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Statistical downscaling of ERA interim data using spatial distributed land surface characteristics and novel tools from machine learning and pattern recognition for hydrological applications


Fachgebiet: Hydrologie, Klimatologie, Modellierung
Gefördert durch: ECMWF

Projektleitung: Bernhardt, M., Schulz, K.
Projektwissenschaftler: Gao, L.

Laufzeit: 07/2011 - 07/2014

Temperature data delivered by meteorological models becomes of more and more importance as e.g. driving variable for land surface models or as information about the temperature development in remote regions. But, the output grid size of global meteorological models is often to coarse for a local or regional interpretation of the data. This project will develop novel tools for dwonsclaing ERA-Interim data and making it usable at the local scale.


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