1,146 research outputs found

    The analog data assimilation: application to 20 years of altimetric data

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    International audienceThe reconstruction of geophysical dynamics remain key challenges in ocean, atmosphere and climate sciences. Data assimilation methods are the state-of-theart techniques to reconstruct the space-time dynamics from noisy and partial observations. They typically involve multiple runs of an explicit dynamical model and may have severe operational limitations, including the computational complexity, the lack of model consistante with respect to the observed data as well as modeling uncertainties. Here, we demonstrate how large amount of historical satellite data can open new avenues to address data assimilation issues, and to develop a fully data-driven assimilation. Assuming that a representative catalog of historical state trajectories is available, the key idea is to use the analog method to propose forecasts with no online evaluation of any physical model. The combination of these analog forecasts with observations resorts to classical stochastic filtering methods. For illustration of the proposed analog data assimilation, the brute force use of 20 years of altimetric data is demonstrated to reconstruct mesoscale sea surface dynamics

    Design of the Observing System Simulation Experiments with multi-platform in situ data and impact on fine- scale structures

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    This report presents the work plan of the Task 2.3: Observing System Simulation Experiments: impact of multi-platform observations for the validation of satellite observation

    Validation and statistical downscaling of ERA-Interim reanalysis data for integrated applications

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    Scientific opportunities using satellite surface wind stress measurements over the ocean

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    Scientific opportunities that would be possible with the ability to collect wind data from space are highlighted. Minimum requirements for the space platform and ground data reduction system are assessed. The operational uses that may develop in government and commercial applications of these data are reviewed. The opportunity to predict the large-scale ocean anomaly called El Nino is highlighted
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