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Servizio Meteorologico (Aeronautica Militare), Italy
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- PublicationRestrictedImpact of Sea Surface Temperature on COSMO Forecasts of a Medicane over the Western Mediterranean Sea(2015)
; ; ; ; ; ; ; ; ; ; ; The paper describes and analyzes the sensitivity of an operational atmospheric model to different SST (sea surface temperature) estimates. The model’s sensitivity has been analyzed in a Medicane (Mediterranean hurricane) test case. Numerical simulations have been performed using the COSMO (consortium for small-scale modeling) atmospheric model, in the COSMO-ME configuration. The model results show that the model is capable of capturing the position, timing and intensity of the cyclone. Sensitivity experiments have been carried out using different SSTs surface boundary conditions for the COSMO forecasts. Four different experiments have been carried out: the first two using SST fields obtained from the OSTIA (operational sea surface temperature and sea ice analysis) system, while the other two using the SST analyses and forecasts from MFS (Mediterranean Forecasting System, Tonani et al., 2015, Pinardi and Coppini, 2010). The different boundary conditions determine differences in the trajectory, pressure minimum and wind intensity of the simulated Medicane. The sensitivity experiments showed that a colder than real SST field determines a weakening of the minimum pressure at the vortex center. MFS SST analyses and forecasts allow the COSMO model to simulate more realistic minimum pressure values, trajectories and wind speeds. It was found that MFS SST forecast, as surface boundary conditions for COSMO-ME runs, determines a significant improvement, compared to ASCAT observations, in terms of wind intensity forecast as well as cyclone dimension and location.158 1 - 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