Preprints
https://doi.org/10.5194/egusphere-2022-832
https://doi.org/10.5194/egusphere-2022-832
 
28 Sep 2022
28 Sep 2022
Status: this preprint is open for discussion.

An analogue based forecasting system for Mediterranean marine litter concentration

Gabriel Jordà1,2, and Javier Soto-Navarro3, Gabriel Jordà and Javier Soto-Navarro
  • 1Centre Oceanogràfic de les Balears, Spanish Institute of Oceanography (CN-IEO/CSIC). Mallorca, 07015, Spain
  • 2University of the Balearic Islands (UIB). Mallorca, 07122, Spain
  • 3Physical Oceanography Group of the University of Málaga (GOFIMA). Málaga, 29071, Spain
  • These authors contributed equally to this work.

Abstract. In this work we explore the performance of a statistical forecasting system for marine litter (ML) concentration in the Mediterranean Sea. In particular, we assess the potential skills of a system based on the analogues method. The system uses a historical database of ML concentration simulated by a high resolution realistic model and is trained to identify meteorological situations in the past that are similar to the forecasted ones. Then, the corresponding ML concentrations of the past analog days are used to construct the ML concentration forecast. Due to the scarcity of observations, the forecasting system has been validated against a synthetic reality (i.e. the outputs from a ML modelling system). Different approaches have been tested to refine the system and the results show that using integral definitions for the similarity function, based on the history of the meteorological situation, improves the system performance. We also find that the system accuracy depends on the region of application being better for larger regions. Also, the method performs well to capture the spatial patterns but performs worse to capture the temporal variability, specially the extreme values. Despite the inherent limitations of using a synthetic reality to validate the system, the results are promising and the approach has potential to become a suitable cost effective forecasting method for ML concentration.

Gabriel Jordà and Javier Soto-Navarro

Status: open (until 25 Dec 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Gabriel Jordà and Javier Soto-Navarro

Gabriel Jordà and Javier Soto-Navarro

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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 MLC from numerical simulations to recognize these situations and patterns, and use them to forecast the future MLC. The results are promising and the approach has potential to become a suitable cost effective forecasting method.