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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2019-53
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-53
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Development and technical paper 04 Mar 2019

Development and technical paper | 04 Mar 2019

Review status
This discussion paper is a preprint. A revision of the manuscript is under review for the journal Geoscientific Model Development (GMD).

Weather and climate forecasting with neural networks: using GCMs with different complexity as study-ground

Sebastian Scher1 and Gabriele Messori1,2 Sebastian Scher and Gabriele Messori
  • 1Department of Meteorology and Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden
  • 2Department of Earth Sciences, Uppsala University, Uppsala, Sweden

Abstract. Recently, there has been growing interest in the possibility of using neural networks for both weather forecasting and the generation of climate datasets. We use a bottom-up approach for assessing whether it should, in principle, be possible to do this. We use the relatively simple General Circulation Models (GCMs) PUMA and PLASIM as a simplified reality on which we train deep neural networks, which we then use for predicting the model weather at lead times of a few days. We specifically assess how the complexity of the climate model affects the neural network's forecast skill, and how dependent the skill is on the length of the provided training period. Additionally, we show that using the neural networks to reproduce the climate of general circulation models including a seasonal cycle remains challenging – in contrast to earlier promising results on a model without seasonal cycle.

Sebastian Scher and Gabriele Messori
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Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Sebastian Scher and Gabriele Messori
Model code and software

Code and data for "Weather and climate forecasting with neural networks: using GCMs with different complexity as study-ground" S. Scher https://doi.org/10.5281/zenodo.2572863

Sebastian Scher and Gabriele Messori
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Latest update: 20 May 2019
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Short summary
Currently, weather forecast are mainly produced by using computer models based on physical equations. It is an appealing idea to use neural networks and “deep learning” for weather forecasting instead. We successfully test the possibility of using deep learning for weather forecasting by considering climate models as simplified versions of reality. Our work therefore is a step towards potentially using deep learning to replace or accompany current weather forecasting models.
Currently, weather forecast are mainly produced by using computer models based on physical...
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