Identification of typical synoptic patterns causing heavy rainfall in the rainy season in Japan by a Self-Organizing Map |
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| Authors: | Koji Nishiyama Shinichi Endo Kenji Jinno Cintia Bertacchi Uvo Jonas Olsson Ronny Berndtsson |
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| Institution: | aInstitute of Environmental Systems, Faculty of Engineering, Kyushu University 6-10-1, Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan;bDepartment of Water Resources Engineering, Lund University, PO Box 118, S-221 00 Lund, Sweden;cSwedish Meteorological and Hydrological Institute, SE-601 76, Norrköping, Sweden |
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| Abstract: | In order to systematically and visually understand well-known but qualitative and complex relationships between synoptic fields and heavy rainfall events in Kyushu Islands, southwestern Japan, during the BAIU season, these synoptic fields were classified using the Self-Organizing Map (SOM), which can convert complex non-linear features into simple two-dimensional relationships. It was assumed that the synoptic field patterns could be simply expressed by the spatial distribution of (1) wind components at the 850 hPa level and (2) precipitable water (PW) defined by the water vapor amount contained in a vertical column of the atmosphere. By the SOM algorithm and the clustering techniques of the U-matrix and the K-means, the synoptic fields could be divided into eight kinds of patterns (clusters). One of the clusters has the notable spatial features represented by a large PW content accompanied by strong wind components known as low-level jet (LLJ). The features of this cluster indicate a typical synoptic field pattern that frequently causes heavy rainfall in Kyushu during the rainy season.In addition, an independent data set was used for validating the performance of the trained SOM. The results indicated that the SOM could successfully extract heavy rainfall events related to typical synoptic field patterns of the BAIU season. Interestingly, one specific SOM unit was closely related to the occurrence of disastrous heavy rainfall events observed during both training and validation periods. From these results, the trained SOM showed good performance for identifying synoptic fields causing heavy rainfall also in the validation period. We conclude that the SOM technique may be an effective tool for classifying complicated non-linear synoptic fields and identifying heavy rainfall events to some degree. |
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| Keywords: | Self-Organizing Map (SOM) Clustering Low-level jet Precipitable water Heavy rainfall |
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