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Identification of typical synoptic patterns causing heavy rainfall in the rainy season in Japan by a self-organizing map

Nishiyama, Koji; Endo, Shinichi; Jinno, Kenji; Bertacchi Uvo, Cintia; Olsson, Jonas and Berndtsson, Ronny LU (2007) In Atmospheric Research 83(2-4). p.185-200
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... (More)
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. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Precipitable water, Low-level jet, Self-Organizing Map (SOM), Clustering, Heavy rainfall
in
Atmospheric Research
volume
83
issue
2-4
pages
185 - 200
publisher
Elsevier
external identifiers
  • wos:000244622400008
  • scopus:33846576949
ISSN
1873-2895
DOI
10.1016/j.atmosres.2005.10.015
language
English
LU publication?
yes
id
7c89075c-33e6-4eac-9c7f-a54d13e09fb2 (old id 587332)
date added to LUP
2007-12-01 12:02:25
date last changed
2017-10-22 03:54:19
@article{7c89075c-33e6-4eac-9c7f-a54d13e09fb2,
  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.},
  author       = {Nishiyama, Koji and Endo, Shinichi and Jinno, Kenji and Bertacchi Uvo, Cintia and Olsson, Jonas and Berndtsson, Ronny},
  issn         = {1873-2895},
  keyword      = {Precipitable water,Low-level jet,Self-Organizing Map (SOM),Clustering,Heavy rainfall},
  language     = {eng},
  number       = {2-4},
  pages        = {185--200},
  publisher    = {Elsevier},
  series       = {Atmospheric Research},
  title        = {Identification of typical synoptic patterns causing heavy rainfall in the rainy season in Japan by a self-organizing map},
  url          = {http://dx.doi.org/10.1016/j.atmosres.2005.10.015},
  volume       = {83},
  year         = {2007},
}