Analyzing Arctic surface temperatures with Self Organizing-Maps: Influence of the maps size
We use ERA-Interim reanalysis data of 2 meter temperature to perform a pattern analysis of the Arctic temperatures exploiting an artificial neural network called Self Organizing-Map (SOM). The SOM method is used as a cluster analysis tool where the number of clusters has to be specified by the user....
| Main Authors: | , |
|---|---|
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Universität Leipzig
2018
|
| Subjects: | |
| Online Access: | https://nbn-resolving.org/urn:nbn:de:bsz:15-qucosa2-317949 https://ul.qucosa.de/id/qucosa%3A31794 https://ul.qucosa.de/api/qucosa%3A31794/attachment/ATT-0/ |
| Summary: | We use ERA-Interim reanalysis data of 2 meter temperature to perform a pattern analysis of the Arctic temperatures exploiting an artificial neural network called Self Organizing-Map (SOM). The SOM method is used as a cluster analysis tool where the number of clusters has to be specified by the user. The different sized SOMs are analyzed in terms of how the size changes the representation of specific features. The results confirm that the larger the SOM is chosen the larger will be the root mean square error (RMSE) for the given SOM, which is followed by the fact that a larger number of patterns can reproduce more specific features for the temperature. Wir benutzten das künstliche neuronale Netzwerk Self Organizing-Map (SOM), um eine Musteranalyse von ERA-Interim Reanalysedaten durchzuführen. Es wurden SOMs mit verschiedener Musteranzahl verglichen. Die Ergebnisse zeigen, dass SOMs mit einer größeren Musteranzahl deutlich spezifischere Muster produzieren im Vergleich zu SOMs mit geringen Musteranzahlen. Dies zeigt sich unter anderem in der Betrachtung der mittleren quadratischen Abweichung (RMSE) der Muster zu den zugeordneten ERA Daten. |
|---|