CranSLIK v2.0: improving the stochastic prediction of oil spill transport and fate using approximation methods

Rutherford, R.; Moulitsas, I.; Snow, B. J.; Kolios, A. J.; De Dominicis, M.

doi:https://doi.org/10.5194/gmd-8-3365-2015

Articles | Volume 8, issue 10

https://doi.org/10.5194/gmd-8-3365-2015

© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/gmd-8-3365-2015

© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 8, issue 10

Model description paper

|

26 Oct 2015

Model description paper |

| 26 Oct 2015

CranSLIK v2.0: improving the stochastic prediction of oil spill transport and fate using approximation methods

R. Rutherford, I. Moulitsas, B. J. Snow, A. J. Kolios, and M. De Dominicis

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Final revised paper (published on 26 Oct 2015)
Preprint (discussion started on 25 Jun 2015)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC C1536: 'Review of the ms gmd-2015-101CranSLIK v2.0: Improvements on the stochastic prediction of oil spill transport', Anonymous Referee #1, 28 Jul 2015
- AC C1879: 'Response to anonymous referee #1', Irene Moulitsas, 04 Sep 2015
RC C1730: 'Cranslik V2.0', Anonymous Referee #2, 17 Aug 2015
- AC C1880: 'Response to anonymous referee #2', Irene Moulitsas, 04 Sep 2015

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Irene Moulitsas on behalf of the Authors (04 Sep 2015)

ED: Publish as is (14 Oct 2015) by Ignacio Pisso

AR by Irene Moulitsas on behalf of the Authors (14 Oct 2015) Manuscript

Short summary

CranSLIK is a model that predicts the movement and spread of a surface oil spill at sea via a statistical approach that takes into account the random, and hence unpredictable, nature, of the affecting parameters. CranSLIK v2.0 demonstrated significant forecasting improvements by capturing the oil spill accurately in real oil spill validation cases and also proved capable of simulating a broader range of oil spill scenarios, while maintaining the run-time efficiency of the method.