Fr. 237.00

Remote Sensing and Climate Modeling: Synergies and Limitations

English · Hardback

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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.

List of contents

A global vegetation index for SeaWiFS: Design and applications.- Modeling sensible heat flux using estimates of soil and vegetation temperatures: the HEIFE and IMGRASS experiments.- Exploitation of Surface Albedo Derived From the Meteosat Data to Characterize Land Surface Changes.- Towards a Climatology of Australian Land Surface Albedo for use in Climate Models.- Collocated surface and satellite observations as constraints for Earth radiation budget simulations with global climate models.- How well do aerosol retrievals from satellites and representation in global circulation models match ground-based AERONET aerosol statistics?.- Remote Sensing of Snow and Characterization of Snow Albedo for Climate Simulations.- Using the Special Sensor Microwave Imager to Monitor Surface Wetness and Temperature.- Snow Cover Fraction In A General Circulation Model.- Boreal Forest Fire Regimes And Climate Change.- Specification of surface characteristics for use in a high resolution regional climate model: on the role of glaciers in the swiss alps.- Using Satellite Data Assimilation to Infer Global Soil Moisture Status and Vegetation Feedback to Climate.- The Use of Remotely-Sensed Data for the Estimation of Energy Balance Components in a Mountainous Catchment Area.- Integration of operationally available remote sensing and synoptic data for surface energy balance modelling and environmental applications on the regional scale.

Summary

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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