Applying a machine learning technique to classification of Japanese pressure patterns

H. Kimura, H. Kawashima, H. Kusaka, H. Kitagawa

研究成果: Article

1 引用 (Scopus)


In climate research, pressure patterns are often very important. When a climatologists need to know the days of a specific pressure pattern, for example "low pressure in Western areas of Japan and high pressure in Eastern areas of Japan (Japanese winter-type weather)," they have to visually check a huge number of surface weather charts. To overcome this problem, we propose an automatic classification system using a support vector machine (SVM), which is a machine-learning method. We attempted to classify pressure patterns into two classes: "winter type" and "non-winter type". For both training datasets and test datasets, we used the JRA-25 dataset from 1981 to 2000. An experimental evaluation showed that our method obtained a greater than 0.8 F-measure. We noted that variations in results were based on differences in training datasets.

ジャーナルData Science Journal
出版物ステータスPublished - 2009 3 30


ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computer Science Applications