Predictability of storm wave heights in the ice-free Beaufort Sea

Nose, Takehiko; Webb, Adrean; Waseda, Takuji; Inoue, Jun; Sato, Kazutoshi

doi:10.1007/s10236-018-1194-0

Cited by 19 publications

(21 citation statements)

References 57 publications

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“…They concluded that the wave model results are critically sensitive to the spatial resolution and less sensitive to the temporal resolution of the meteorological input data. Similar analyses have been conducted both globally (Feng et al, 2006) and for coastal areas such as that around the Mediterranean Sea (Cavaleri and Bertotti, 2003a;Cavaleri andBertotti, 2003b, 2004;Signell et al, 2005;Bolaños-Sanchez et al, 2007; de León and Soares, 2008; de León et al, 2012), the Caribbean Sea and Gulf of Mexico (Appendini et al, 2013), the Black Sea (Van Vledder and Akpınar, 2015) and the Beaufort Sea (Nose et al, 2018) but not for the area of interest in the present study, i.e. that around the North and Baltic seas, and with the wind data available at present.…”

Section: Introductionmentioning

confidence: 71%

Synergy of wind wave model simulations and satellite observations during extreme events

et al. 2018

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Abstract. In this study, the quality of wave data provided by the new Sentinel-3A satellite is evaluated and the sensitivity of the wave model to wind forcing is tested. We focus on coastal areas, where altimeter data are of lower quality and wave modelling is more complex than for the open ocean. In the first part of the study, the sensitivity of the wave model to wind forcing is evaluated using data with different temporal and spatial resolution, such as ERA-Interim and ERA5 reanalyses, the European Centre for Medium-Range Weather Forecasts (ECMWF) operational analysis and short-range forecasts, German Weather Service (DWD) forecasts and regional atmospheric model simulations (coastDat). Numerical simulations show that the wave model forced using the ERA5 reanalyses and that forced using the ECMWF operational analysis/forecast demonstrate the best capability over the whole study period, as well as during extreme events. To further estimate the variance of the significant wave height of ensemble members for different wind forcings, especially during extreme events, an empirical orthogonal function (EOF) analysis is performed. In the second part of the study, the satellite data of Sentinel-3A, Jason-2 and CryoSat-2 are assessed in comparison with in situ measurements and spectral wave model (WAM) simulations. Intercomparisons between remote sensing and in situ observations demonstrate that the overall quality of the former is good over the North Sea and Baltic Sea throughout the study period, although the significant wave heights estimated based on satellite data tend to be greater than the in situ measurements by 7 to 26 cm. The quality of all satellite data near the coastal area decreases; however, within 10 km off the coast, Sentinel-3A performs better than the other two satellites. Analyses in which data from satellite tracks are separated in terms of onshore and offshore flights have been carried out. No substantial differences are found when comparing the statistics for onshore and offshore flights. Moreover, no substantial differences are found between satellite tracks under various metocean conditions. Furthermore, the satellite data quality does not depend on the wind direction relative to the flight direction. Thus, the quality of the data obtained by the new Sentinel-3A satellite over coastal areas is improved compared to that of older satellites.

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Section: Introductionmentioning

confidence: 71%

Synergy of wind wave model simulations and satellite observations during extreme events

et al. 2018

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“…The effect of SIC uncertainty on wave predictions was investigated by a hindcast experiment using The Arctic Ocean wave 130 model developed at the University of Tokyo (TodaiWW3-ArCS) based on WW3, which was introduced in Nose et al (2018).…”

