Information - HS12 Remote sensing data assimilation in land surface process models (co-listed in AS & GI)
Event Information
Remote sensing derived land surface parameters are widely used in manifold applications. Commonly, land surface parameters are inverted from remote sensing observations by means of distinct parameter inversion models. By integration of the remote sensing information into land surface process models, the simulations of the process model can be improved and forced to mirror a more appropriate and realistic land surface state. Uncertainties in the surface parameter inversion process as well as in the simulations obtained from the process model should be considered in this context.
Modern data assimilation theory provides methods for optimally merging remote sensing observation with land surface process models by considering the process model and observation uncertainties. This allows for the estimation of the most probable surface state.
The session aims at the evaluation of the current state of art of remote sensing data assimilation techniques for land surface modelling purposes. This includes examples of remote sensing data assimilation projects as well as data fusion approaches to combine the information content of various heterogeneous data sources.
This session therefore welcomes contributions on the following particular issues:
- remote sensing data assimilation techniques, including technical background and comparison of different assimilation strategies
- comparative studies, evaluating the benefit of different assimilation techniques
- examples of remote sensing data assimilation into land surface process models
- disaggregation of remote sensing derived land surface information using assimilation techniques
- fusion of different (complementary) data sources to improve surface parameter retrievals by means of data assimilation
Preliminary List of Solicited Speakers
McLaughlin, D.: Multiscale Ensemble Methods for Large Data Assimilation Problems
Co-Sponsorship
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