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| PO26: | Lagrangian Predictability and Data Assimilation |
| Sponsor: |
Physical Oceanography
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| CoSponsor: |
Interdisciplinary |
| Convener: |
A. D. Kirwan University of Delaware Newark, DE, USA adk@udel.edu Tamay Ozgokmen RSMAS, UNIVERSITY OF MIAMI Miami, FL, USA tozgokmen@rsmas.miami.edu AnnaLisa Griffa RSMAS, UNIVERSITY OF MIAMI Miami, FL, USA anna@rsmas.miami.edu Manny Fiadeiro Office of Naval Research 875 North Randolph St. ONR Code 322PO Arlington, VA, USA 703-696-4441 manny.fiadeiro@navy.mil |
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4263 4255 4420 4534 .
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| Description: | Because of its fundamental role in advective transport and its practical importance in drifting sensor arrays, autonomous vehicles, and pollution mitigation, Lagrangian data is routinely collected in oceanographic experiments. Associated with these experiments are substantial efforts to assess the Lagrangian predictability of data assimilating models, and to assimilate Lagrangian data into these models to improve predictability. The Office of Naval Research through several research initiatives has supported much of this research. In addition there have been several highly successful Lagrangian Analysis and Prediction of Coastal and Ocean Dynamics workshops. In view of these activities it is appropriate to convene a session to review recent developments in Lagrangian predictability and data assimilation and to facilitate further development and multidisciplinary collaborations. Some specific questions the session will address are: What is the Lagrangian predictability horizon for state-of-the-art data assimilating models? How effective are various methods in improving Lagrangian predictability? What is the status of Lagrangian data assimilation into these models? |