Section: Third-generation Spectral Wave Modelmentioning

confidence: 99%

“…The grid was defined using the International Bathymetry Chart of the Arctic Ocean bathymetry (Jakobsson et al, 2012) and the Global Self-consistent, Hierarchical, High-resolution Geography shoreline data 150 (Wessel and Smith, 1996), and there are approximately 301,535 sea point cells. During the version upgrade of TodaiWW3-ArCS, s wind and s dissipation parameterisations and wind forcing were sanity checked against the 2016 September storm when the model and observations agreed well (Nose et al, 2018). We compared the leading packages, ST4 (Ardhuin et al, 2010;Rascle and Ardhuin, 2013) and ST6 (Rogers et al, 2012;Zieger et al, 2015;Liu et al, 2019), using ECMWF global reanalysis (ERA5) 10 m wind (U 10 ).…”

Section: Third-generation Spectral Wave Modelmentioning

confidence: 99%

Satellite retrieved sea ice concentration uncertainty and its effect on modelling wave evolution in marginal ice zones

Nose

Waseda

Kodaira

et al. 2019

Preprint

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Satellite retrieved Sea Ice Concentration (SIC) uncertainty is studied with respect to its effect on spectral wave modelling of ice-covered water. Eight SIC products based on four algorithms applied to SSMIS and AMSR2 data were analysed.They were compared with sea-truth images captured from a 12 day fixed Marginal Ice Zone (MIZ) transect observation during the November 2018 R/V Mirai expedition in the Chukchi Sea. The analysis shows the refreezing sea ice field is highly variable in time and space, and the uncertainty of SIC estimates is considerable. A wave hindcast experiment for the observation period 5 using these SIC products as model forcing has shown that the SIC uncertainty translates to wave prediction discrepancies in ice cover. There is evidence that bivariate uncertainty data (model significant wave heights and SIC forcing) are correlated, although off-ice wave growth is more complicated due to the cumulative effect of SIC uncertainty and the model implementation of wave-ice interactions along an MIZ fetch. Analysis of significant wave height uncertainty distributions for SIC forcing and wave-ice interaction source terms reveals that they are both sizeable; however, the study concludes the more dominant 10 uncertainty source of modelling wave-ice interactions is the accuracy of satellite retrieved SIC estimates that are used as model forcing.

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“…In contrast, the ocean wave field in the third quadrant (rear left area relative to the propagation direction) of the EC is composed of swell and wind-sea systems propagating in different directions. Nose et al (2018) consider the sea-state predictability in an ice-free Beaufort Sea in two cases from the fall of 2016 and conclude that, although ice coverage influenced the results through changes of the fetch, the most important factor was the quality of the wind fields. This highlights the difficulty in correctly modeling the atmosphere in an area with rapid ice growth.…”

Section: The 1st Wave Surge and Coastal Hazards Workhopmentioning

confidence: 99%

The 1st International Workshop on Waves, Storm Surges and Coastal Hazards incorporating the 15th International Workshop on Wave Hindcasting and Forecasting

et al. 2019

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Following the 1st International Workshop on Waves, Storm Surges and Coastal Hazards, which incorporated the 15th session of the long-standing the International Workshop on Wave Hindcasting and Forecasting, in September 2017 in Liverpool, United Kingdom, a topical collection has appeared in recent issues of Ocean Dynamics. Here, we give a brief overview of the 15 papers published in this topical collection as well as an overview of the widening scope of the conference in recent years. The continuing trend towards closer integration between the wave and ocean modeling communities is reflected in this workshop, culminating in the renaming of the workshop, while maintaining the connection with the three-decade long history of the wave workshop. This is also seen in this topical collection, with several papers exploring wave-generated storm surge, wave-tide contributions to coastal flooding, forcing a global ocean model with fluxes from a wave model and interaction between surface waves and sea ice.

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Predictability of storm wave heights in the ice-free Beaufort Sea

Cited by 19 publications

References 57 publications

Synergy of wind wave model simulations and satellite observations during extreme events

Synergy of wind wave model simulations and satellite observations during extreme events

Satellite retrieved sea ice concentration uncertainty and its effect on modelling wave evolution in marginal ice zones

The 1st International Workshop on Waves, Storm Surges and Coastal Hazards incorporating the 15th International Workshop on Wave Hindcasting and Forecasting

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