be interpreted in terms of practical predictability of coastal inundation at different lead times. Two types of inundation metrics are evaluated: point-based statistics and spatially integrated volumes. The practical predictability of surge inundation is found to be limited foremost by current errors in hurricane track forecasts, followed by intensity errors, then speed errors. Errors in storm size can also play an important role in limiting surge predictability at short lead times, due to observational uncertainty. Results show that given current mean errors in hurricane forecasts, location-specific surge inundation is predictable for as little as 12–24 h prior to landfall, less for small-sized storms. The results also indicate potential for increased surge predictability beyond 24 h for large storms by considering a storm-following, volume-integrated metric of inundation.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Kathryn Fossell, fossell@ucar.edu

Abstract

The potential for storm surge to cause extensive property damage and loss of life has increased urgency to more accurately predict coastal flooding associated with landfalling tropical cyclones. This work investigates the sensitivity of coastal inundation from storm tide (surge + tide) to four hurricane parameters—track, intensity, size, and translation speed—and the sensitivity of inundation forecasts to errors in forecasts of those parameters. An ensemble of storm tide simulations is generated for three storms in the Gulf of Mexico, by driving a storm surge model with best track data and systematically generated perturbations of storm parameters from the best track. The spread of the storm perturbations is compared to average errors in recent operational hurricane forecasts, allowing sensitivity results to be interpreted in terms of practical predictability of coastal inundation at different lead times. Two types of inundation metrics are evaluated: point-based statistics and spatially integrated volumes. The practical predictability of surge inundation is found to be limited foremost by current errors in hurricane track forecasts, followed by intensity errors, then speed errors. Errors in storm size can also play an important role in limiting surge predictability at short lead times, due to observational uncertainty. Results show that given current mean errors in hurricane forecasts, location-specific surge inundation is predictable for as little as 12–24 h prior to landfall, less for small-sized storms. The results also indicate potential for increased surge predictability beyond 24 h for large storms by considering a storm-following, volume-integrated metric of inundation.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Kathryn Fossell, fossell@ucar.edu
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