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Remote Sensing And Climate Modeling Synergies And Limitations 1st Edition N Gobron

  • SKU: BELL-2258770
Remote Sensing And Climate Modeling Synergies And Limitations 1st Edition N Gobron
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Remote Sensing And Climate Modeling Synergies And Limitations 1st Edition N Gobron instant download after payment.

Publisher: Springer Netherlands
File Extension: PDF
File size: 16.75 MB
Pages: 346
Author: N. Gobron, F. Mélin, B. Pinty (auth.), Martin Beniston, Michel M. Verstraete (eds.)
ISBN: 0792368010, 9780792368014
Language: English
Year: 2001
Edition: 1

Product desciption

Remote Sensing And Climate Modeling Synergies And Limitations 1st Edition N Gobron by N. Gobron, F. Mélin, B. Pinty (auth.), Martin Beniston, Michel M. Verstraete (eds.) 0792368010, 9780792368014 instant download after payment.

1 2 Michel M. VERSTRAETE and Martin BENISTON 1 Space Applications Institute, EC Joint Research Centre, Ispra, Italy 2 Department of Geography, University of Fribourg, Switzerland This volume contains the proceedings ofthe workshop entitled “Satellite Remote Sensing and Climate Simulations: Synergies and Limitations” that took place in Les Diablerets, Switzerland, September 20–24, 1999. This international scientific conference aimed at addressing the current and pot- tial role of satellite remote sensing in climate modeling, with a particular focus on land surface processes and atmospheric aerosol characterization. Global and regional circulation models incorporate our knowledge ofthe dynamics ofthe Earth's atmosphere. They are used to predict the evolution of the weather and climate. Mathematically, this system is represented by a set ofpartial differential equations whose solution requires initial and bo- dary conditions. Limitations in the accuracy and geographical distribution of these constraints, and intrinsic mathematical sensitivity to these conditions do not allow the identification of a unique solution (prediction). Additional observations on the climate system are thus used to constrain the forecasts of the mathematical model to remain close to the observed state ofthe system.

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