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GeoHydrodynamics and Environmental Research, University of Liege, Belgium
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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.282 253 - 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