Data Science Journal
Online ISSN : 1683-1470
Papers
Applying a Machine Learning Technique to Classification of Japanese Pressure Patterns
H Kimura, H Kawashima, H Kusaka, H Kitagawa
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ジャーナル フリー

2009 年 8 巻 p. S59-S67

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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.
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