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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/gmd-2020-278
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2020-278
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: model description paper 09 Oct 2020

Submitted as: model description paper | 09 Oct 2020

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This preprint is currently under review for the journal GMD.

A structural self-organizing map (S-SOM) algorithm for weather typing

Quang-Van Doan1, Hiroyuki Kusaka1, Takuto Sato2, and Fei Chen3 Quang-Van Doan et al.
  • 1Center for Computational Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan
  • 2Graduate School of Life and Environmental Sciences, University of Tsukuba
  • 3Research Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado, USA

Abstract. In this study, we propose a novel structural self-organizing map (S-SOM) algorithm for synoptic weather typing. A novel feature of the S-SOM compared with traditional SOMs is its ability to deal with input data that have spatial or temporal structures. In detail, the search scheme for the best matching unit (BMU) in a S-SOM is built based upon a structural similarity (S-SIM) index rather than by using the traditional Euclidean distance (ED). S-SIM enables the BMU search to consider the correlation in space between weather states, such as the location of highs of lows, that is impossible when using ED. The S-SOM performance is evaluated by multiple demo simulations of clustering weather patterns over Japan using the ERA-Interim sea-level pressure data. The results show the superiority of the S-SOM compared with a standard SOM with ED (or ED-SOM) in two respects: clustering quality based on silhouette analysis and topological preservation based on topological error analysis. The superior performance of the S-SOM compared with an ED-SOM is probably independent of both the input data and SOM configuration.

Quang-Van Doan et al.

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Short summary
This study proposes a novel structural self-organizing map (S-SOM) algorithm. The superiority of S-SOM is that it can better recognize the difference (or similarity) among spatial (or temporal) data used for training, thus improve the clustering quality compared to traditional SOM algorithms.
This study proposes a novel structural self-organizing map (S-SOM) algorithm. The superiority of...
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