Session Programme Meeting Programme Personal Programme Search
 
Quick Search
Atmospheric Sciences
Meteorology
Boundary layer processes
Atmospheric chemistry and aerosols
Programme Groups
Great Debates
Union Symposia
Educational Symposia
Atmospheric Sciences
Biogeosciences
Climate: Past, Present, Future
Cryospheric Sciences
Energy, Resources and the Environment
Geochemistry, Mineralogy, Petrology & Volcanology
Geodesy
Geodynamics
Geomorphology
Geophysical Instrumentation
Hydrological Sciences
Magnetism, Palaeomagnetism, Rock Physics & Geomaterials
Natural Hazards
Nonlinear Processes in Geophysics
Ocean Sciences
Planetary and Solar System Sciences
Seismology
Soil System Sciences
Solar-Terrestrial Sciences
Stratigraphy, Sedimentology and Palaeontology
Tectonics and Structural Geology
Medal Lectures
Key Note Lectures
Key Note Sessions
Division Business Meetings
Editorial Board Meetings
Townhall Meetings
Splinter Meetings
Union Meetings
  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

General Statement
The information contained hereafter has been compiled and uploaded by the Session Organizers via the "Organizer Session Form". The Session Organizers have therefore the sole responsibility that this information is true and accurate at the date of publication, and the conference organizer cannot accept any legal responsibility for any errors or omissions that may be made, and he makes no warranty, expressed or implied, with regard to the material published.



Back to Session Programme

 
 
 
 


©2002-2008 Copernicus Systems + Technology GmbH