Detection of Pressure Patterns Using Support Vector Machine: Winter Type Pressure Pattern

Kimura, Hironobu

doi:10.4157/grj.82.323

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…Typical examples of such classification methods are clustering techniques, such as Ward's method, K-means clustering, and principal component analysis (PCA; e.g., Key and Crane, 1986;Cheng and Wallace, 1993;Hoffmann andSchkunzen, 2013, Kato et al, 2013;Miyasaka et al, 2020). Machine learning methods, including self-organizing maps (e.g., Hewiston and Crane, 2002;Cassano et al, 2006;Johnson et al, 2008;Ohba et al, 2016;Tamaki et al, 2018) and support vector machines (e.g., Kimura et al, 2009;Ortiz-García et al, 2014;Su et al, 2018), are now being used for classification or selection problems.…”

Section: Introductionmentioning

confidence: 99%

Statistical Intercomparison of Similarity Metrics in Sea Level Pressure Pattern Classification

Sato

Kusaka

2021

Journal of the Meteorological Society of Japan

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In this study, we compare the accuracy of five representative similarity metrics in extracting sea level pressure (SLP) patterns for accurate weather chart classification: correlation coefficient, Euclidean distance (EUC), S1-score (S1), structural similarity (SSIM), and average hash. We use a large amount of teacher data to statistically evaluate the accuracy of each metric. The evaluation results reveal that S1 and SSIM have the highest accuracy in terms of both average and maximum scores. Their accuracy does not change even when non-ideal data are used as the teacher data. In addition, S1 and SSIM can reproduce the subjective resemblance between two maps better than EUC. However, EUC reproduces the central position of the signal in a sample case. This study can serve as a reference for identifying the most useful similarity metric for the classification of SLP patterns, especially when using non-ideal teacher data.

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Section: Introductionmentioning

confidence: 99%

Statistical Intercomparison of Similarity Metrics in Sea Level Pressure Pattern Classification

Sato

Kusaka

2021

Journal of the Meteorological Society of Japan

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“…Daily or sub‐monthly data are necessary. Kimura et al (2009) proposed an objective method for detecting typical winter monsoon‐type pressure pattern by using the daily mean sea level pressure (SLP) data obtained from the Japanese 25‐year Reanalysis Project (JRA‐25) data. They indicated that typical winter monsoon‐type pressure pattern could be detected with high accuracy by using the JRA‐25 reanalysis data.…”

Section: Introductionmentioning

confidence: 99%

Secular and seasonal variations of winter monsoon weather patterns in Japan since the early 20th century

Hirano

Matsumoto

2010

Intl Journal of Climatology

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Winter weather conditions in Japan are characterized by a clear contrast between the regions on the Japan Sea side and those on the Pacific Ocean side of the Japanese Islands. Weather conditions in the Japan Sea side are characterized by snowfall or rainfall under winter monsoon outbreak conditions. However, the Pacific Ocean side has fine weather because the central ridges of mountains block snow clouds coming from the Japan Sea. This particular weather pattern can easily be recognized from the daily precipitation distributions. In this study, we used the daily precipitation data recorded by the Japan Meteorological Agency since 1901 to define winter monsoon weather pattern based on the contrast in the spatial distribution of daily precipitation between the regions on the Japan Sea side and those on the Pacific Ocean side. On the basis of seasonal changes in the occurrence frequencies of winter monsoon weather pattern, we delimited the beginning and ending pentads of the winter monsoon season for each year. We aimed to clarify the characteristics of long-term variations in the lengths of the winter monsoon season and in the seasonal march of winter weather conditions in Japan since the early 20th century. The results revealed a significant decreasing trend in the lengths of the winter monsoon season since the early 20th century. We also found that the occurrence frequencies of winter monsoon weather pattern have been decreasing significantly since the early 20th century. In addition to the long-term decreasing trend, we detected an abrupt decrease in the occurrence frequencies of winter monsoon weather pattern in the mid-1980s, which suggests a weakening of the winter monsoon after the mid-1980s.

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Detection of Pressure Patterns Using Support Vector Machine: Winter Type Pressure Pattern

Cited by 2 publications

References 6 publications

Statistical Intercomparison of Similarity Metrics in Sea Level Pressure Pattern Classification

Statistical Intercomparison of Similarity Metrics in Sea Level Pressure Pattern Classification

Secular and seasonal variations of winter monsoon weather patterns in Japan since the early 20th century

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