Articles | Volume 16, issue 2
https://doi.org/10.5194/os-16-355-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-16-355-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Ensemble hindcasting of wind and wave conditions with WRF and WAVEWATCH III® driven by ERA5
Robert Daniel Osinski
CORRESPONDING AUTHOR
Leibniz Institute for Baltic Sea Research Warnemünde, Physical Oceanography and Instrumentation, Seestrasse 15, 18119 Rostock, Germany
Hagen Radtke
Leibniz Institute for Baltic Sea Research Warnemünde, Physical Oceanography and Instrumentation, Seestrasse 15, 18119 Rostock, Germany
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19 citations as recorded by crossref.
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- Baltic sea wave climate in 1979–2018: Numerical modelling results A. Sokolov & B. Chubarenko 10.1016/j.oceaneng.2024.117088
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- Urban Microplastics Emissions: Effectiveness of Retention Measures and Consequences for the Baltic Sea G. Schernewski et al. 10.3389/fmars.2021.594415
- Evaluation of HRCLDAS and ERA5 Datasets for Near-Surface Wind over Hainan Island and South China Sea Y. Jiang et al. 10.3390/atmos12060766
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- Emission, Transport, and Deposition of visible Plastics in an Estuary and the Baltic Sea—a Monitoring and Modeling Approach G. Schernewski et al. 10.1007/s00267-021-01534-2
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- A fast simulation method for the probabilistic assessment of emissions in cruise ship’s itinerary planning L. Braidotti et al. 10.1016/j.oceaneng.2024.118804
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- Feasibility of ERA5 reanalysis wind dataset on wave simulation for the western inner-shelf of Yellow Sea X. Feng & X. Chen 10.1016/j.oceaneng.2021.109413
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- Effect of environmental data uncertainty in the framework of second generation intact stability criteria G. Bulian & A. Orlandi 10.1016/j.oceaneng.2022.111253
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- Optimized WAVEWATCH Ⅲ for significant wave height computation using machine learning L. Zhang et al. 10.1016/j.oceaneng.2024.119004
- Numerical Simulation of Wind Wave Using Ensemble Forecast Wave Model: A Case Study of Typhoon Lingling M. Roh et al. 10.3390/jmse9050475
19 citations as recorded by crossref.
- Transport and Behavior of Microplastics Emissions From Urban Sources in the Baltic Sea G. Schernewski et al. 10.3389/fenvs.2020.579361
- Swell Generation Under Extra‐Tropical Storms M. Hell et al. 10.1029/2021JC017637
- Baltic sea wave climate in 1979–2018: Numerical modelling results A. Sokolov & B. Chubarenko 10.1016/j.oceaneng.2024.117088
- How does the selection of wave hindcast datasets and statistical models influence the probabilistic design of offshore scour protections? R. Figueiredo et al. 10.1016/j.oceaneng.2022.113123
- Urban Microplastics Emissions: Effectiveness of Retention Measures and Consequences for the Baltic Sea G. Schernewski et al. 10.3389/fmars.2021.594415
- Evaluation of HRCLDAS and ERA5 Datasets for Near-Surface Wind over Hainan Island and South China Sea Y. Jiang et al. 10.3390/atmos12060766
- Microplastics in Combined Sewer Overflows: An Experimental Study F. Di Nunno et al. 10.3390/jmse9121415
- Emission, Transport, and Deposition of visible Plastics in an Estuary and the Baltic Sea—a Monitoring and Modeling Approach G. Schernewski et al. 10.1007/s00267-021-01534-2
- Verification of Forecast Performance of a Rapid Refresh Wave Model Based on Wind–Wave Interaction Effect M. Roh et al. 10.3390/jmse9111230
- Ensemble hindcasting of winds and waves for the coastal and oceanic region of Southern Brazil G. Correia et al. 10.1016/j.cageo.2024.105658
- A fast simulation method for the probabilistic assessment of emissions in cruise ship’s itinerary planning L. Braidotti et al. 10.1016/j.oceaneng.2024.118804
- Model uncertainties of a storm and their influence on microplastics and sediment transport in the Baltic Sea R. Osinski et al. 10.5194/os-16-1491-2020
- Feasibility of ERA5 reanalysis wind dataset on wave simulation for the western inner-shelf of Yellow Sea X. Feng & X. Chen 10.1016/j.oceaneng.2021.109413
- Hazardous substances in the sediments and their pathways from potential sources in the eastern Gulf of Finland I. Kuprijanov et al. 10.1016/j.marpolbul.2021.112642
- A convection-permitting and limited-area model hindcast driven by ERA5 data: precipitation performances in Italy V. Capecchi et al. 10.1007/s00382-022-06633-2
- Effect of environmental data uncertainty in the framework of second generation intact stability criteria G. Bulian & A. Orlandi 10.1016/j.oceaneng.2022.111253
- A framework for implementing structural integrity management of an aging fixed offshore platform using wave modeling for risk-based underwater inspection provision A. Alizadeh et al. 10.1016/j.oceaneng.2024.118368
- Optimized WAVEWATCH Ⅲ for significant wave height computation using machine learning L. Zhang et al. 10.1016/j.oceaneng.2024.119004
- Numerical Simulation of Wind Wave Using Ensemble Forecast Wave Model: A Case Study of Typhoon Lingling M. Roh et al. 10.3390/jmse9050475
Latest update: 03 Nov 2024
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 spatio-temporal 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...