%0 Journal Article %1 Jiang2014Insights %A Jiang, Ningbo %A Luo, Kehui %A Beggs, Paul J. %A Cheung, Kevin %A Scorgie, Yvonne %D 2014 %I John Wiley & Sons, Ltd %J Int. J. Climatol. %K weathertypes circulation statistics clustering %P n/a %R 10.1002/joc.4221 %T Insights into the implementation of synoptic weather-type classification using self-organizing maps: an Australian case study %U http://dx.doi.org/10.1002/joc.4221 %X The two-fold utility (data projection and cluster analysis) of a two-phase batch self-organizing map (SOM) procedure (CP2) has been previously explored using the NCEP/NCAR geopotential height data for east Australia. That study focused on examining the performance of CP2 in comparison with a traditional cluster analysis procedure, CP1, for the purpose of synoptic typing. The present paper provides additional documentation on the implementation of CP2 for the same region, with broader considerations on the effect of SOM map size, seasonality, data standardization and the choice of neighbourhood functions. A total of 215 SOMs (classifications) were trained through CP2 with various data processing and parameter settings. The examination of these SOMs shows that the two-fold utility of CP2 leads to supplementary visualization of the dominant synoptic patterns over the study region. For SOMs of the same map size (i.e. number of synoptic types), cluster analysis via CP2 provides data groupings with relatively high accuracy and large separation but reduced level of pattern self-organization, while data projection via CP2 tends to create data groupings with a high level of pattern self-organization but reduced accuracy and separation. The choice of map size affects the accuracy, separation and self-organization of data groupings. As a compromise, a map size of 10–20 for cluster analysis and 20–30 for data projection is recommended for the study region. To account for the seasonality and latitudinal heterogeneity in the activity of synoptic systems, a relatively larger SOM size is needed to capture typical synoptic features prevailing in different seasons. Data standardization helps to provide a relatively balanced representation between larger-scale synoptic systems (e.g. anticyclones) and smaller-scale synoptic features (e.g. thermal lows), and also improves the level of pattern self-organization on the SOM across seasons. The additional documentation in this paper encourages a wider application of CP2 in environmental research.

@article{Jiang2014Insights, abstract = {The two-fold utility (data projection and cluster analysis) of a two-phase batch self-organizing map (SOM) procedure (CP2) has been previously explored using the NCEP/NCAR geopotential height data for east Australia. That study focused on examining the performance of CP2 in comparison with a traditional cluster analysis procedure, CP1, for the purpose of synoptic typing. The present paper provides additional documentation on the implementation of CP2 for the same region, with broader considerations on the effect of SOM map size, seasonality, data standardization and the choice of neighbourhood functions. A total of 215 SOMs (classifications) were trained through CP2 with various data processing and parameter settings. The examination of these SOMs shows that the two-fold utility of CP2 leads to supplementary visualization of the dominant synoptic patterns over the study region. For SOMs of the same map size (i.e. number of synoptic types), cluster analysis via CP2 provides data groupings with relatively high accuracy and large separation but reduced level of pattern self-organization, while data projection via CP2 tends to create data groupings with a high level of pattern self-organization but reduced accuracy and separation. The choice of map size affects the accuracy, separation and self-organization of data groupings. As a compromise, a map size of 10–20 for cluster analysis and 20–30 for data projection is recommended for the study region. To account for the seasonality and latitudinal heterogeneity in the activity of synoptic systems, a relatively larger SOM size is needed to capture typical synoptic features prevailing in different seasons. Data standardization helps to provide a relatively balanced representation between larger-scale synoptic systems (e.g. anticyclones) and smaller-scale synoptic features (e.g. thermal lows), and also improves the level of pattern self-organization on the SOM across seasons. The additional documentation in this paper encourages a wider application of CP2 in environmental research.}, added-at = {2018-06-18T21:23:34.000+0200}, author = {Jiang, Ningbo and Luo, Kehui and Beggs, Paul J. and Cheung, Kevin and Scorgie, Yvonne}, biburl = {https://www.bibsonomy.org/bibtex/2fb393832200a3decaef7ed1d8f4dc4f0/pbett}, citeulike-article-id = {13447038}, citeulike-linkout-0 = {http://dx.doi.org/10.1002/joc.4221}, day = 1, doi = {10.1002/joc.4221}, interhash = {ff0d3a8e678137fc58b188e3a3ce8eda}, intrahash = {fb393832200a3decaef7ed1d8f4dc4f0}, journal = {Int. J. Climatol.}, keywords = {weathertypes circulation statistics clustering}, month = nov, pages = {n/a}, posted-at = {2014-11-28 07:27:35}, priority = {2}, publisher = {John Wiley \& Sons, Ltd}, timestamp = {2018-06-22T18:34:41.000+0200}, title = {Insights into the implementation of synoptic weather-type classification using self-organizing maps: an Australian case study}, url = {http://dx.doi.org/10.1002/joc.4221}, year = 2014 }