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Martin, Paul
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Martin, Paul
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- PublicationOpen AccessSueper-Ensemble techniques: application to surface drift prediction during the DART06 and MREA07 campaigns(2009-11)
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ;Vanderbulcke, L.; GeoHydrodynamics and Environmental Research, University of Liege, Belgium ;Beckers, J.-M.; GeoHydrodynamics and Environmental Research, University of Liege, Belgium ;Lenartz, F.; GeoHydrodynamics and Environmental Research, University of Liege, Belgium ;Barth, A.; GeoHydrodynamics and Environmental Research, University of Liege, Belgium ;Poulain, P.-M.; stituto Nazionale di Oceanografia Sperimentale (OGS), Trieste, Italy ;Aidonindis, M.; ServiceIdrographique et Oceanographique de la marine, 13 rue du Chatelier, 29200 Brest, France ;Meyrat, J.; ServiceIdrographique et Oceanographique de la marine, 13 rue du Chatelier, 29200 Brest, France ;Ardhuin, F.; ServiceIdrographique et Oceanographique de la marine, 13 rue du Chatelier, 29200 Brest, France ;Fratianni, C.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Bologna, Bologna, Italia ;Tonani, M.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Bologna, Bologna, Italia ;Torrisi, L.; Servizio Meteorologico (Aeronautica Militare), Italy ;Pasquini, S.; Servizio Meteorologico (Aeronautica Militare), Italy ;Chiggiato, J.; ARPA Emilia Romagna, Servizio Idro Meteorologico, Bologna ;Tudor, M.; DHMZ Meteorological and Hydrological Service, Zagreb, Croatia ;Book, J.; US Naval Research Lab., 4555 Overlook Ave, SW, Washington, DC 20375 ;Martin, P.; US Naval Research Lab., 4555 Overlook Ave, SW, Washington, DC 20375 ;Allard, R.; US Naval Research Lab., 4555 Overlook Ave, SW, Washington, DC 20375 ;Peggion, G.; US Naval Research Lab., 4555 Overlook Ave, SW, Washington, DC 20375 ;Rixen, M.; NATO/SACLANT Undersea Research Centre, La Spezia, Italy; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; The prediction of the drift of floating objects is an important task, with applications such as marine transport, pollutant dispersion, and search-and-rescue activities. But forecasting surface drift is also very challenging, because it depends in a complex way on various interacting factors such as the wind, the ocean surface current, and the wave field. Furthermore, although each of the cited factors can be fore- casted by deterministic models, the latter all suffer from limitations, resulting in imperfect predictions. In the present study, we try and predict the drift of buoys launched during the DART06 (Dynamics of the Adriatic sea in Real-Time 2006) and MREA07 (Maritime Rapid Environmental Assessment 2007) sea trials, using the so-called hyper-ensemble technique: different models are combined in order to minimize departure from independent observations during a training period; the ob- tained combination is then used in forecasting mode. We review and try out different hyper-ensemble techniques, going from simple ensemble mean to techniques based on data assimilation, which dynamically update the model’s weights in the combi- nation when new observations become available. We show that the latter methods alleviate the need of fixing the training length a priori, as older information is au- tomatically discarded, and hence they lead to better results. Moreover, they allow to determine a characteristic time during which the model weights are more or less stable, which allows to predict how long the obtained combination will be valid in forecasting mode.282 254 - PublicationRestrictedBuilding an integrated enhanced virtual research environment metadata catalogue(2019-12-09)
; ; ; ; ; ; ; ; ; ; ; ; ; ;; ; ;Purpose The purpose of this paper is to boost multidisciplinary research by the building of an integrated catalogue or research assets metadata. Such an integrated catalogue should enable researchers to solve problems or analyse phenomena that require a view across several scientific domains. Design/methodology/approach There are two main approaches for integrating metadata catalogues provided by different e-science research infrastructures (e-RIs): centralised and distributed. The authors decided to implement a central metadata catalogue that describes, provides access to and records actions on the assets of a number of e-RIs participating in the system. The authors chose the CERIF data model for description of assets available via the integrated catalogue. Analysis of popular metadata formats used in e-RIs has been conducted, and mappings between popular formats and the CERIF data model have been defined using an XML-based tool for description and automatic execution of mappings. Findings An integrated catalogue of research assets metadata has been created. Metadata from e-RIs supporting Dublin Core, ISO 19139, DCAT-AP, EPOS-DCAT-AP, OIL-E and CKAN formats can be integrated into the catalogue. Metadata are stored in CERIF RDF in the integrated catalogue. A web portal for searching this catalogue has been implemented. Research limitations/implications Only five formats are supported at this moment. However, description of mappings between other source formats and the target CERIF format can be defined in the future using the 3M tool, an XML-based tool for describing X3ML mappings that can then be automatically executed on XML metadata records. The approach and best practices described in this paper can thus be applied in future mappings between other metadata formats. Practical implications The integrated catalogue is a part of the eVRE prototype, which is a result of the VRE4EIC H2020 project. Social implications The integrated catalogue should boost the performance of multi-disciplinary research; thus it has the potential to enhance the practice of data science and so contribute to an increasingly knowledge-based society. Originality/value A novel approach for creation of the integrated catalogue has been defined and implemented. The approach includes definition of mappings between various formats. Defined mappings are effective and shareable.125 6 - PublicationRestrictedImproved ocean prediction skill and reduced uncertainty in the coastal region from multi-model super-ensembles(2009)
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ;; ; ; ; ; ; ; ;The use of Multi-model Super-Ensembles (SE) which optimally combine different models, has been shown to significantly improve atmospheric weather and climate predictions. In the highly dynamic coastal ocean, the presence of small-scales processes, the lack of real-time data, and the limited skill of operational models at the meso-scale have so far limited the application of SE methods. Here, we report results from state-of-the-art super-ensemble techniques in which SEPTR (a trawl-resistant bottom mounted instrument platform transmitting data in near real-time) temperature profile data are combined with outputs from eight ocean models run in a coastal area during the Dynamics of the Adriatic in Real-Time (DART) experiment in 2006. New Kalman filter and particle filter based SE methods, which allow for dynamic evolution of weights and associated uncertainty, are compared to standard SE techniques and numerical models. Results show that dynamic SE are able to significantly improve prediction skill. In particular, the particle filter SE copes with non-Gaussian error statistics and provides robust and reduced uncertainty estimates.61 1