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Full-Text Articles in Engineering
Global Observations Of Fine-Scale Ocean Surface Topography With The Surface Water And Ocean Topography (Swot) Mission, Rosemary Morrow, Lee-Lueng Fu, Fabrice Ardhuin, Mounir Benkiran, Bertrand Chapron, Emmanuel Cosme, Francesco D’Ovidio, J. Thomas Farrar, Sarah T. Gille, Guillaume Lapeyre, Pierre-Yves Le Traon, Ananda Pascual, Aurélien Ponte, Bo Qiu, Nicolas Rascle, Clement Ubelmann, Jinbo Wang, Edward Zaron
Global Observations Of Fine-Scale Ocean Surface Topography With The Surface Water And Ocean Topography (Swot) Mission, Rosemary Morrow, Lee-Lueng Fu, Fabrice Ardhuin, Mounir Benkiran, Bertrand Chapron, Emmanuel Cosme, Francesco D’Ovidio, J. Thomas Farrar, Sarah T. Gille, Guillaume Lapeyre, Pierre-Yves Le Traon, Ananda Pascual, Aurélien Ponte, Bo Qiu, Nicolas Rascle, Clement Ubelmann, Jinbo Wang, Edward Zaron
Civil and Environmental Engineering Faculty Publications and Presentations
The future international Surface Water and Ocean Topography (SWOT) Mission, planned for launch in 2021, will make high-resolution 2D observations of sea-surface height using SAR radar interferometric techniques. SWOT will map the global and coastal oceans up to 77.6 latitude every 21 days over a swath of 120 km (20 km nadir gap). Today’s 2D mapped altimeter data can resolve ocean scales of 150 km wavelength whereas the SWOT measurement will extend our 2D observations down to 15–30 km, depending on sea state. SWOT will offer new opportunities to observe the oceanic dynamic processes at scales that are important in …
The Quest For Model Uncertainty Quantification: A Hybrid Ensemble And Variational Data Assimilation Framework, Peyman Abbaszadeh, Hamid Moradkhani, Dacian Daescu
The Quest For Model Uncertainty Quantification: A Hybrid Ensemble And Variational Data Assimilation Framework, Peyman Abbaszadeh, Hamid Moradkhani, Dacian Daescu
Civil and Environmental Engineering Faculty Publications and Presentations
This article presents a novel approach to couple a deterministic four‐dimensional variational (4DVAR) assimilation method with the particle filter (PF) ensemble data assimilation system, to produce a robust approach for dual‐state‐parameter estimation. In our proposed method, the Hybrid Ensemble and Variational Data Assimilation framework for Environmental systems (HEAVEN), we characterize the model structural uncertainty in addition to model parameter and input uncertainties. The sequential PF is formulated within the 4DVAR system to design a computationally efficient feedback mechanism throughout the assimilation period. In this framework, the 4DVAR optimization produces the maximum a posteriori estimate of state variables at the beginning …