Articles | Volume 14, issue 4
Geosci. Model Dev., 14, 2097–2111, 2021
https://doi.org/10.5194/gmd-14-2097-2021
Geosci. Model Dev., 14, 2097–2111, 2021
https://doi.org/10.5194/gmd-14-2097-2021
Model description paper
22 Apr 2021
Model description paper | 22 Apr 2021

S-SOM v1.0: a structural self-organizing map algorithm for weather typing

Quang-Van Doan et al.

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Cited articles

Alexander, L. V., Uotila, P., Nicholls, N., and Lynch, A.: A new daily pressure dataset for Australia and its application to the assessment of changes in synoptic patterns during the last century, J. Climate, 23, 1111–1126, 2010. 
Borah, N., Sahai, A., Chattopadhyay, R., Joseph, S., Abhilash, S., and Goswami, B.: A self-organizing map–based ensemble forecast system for extended range prediction of active/break cycles of Indian summer monsoon, J. Geophys. Res.-Atmos., 118, 9022–9034, 2013. 
Chang, L.-C., Shen, H.-Y., and Chang, F.-J.: Regional flood inundation nowcast using hybrid SOM and dynamic neural networks, J. Hydrol., 519, 476–489, 2014. 
Doan, Q. V.: S-SOM v1.0: A structural self-organizing map algorithm for weather typing (Version V1), Zenodo, https://doi.org/10.5281/zenodo.4437954, 2021. 
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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 and thus improve the clustering quality compared to traditional SOM algorithms.