Ocean Science
Ocean Science
OS
Articles | Volume 19, issue 2
Articles | Volume 19, issue 2
https://doi.org/10.5194/os-19-485-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-19-485-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 2
Research article
 | 
21 Apr 2023
Research article |  | 21 Apr 2023

An analogues-based forecasting system for Mediterranean marine-litter concentration

Gabriel Jordà and Javier Soto-Navarro

Data sets

An analogue based forecasting system for Mediterranean marine litter concentration - Code and simulations dataset Gabriel Jordà and Javier Soto-Navarro https://doi.org/10.5281/zenodo.7104402

The ERA5 global reanalysis (https://climate.copernicus.eu/climate-reanalysis) Hans Hersbach, Bill Bell, Paul Berrisford, Shoji Hirahara, András Horányi, Joaquín Muñoz-Sabater, Julien Nicolas, Carole Peubey, Raluca Radu, Dinand Schepers, Adrian Simmons, Cornel Soci, Saleh Abdalla, Xavier Abellan, Gianpaolo Balsamo, Peter Bechtold, Gionata Biavati, Jean Bidlot, Massimo Bonavita, Giovanna De Chiara, Per Dahlgren, Dick Dee, Michail Diamantakis, Rossana Dragani, Johannes Flemming, Richard Forbes, Manuel Fuentes, Alan Geer, Leo Haimberger, Sean Healy, Robin J. Hogan, Elías Hólm, Marta Janisková, Sarah Keeley, Patrick Laloyaux, Philippe Lopez, Cristina Lupu, Gabor Radnoti, Patricia de Rosnay, Iryna Rozum, Freja Vamborg, Sebastien Villaume, and Jean-Noël Thépaut https://doi.org/10.1002/qj.3803

Model code and software

An analogue based forecasting system for Mediterranean marine litter concentration - Code and simulations dataset Gabriel Jordà and Javier Soto-Navarro https://doi.org/10.5281/zenodo.7104402

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Short summary
We develop a forecasting system for marine-litter concentration (MLC) in the Mediterranean based on a simple statistical method. The idea is that similar meteorological situations yield similar MLC patterns. We train our model with a historical meteorological dataset and MLCs from numerical simulations to recognize these situations and patterns and use them to forecast the future MLC. The results are promising; the approach has potential to become a suitable, cost-effective forecasting method.
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