Articles | Volume 14, issue 4
https://doi.org/10.5194/gmd-14-2097-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-2097-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
22 Apr 2021
Model description paper |
| 22 Apr 2021
| 22 Apr 2021
S-SOM v1.0: a structural self-organizing map algorithm for weather typing
Quang-Van Doan
CORRESPONDING AUTHOR
Center for Computational Sciences, University of Tsukuba, Tsukuba,
Ibaraki, Japan
Hiroyuki Kusaka
Center for Computational Sciences, University of Tsukuba, Tsukuba,
Ibaraki, Japan
Takuto Sato
Graduate School of Life and Environmental Sciences, University of
Tsukuba, Tsukuba,
Ibaraki, Japan
Fei Chen
Research Applications Laboratory, National Center for Atmospheric
Research, Boulder, CO, USA
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- Offshore wind resource assessment by characterizing weather regimes based on self-organizing map S. Yang et al. 10.1088/1748-9326/aca2c2
- Projected future changes in water availability and dry spells in Japan: Dynamic and thermodynamic climate impacts M. Ohba et al. 10.1016/j.wace.2022.100523
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- Organized precipitation and associated large‐scale circulation patterns over the Kingdom of Saudi Arabia T. Luong et al. 10.1002/joc.8524
- Effects of meteorological and climatological factors on extremely high residual load and possible future changes M. Ohba et al. 10.1016/j.rser.2023.113188
- Structural k-means (S k-means) and clustering uncertainty evaluation framework (CUEF) for mining climate data Q. Doan et al. 10.5194/gmd-16-2215-2023
- Using a Self-Organizing Map to Explore Local Weather Features for Smart Urban Agriculture in Northern Taiwan A. Huang & F. Chang 10.3390/w13233457
- Determining the onset of summer rainfall over Vietnam using self-organizing maps T. Bui-Minh et al. 10.1007/s00382-024-07385-x
- Impact of weather patterns and meteorological factors on PM2.5 and O3 responses to the COVID-19 lockdown in China F. Shen et al. 10.5194/acp-24-6539-2024
- Comparative Analysis of the Clustering Quality in Self-Organizing Maps for Human Posture Classification L. Ekemeyong Awong & T. Zielinska 10.3390/s23187925
- Assessment of long-term historical trends in winter precipitation in Japan using large-ensemble climate simulations: Changes in the impact of southern coastal cyclones M. Ohba & H. Kawase 10.1007/s00382-024-07213-2
- Projected climate change impacts on hydrological droughts in Japan: dependency on climate and weather patterns M. Ohba et al. 10.1007/s00382-025-07624-9
- Understanding Internal Cluster Variability Through Subcluster Metric Analysis in a Geophysical Context A. Schuddeboom & A. McDonald 10.1029/2022EA002373
- Pan evaporation is increased by submerged macrophytes B. Simon-Gáspár et al. 10.5194/hess-26-4741-2022
- Environmental monitoring of oil pollution in the marine waters using machine learning and remote sensing M. Mokarram & T. Pham 10.1016/j.asr.2025.01.062
- S-SOM v1.0: a structural self-organizing map algorithm for weather typing Q. Doan et al. 10.5194/gmd-14-2097-2021
- Offshore wind resource assessment by characterizing weather regimes based on self-organizing map S. Yang et al. 10.1088/1748-9326/aca2c2
- Projected future changes in water availability and dry spells in Japan: Dynamic and thermodynamic climate impacts M. Ohba et al. 10.1016/j.wace.2022.100523
- Impact of weather regime on projected future changes in streamflow in a heavy snowfall area of Japan M. Ohba et al. 10.1007/s00382-022-06163-x
- Dynamic and thermodynamic contributions of ENSO to winter precipitation in Japan: frequency and precipitation of synoptic weather patterns M. Ohba & S. Sugimoto 10.1007/s00382-021-06052-9
1 citations as recorded by crossref.
Latest update: 19 Feb 2025
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.
This study proposes a novel structural self-organizing map (S-SOM) algorithm. The superiority of...
