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Assimilation of lagrangian data in oceanography (2007).
| Content Provider | CiteSeerX |
|---|---|
| Abstract | Within the framework of Global Ocean Data Assimilation Experiment (GODAE), an increasing amount of data are available. A crucial issue for oceanographers is to exploit at best these observations, in order to improve models, climatology, forecasts, etc. Thanks to the international program Argo and to more localized experiments, a new type of data is now available: positions of floats drifting at depth in the ocean. Unlike other data, mainly Eulerian, these ones are Lagrangian: the measuring instrument move in the flow. I will briefly described methods about 4D-Var assimilation of Lagrangian data in the OPAVAR ocean model, and show some results assessing the complementarity with temperature data. Copyright line will be provided by the publisher 1 Presentation of the Method 1.1 Introduction to Data Assimilation for geophysical fluids Geophysical fluids are chaotic, and consequently show high sensitivity to initial conditions. In order to do accurate forecasts, we need a very accurate estimate of the initial state of the system. Data Assimilation (DA) covers all theoretical and numerical mathematical methods which allow us to combine, as optimally as possible, all sources of information (model equations and observations) in order to produce a good initial state. There exist two main approaches to DA: sequential methods (Kalman |
| File Format | |
| Publisher Date | 2007-01-01 |
| Access Restriction | Open |
| Subject Keyword | Lagrangian Data Data Assimilation Instrument Move Temperature Data High Sensitivity 4d-var Assimilation Geophysical Fluid Geophysical Fluid Numerical Mathematical Method Main Approach Global Ocean Data Assimilation Experiment Accurate Forecast Initial State Localized Experiment Initial Condition Crucial Issue Model Equation Accurate Estimate International Program Argo Sequential Method Copyright Line Good Initial State |
| Content Type | Text |
