The effect of density stratification on the prediction of global storm surges

Kodaira, Tsubasa; Thompson, Keith R.; Bernier, Natacha B.

doi:10.1007/s10236-016-1003-6

Cited by 17 publications

(16 citation statements)

References 24 publications

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“…Thus, the inclusion of ocean baroclinicity allows the simulation to track the observations better over the course of the year as detailed in section . Although not directly comparable, to put the values of γ 2 computed on the nontidal residual in perspective, a previous study obtained γ 2 ≈0.75 − 0.85 in the PRVI region (Kodaira et al, ). This study conducted 1/12° resolution baroclinic global ocean model simulations between 15 August and 30 October 2014.…”

Section: Resultsmentioning

confidence: 96%

“…The following metrics are used to evaluate the performance of a model run at a particular tide gauge:

γ^{2} = \frac{v a r false(ζ_{m} - ζ_{o} false)}{v a r false(ζ_{o} false)},

S k i l l = 1 - \frac{\int_{0}^{T} {((), ζ_{m} - ζ_{o})}^{2} d t}{\int_{0}^{T} {((), false| ζ_{m} - \overset{ζ_{o}}{true} false| + false| ζ_{o} - \overset{ζ_{o}}{true} false|)}^{2} d t},

R M S E = {([], \frac{1}{T} \int_{0}^{T} {((), ζ_{m} - ζ_{o})}^{2} d t)}^{1 false/ 2},

in which T is the time period of assessment, the subscripts “ o ” and “ m ” represent observed and modeled values, respectively, the overline represents the temporal mean, and var represents the temporal variance. γ 2 (Kodaira et al, ) is the normalized variance of the error, where a value of 0 represents perfect agreement and a value of 1 represents no better than constant ( γ 2 can exceed 1), and Skill (Willmott, ) is an “index of agreement” measuring model skill (a value of 1 represents perfect agreement and a value of 0 represents complete disagreement). RMSE is the root‐mean squared‐error, which is composed of (linear) systematic ( RMSE s ) and (random) unsystematic ( RMSE u ) components (Willmott, ):

R M S E_{s} = {([], \frac{1}{T} \int_{0}^{T} {((), \overset{ζ_{m}}{true} - ζ_{o})}^{2} d t)}^{1 false/ 2} R M S E_{u} = ([], \frac{1}{T})

…”

Section: Resultsmentioning

confidence: 99%

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Baroclinic Coupling Improves Depth‐Integrated Modeling of Coastal Sea Level Variations Around Puerto Rico and the U.S. Virgin Islands

Pringle

Gonzalez-Lopez²,

Joyce

et al. 2019

JGR Oceans

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This study applies a baroclinic‐coupled depth‐integrated modeling system to the North Atlantic Ocean, where an unstructured mesh is used to focus resolution down to ∼30 m along the coasts of Puerto Rico and the U.S. Virgin Islands. Ocean baroclinicity is incorporated through one‐way coupling from operational data‐assimilated Global Ocean Forecasting System 3.1 temperature and salinity fields at just 12% additional computational time. The main objectives are to provide a comprehensive analysis of observed and modeled coastal sea levels (spanning from seasonal to supertidal variations) in Puerto Rico and the U.S. Virgin Islands during 2017 and to evaluate the associated model performance with and without baroclinic coupling at 14 National Oceanic and Atmospheric Administration/National Ocean Service tide gauges deployed in the region. It is found that baroclinic coupling increases modeled energy across the entire frequency spectrum, which is more commensurate with observations. In particular, density‐driven effects such as the seasonal cycle and sea level setdown due to trailing cold wakes from passing hurricanes are largely reproduced. Supertidal shelf‐resonant seiching at one to two cycles per hour is observed and modeled at a number of locations, where excitation of these modes is often promoted by the baroclinic coupling. Baroclinicity improves the yearlong model skill at every tide gauge, where the mean total skill is increased from 87% to 93% accuracy (54% to 85% for the nontidal residual). In September 2017 during Hurricanes Irma and Maria, baroclinicity increases model skill at 10 out of 14 tide gauges even when the barotropic mode is adjusted to have no mean offset from the observations.

