Articles | Volume 17, issue 4
https://doi.org/10.5194/os-17-919-2021
© Author(s) 2021. 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-17-919-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Jul 2021
Research article |
| 15 Jul 2021
| 15 Jul 2021
Oil spill model uncertainty quantification using an atmospheric ensemble
Konstantinos Kampouris
CORRESPONDING AUTHOR
National and Kapodistrian University of Athens, Department of Physics, Section of
Environmental Physics and Meteorology, Athens, Greece
Vassilios Vervatis
National and Kapodistrian University of Athens, Department of Physics, Section of
Environmental Physics and Meteorology, Athens, Greece
John Karagiorgos
National and Kapodistrian University of Athens, Department of Physics, Section of
Environmental Physics and Meteorology, Athens, Greece
Sarantis Sofianos
National and Kapodistrian University of Athens, Department of Physics, Section of
Environmental Physics and Meteorology, Athens, Greece
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Cited
17 citations as recorded by crossref.
- Risk assessment of offshore oil spill in the northern South China Sea: A stochastic scenario modeling approach B. Geng et al. 10.3389/fmars.2022.986379
- A hybrid Monte Carlo simulation risk model for oil exploration projects E. Balas 10.1016/j.marpolbul.2023.115270
- Parameterization Method of Wind Drift Factor Based on Deep Learning in the Oil Spill Model F. Yu et al. 10.1007/s11802-023-5377-z
- Assessing ocean ensemble drift predictions by comparison with observed oil slicks V. de Aguiar et al. 10.3389/fmars.2023.1122192
- Satellite imagery in evaluating oil spill modelling scenarios for the Syrian oil spill crisis, summer 2021 P. Keramea et al. 10.3389/fmars.2023.1264261
- Performance diagnostics for probabilistic Lagrangian drift prediction V. de Aguiar et al. 10.1080/1755876X.2025.2538383
- Modeling the long-term transport and fate of oil spilled from the 2021 A Symphony tanker collision in the Yellow Sea, China: Reliability of the stochastic simulation R. Cao et al. 10.1016/j.ocemod.2023.102285
- Research on transport and weathering of oil spills in Jiaozhou Bight, China Z. Liu et al. 10.1016/j.rsma.2022.102197
- Lagrangian modelling of oil concentrations at sea: A sensitivity analysis to the grid resolution and number of Lagrangian elements A. Martínez et al. 10.1016/j.marpolbul.2023.115787
- Modeling chronic oil pollution from ships S. Liubartseva et al. 10.1016/j.marpolbul.2023.115450
- Modes of Operation and Forcing in Oil Spill Modeling: State-of-Art, Deficiencies and Challenges P. Keramea et al. 10.3390/jmse11061165
- Mapping pollution dynamics: utilizing GNOME to model oil spill trajectories in tanker terminals M. Farzingohar et al. 10.1007/s11356-024-33506-4
- Mathematical Physics Modelling and Prediction of Oil Spill Trajectory for a Catenary Anchor Leg Mooring (CALM) System X. Ju et al. 10.1155/2022/3909552
- Promoting best practices in ocean forecasting through an Operational Readiness Level E. Alvarez Fanjul et al. 10.3389/fmars.2024.1443284
- Variance-based sensitivity analysis of oil spill predictions in the Red Sea region M. Hammoud et al. 10.3389/fmars.2023.1185106
- Stochastic oil spill modeling for environmental protection at the Port of Taranto (southern Italy) S. Liubartseva et al. 10.1016/j.marpolbul.2021.112744
- Improved prediction of oil drift pattern using ensemble of ocean currents P. S.J et al. 10.1080/1755876X.2022.2147699
16 citations as recorded by crossref.
- Risk assessment of offshore oil spill in the northern South China Sea: A stochastic scenario modeling approach B. Geng et al. 10.3389/fmars.2022.986379
- A hybrid Monte Carlo simulation risk model for oil exploration projects E. Balas 10.1016/j.marpolbul.2023.115270
- Parameterization Method of Wind Drift Factor Based on Deep Learning in the Oil Spill Model F. Yu et al. 10.1007/s11802-023-5377-z
- Assessing ocean ensemble drift predictions by comparison with observed oil slicks V. de Aguiar et al. 10.3389/fmars.2023.1122192
- Satellite imagery in evaluating oil spill modelling scenarios for the Syrian oil spill crisis, summer 2021 P. Keramea et al. 10.3389/fmars.2023.1264261
- Performance diagnostics for probabilistic Lagrangian drift prediction V. de Aguiar et al. 10.1080/1755876X.2025.2538383
- Modeling the long-term transport and fate of oil spilled from the 2021 A Symphony tanker collision in the Yellow Sea, China: Reliability of the stochastic simulation R. Cao et al. 10.1016/j.ocemod.2023.102285
- Research on transport and weathering of oil spills in Jiaozhou Bight, China Z. Liu et al. 10.1016/j.rsma.2022.102197
- Lagrangian modelling of oil concentrations at sea: A sensitivity analysis to the grid resolution and number of Lagrangian elements A. Martínez et al. 10.1016/j.marpolbul.2023.115787
- Modeling chronic oil pollution from ships S. Liubartseva et al. 10.1016/j.marpolbul.2023.115450
- Modes of Operation and Forcing in Oil Spill Modeling: State-of-Art, Deficiencies and Challenges P. Keramea et al. 10.3390/jmse11061165
- Mapping pollution dynamics: utilizing GNOME to model oil spill trajectories in tanker terminals M. Farzingohar et al. 10.1007/s11356-024-33506-4
- Mathematical Physics Modelling and Prediction of Oil Spill Trajectory for a Catenary Anchor Leg Mooring (CALM) System X. Ju et al. 10.1155/2022/3909552
- Promoting best practices in ocean forecasting through an Operational Readiness Level E. Alvarez Fanjul et al. 10.3389/fmars.2024.1443284
- Variance-based sensitivity analysis of oil spill predictions in the Red Sea region M. Hammoud et al. 10.3389/fmars.2023.1185106
- Stochastic oil spill modeling for environmental protection at the Port of Taranto (southern Italy) S. Liubartseva et al. 10.1016/j.marpolbul.2021.112744
1 citations as recorded by crossref.
Latest update: 13 Sep 2025
Short summary
The wind is a source of uncertainty in oil spill modeling. We performed oil spill ensemble simulations using an atmospheric ensemble to quantify this uncertainty. We investigate the reliability of oil spill ensemble prediction used as an important forecasting tool to better plan mitigation procedures in the event of an oil spill.
The wind is a source of uncertainty in oil spill modeling. We performed oil spill ensemble...
