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Derivation of Photosynthetically Available Radiation from METEOSAT data in the German Bight with Neural Nets

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Abstract

Two different models, a Physical Model and a Neural Net (NN), are used for the derivation of the Photosynthetically Available Radiation (PAR) from METEOSAT data in the German Bight; advantages and disadvantages of both models are discussed. The use of a NN for derivation of PAR should be preferred to the Physical Model because by construction, a NN can take the various processes determining PAR on a surface much better into account than a non-statistical model relying on averaged relations.

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Notes

  1. Kipp & Zonen ‘PAR Lite’ Photosynthetic Active Radiometer

  2. Kipp & Zonen ‘CM11’ Pyranometer

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Acknowledgements

This research was supported by the HGF strategy fund as part of the ENVOC project. The authoress likes to thank Dr. R. Hollmann for helping me getting started, Dr. R. Doerffer, Dr. H. Schiller and Dr. R. Röttgers for the helpful discussions and the EUMETSAT team for the supply of METEOSAT data.

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  1. GKSS Research Centre, Institute for Coastal Research, Max-Planck-Str. 1, 21502, Geesthacht, Germany

    Kathrin Schiller

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  1. Kathrin Schiller
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Correspondence to Kathrin Schiller.

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Responsible editor: Jörg-Olaf Wolff

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Schiller, K. Derivation of Photosynthetically Available Radiation from METEOSAT data in the German Bight with Neural Nets. Ocean Dynamics 56, 79–85 (2006). https://doi.org/10.1007/s10236-006-0058-1

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