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Section: Resultsmentioning

confidence: 96%

“…The following metrics are used to evaluate the performance of a model run at a particular tide gauge:

γ^{2} = \frac{v a r false(ζ_{m} - ζ_{o} false)}{v a r false(ζ_{o} false)},

S k i l l = 1 - \frac{\int_{0}^{T} {((), ζ_{m} - ζ_{o})}^{2} d t}{\int_{0}^{T} {((), false| ζ_{m} - \overset{ζ_{o}}{true} false| + false| ζ_{o} - \overset{ζ_{o}}{true} false|)}^{2} d t},

R M S E = {([], \frac{1}{T} \int_{0}^{T} {((), ζ_{m} - ζ_{o})}^{2} d t)}^{1 false/ 2},

R M S E_{s} = {([], \frac{1}{T} \int_{0}^{T} {((), \overset{ζ_{m}}{true} - ζ_{o})}^{2} d t)}^{1 false/ 2} R M S E_{u} = ([], \frac{1}{T})

…”

Section: Resultsmentioning

confidence: 99%

Baroclinic Coupling Improves Depth‐Integrated Modeling of Coastal Sea Level Variations Around Puerto Rico and the U.S. Virgin Islands

Pringle

Gonzalez-Lopez²,

Joyce

et al. 2019

JGR Oceans

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“…Moreover, the vertical velocity profile also fits the observations better when the wave forcing is included. Kodaira et al (2016) also employed NEMO, but here the model was set up on a global grid. The objective is to develop an operational forecast system for total water level, and to this end, a study is performed of global storm surges for the northern hemisphere autumn of 2014.…”

Section: The 14th Workhopmentioning

confidence: 99%

The 14th international workshop on wave hindcasting and forecasting and the 5th coastal hazards symposium

et al. 2017

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Following the 14th International Workshop on Wave Hindcasting and Forecasting and 5th Coastal Hazards Symposium in November 2014 in Key West, Florida, a topical collection has appeared in recent issues of Ocean Dynamics. Here, we give a brief overview of the 16 papers published in this topical collection as well as an overview of the widening scope of the conference in recent years. A general trend in the field has been towards closer integration between the wave and ocean modelling communities. This is also seen in this topical collection, with several papers exploring the interaction between surface waves and mixed layer dynamics and sea ice.

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“…The dynamic aspects of surge have typically been assessed through hydrodynamic modeling studies, with applications in recreating historical surge events [Bilskie et al, 2016a;Yin et al, 2016], understanding causal mechanisms [Haigh et al, 2014b;Kodaira et al, 2016;Lapetina and Sheng, 2015], and estimating the implications of future changes to those events [Bilskie et al, 2014[Bilskie et al, , 2016bIzuru et al, 2015;Oey and Chou, 2016]. An advantage of these studies is that by solving the governing equations of fluid motion, they preserve faithfulness to the underlying physics and often allow realistic depiction of real-world storm surge events, including inundation across normally dry coastal areas.…”

Section: Introductionmentioning

confidence: 99%

A basis function approach for exploring the seasonal and spatial features of storm surge events

Westra

Leonard

2017

Geophysical Research Letters

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Storm surge is a significant contributor to flooding in coastal and estuarine regions. To represent the statistical characteristics of storm surge over a climatologically diverse region, we propose the use of basis functions that capture the temporal progression of individual storm surge events. This extends statistical analyses of surge from considering only the peak to a more multifaceted approach that also includes decay rate and duration. Our results show that there is seasonal variation in storm surge along the Australian coastline. During the dominant storm surge seasons, the peak and duration of storm surge events tend to increase simultaneously at a number of locations, with implications for flood damage assessments and evacuation planning. By combining the dynamic and statistical features of storm surge, it is possible to better understand the factors that can lead to flood risk along the coastline, including estuarine areas that are also affected by fluvial floods.

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The effect of density stratification on the prediction of global storm surges

Cited by 17 publications

References 24 publications

Baroclinic Coupling Improves Depth‐Integrated Modeling of Coastal Sea Level Variations Around Puerto Rico and the U.S. Virgin Islands

Baroclinic Coupling Improves Depth‐Integrated Modeling of Coastal Sea Level Variations Around Puerto Rico and the U.S. Virgin Islands

The 14th international workshop on wave hindcasting and forecasting and the 5th coastal hazards symposium

A basis function approach for exploring the seasonal and spatial features of storm surge events

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