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You are here: Home / Library / RBINS Staff Publications 2020 / NOOS-Drift, an innovative operational transnational multi-model ensemble system to assess ocean drift forecast accuracy.

Sébastien Legrand, Knut-Frode Dagestad, Pierre Daniel, Michel Kapel and Samuel Orsi (2020)

NOOS-Drift, an innovative operational transnational multi-model ensemble system to assess ocean drift forecast accuracy.

Webpublished.

In case of maritime pollution, man-overboard, or objects adrift at sea, national maritime authorities of the 9 countries bordering the European North West Continental Shelf (NWS) rely on drift model simulations in order to better understand the situation at stake and plan the best response strategy. So far, the drift forecast services are mainly managed at national levels with almost no integration at the transnational level. Designed as a support service to the national drift forecasting services, NOOS-Drift has the ambition to change this paradigm. NOOS-Drift is a distributed transnational multi-model ensemble system to assess and improve drift forecast accuracy in the European North West Continental Shelf. Developed as a one-stop-shop web service, the service allows registered users (national drift model operators or trained maritime authorities) to submit on-demand drift simulation requests to be run by all the national drift forecasting services connected to NOOS-Drift. Within 15 minutes after activation, the NOOS-Drift users shall get access to the drift simulation results of the individual ensemble members, as well as the results of a multi-models joint analysis assessing the ensemble spread and delineating risk areas to locate possible maritime pollution. This operation of such a distributed multi-models service is to our knowledge a world premiere. In this communication, we will present the technical and scientific developments that had to be done to make this service possible, including: - a robust, secure and latency-free communication system that coordinates the execution of the different national models - a strategy to build the multi-model ensemble - a definition of drift forecast accuracy - the joint multi-model analysis tools - the standard file formats and visualisation means. Finally we will illustrate on an example how the NOOS-Drift service could change the decision making process.
Abstract of an Oral Presentation or a Poster
Abstract and presentation published for EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12653, https://doi.org/10.5194/egusphere-egu2020-12653

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