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Discussion papers
https://doi.org/10.5194/os-2019-76
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-2019-76
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 26 Jul 2019

Submitted as: research article | 26 Jul 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Ocean Science (OS).

Ensemble hindcasting of wind and wave conditions with WRF and Wavewatch III® driven by ERA5

Robert Daniel Osinski and Hagen Radtke Robert Daniel Osinski and Hagen Radtke
  • Leibniz Institute for Baltic Sea Research Warnemünde, Seestrasse 15, 18119 Rostock, Germany

Abstract. When hindcasting wave fields of storm events with state-of-the-art wave models, the quality of the results strongly depends on the meteorological forcing dataset. The wave model will inherit the uncertainty of the atmospheric data, and additional discretisation errors will be introduced due to a limited spatial and temporal resolution of the forcing data. In this study, we demonstrate that applying an atmospheric downscaling with the atmospheric mesoscale model WRF can address all these three issues. Not only does it add regional detail to the wind field and can increase the temporal resolution of the wind fields, which gives a more detailed representation of transient events such as storms. It can also be used to generate ensembles with perturbed atmospheric conditions which allow for a flow dependent and spatiotemporally variable uncertainty estimation. We test different strategies to generate an ensemble hindcast of a storm event in February 2002 in the Baltic Sea, which provoked a severe storm surge. The WRF model used for this purpose is driven by the ECMWF ERA5 reanalysis, and wind fields are passed to the third-generation wave model Wavewatch III®. A combination of initial conditions from the ERA5 ensemble of data assimilations and stochastic pertubations during runtime is identified as the most promising strategy. The final aim of the ensemble approach is to quantify the hindcast error, but this approach can also be used to generate alternative representations of historical extreme events to sample the recent climate and to increase the sample size for statistical studies, such as for civil engineering applications for coastal protection studies.

Robert Daniel Osinski and Hagen Radtke
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Robert Daniel Osinski and Hagen Radtke
Robert Daniel Osinski and Hagen Radtke
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Latest update: 23 Aug 2019
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Short summary
The idea of this study is to quantify the uncertainty in hindcasts of severe storm events by applying a state-of-the-art ensemble generation technique. Other ensemble generation techniques are tested. The atmospheric WRF model is driven by the ERA5 reanalysis. A setup of the Wavewatch III® wave model for the Baltic Sea is used with the wind fields produced with the WRF ensemble. The effect of different spatiotemporal resolutions of the wind fields on the significant wave height is investigated.
The idea of this study is to quantify the uncertainty in hindcasts of severe storm events by...
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