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Stoner, Richard
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- PublicationRestrictedInvestigating the Mediterranean by seafloor observations: the Eastern branch of the EMSO Ligurian Sea node(2015-05-21)
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ;Locritani, Marina; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Roma2, Roma, Italia ;Beranzoli, Laura; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Roma2, Roma, Italia ;Carmisciano, Cosmo; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Roma2, Roma, Italia ;Embriaco, Davide; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Roma2, Roma, Italia ;Muccini, Filippo; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Roma2, Roma, Italia ;Favali, Paolo; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Roma2, Roma, Italia ;Aguzzi, Jacopo; Consejo Superior de Investigaciones Scientifica ;Benedetti, Alessandro; CNR-IENI ;Liggieri, Libero; CNR-IENI ;Ciuffardi, Tiziana; ENEA ;Cocito, Silvia; ENEA ;Delfanti, Roberta; ENEA ;Fanelli, Emanuela; ENEA ;Peirano, Andrea; ENEA ;Coelho, Emanuel F.; NATO STO CMRE ;Stoner, Richard; NATO STO CMRE ;Dialti, Lorenzo; IIM ;Pizzeghello, Nicola; IIM ;Marini, Davide; DLTM ;Martinelli, Andrea; DLTM ;Stroobant, Mascha; DLTM ;Marini, Simone; CNR-ISMAR ;Vetrano, Anna; CNR-ISMAR ;Povero, Paolo; UNIGE-DISTAV ;Stifani, Mirko; CSSN; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; The overall objective of this proposal is to build an advanced and original prototype specifically devoted to seafloor and water-column monitoring as starting Italian contribution to the further development of the EMSO Ligurian Sea node. In detail the aim of the observatory is to ensure realtime continuous acquisition of geophysical, oceanographic and biological data by a cable system from a marine depth of about 500 m to the shore station.202 30 - 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