Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/gmd-2015-273
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Model description paper
11 Feb 2016
Review status
This discussion paper is a preprint. It has been under review for the journal Geoscientific Model Development (GMD). The revised manuscript was not accepted.
ClimateLearn: A machine-learning approach for climate prediction using network measures
Qing Yi Feng1, Ruggero Vasile2,3, Marc Segond4, Avi Gozolchiani5, Yang Wang5, Markus Abel3, Shilomo Havlin5, Armin Bunde6, and Henk A. Dijkstra1 1Institute for Marine and Atmospheric research Utrecht, Utrecht University, The Netherlands
2UP Transfer, Potsdam, Germany
3Ambrosys, Potsdam, Germany
4European Centre for Soft Computing, Mieres, Spain
5Bar-Ilan University, Isreal
6University of Giessen, Germany
Abstract. We present the toolbox ClimateLearn to tackle problems in climate prediction using machine learning techniques and climate network analysis. The package allows basic operations of data mining, i.e. reading, merging, and cleaning data, and running machine learning algorithms such as multilayer artificial neural networks and symbolic regression with genetic programming. Because spatial temporal information on climate variability can be efficiently represented by complex network measures, such data are considered here as input to the machine-learning algorithms. As an example, the toolbox is applied to the prediction of the occurrence and the development of El Niño in the equatorial Pacific, first concentrating on the occurrence of El Niño events one year ahead and second on the evolution of sea surface temperature anomalies with a lead time of three months.

Citation: Feng, Q. Y., Vasile, R., Segond, M., Gozolchiani, A., Wang, Y., Abel, M., Havlin, S., Bunde, A., and Dijkstra, H. A.: ClimateLearn: A machine-learning approach for climate prediction using network measures, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2015-273, 2016.
Qing Yi Feng et al.
Interactive discussionStatus: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version      Supplement - Supplement
 
RC1: 'Comments for ClimateLearn manuscript', Anonymous Referee #1, 16 Mar 2016 Printer-friendly Version 
AC1: 'Point by point reply to reviewer #1', Qing Yi Feng, 17 Sep 2016 Printer-friendly Version 
 
RC2: 'Review', Anonymous Referee #2, 21 Aug 2016 Printer-friendly Version 
AC2: 'Point by point reply to reviewer #2', Qing Yi Feng, 17 Sep 2016 Printer-friendly Version 
Qing Yi Feng et al.
Qing Yi Feng et al.

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Short summary
We present the toolbox ClimateLearn to tackle problems in climate prediction using machine learning techniques and climate network analysis. Because spatial temporal information on climate variability can be efficiently represented by complex network measures, such data are considered here as input to the machine-learning algorithms. As an example, the toolbox is applied to the prediction of the occurrence and the development of El Niño in the equatorial Pacific.
We present the toolbox ClimateLearn to tackle problems in climate prediction using machine...
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