Methods to Evaluate Prediction Skill in the Madden-Julian Oscillation Phase Space

• ICHIKAWA Yuiko

  Graduate School of Science, Hokkaido University, Sapporo, Japan

• INATSU Masaru

  Faculty of Science, Hokkaido University, Sapporo, Japan

Abstract

 Indices of prediction skill over the Madden-Julian oscillation (MJO) phase space are examined with reanalysis and forecast data provided by the Japan Meteorological Agency (JMA). In addition to the bivariate root-mean-square error (RMSE) and the bivariate anomaly correlation coefficient (ACC), the mean-error vector is assessed. Conventionally, the RMSE and ACC have been used, although this approach misses information on the model bias for MJO events. Moreover, the ACC is not suitable for models in which the MJO signal tends to damp in some phases, because the ACC strongly depends on the MJO amplitude. The mean-error vector compensates for this drawback by associating a model’s erroneous mean tendency with the RMSE. For example, the JMA forecast produces a leftward mean error vector field uniformly distributed over the MJO phase space with its amplitude related to the RMSE. The RMSE should be then used with the mean error vector for evaluating the MJO prediction skill.

Journal

• Journal of the Meteorological Society of Japan. Ser. II

  Journal of the Meteorological Society of Japan. Ser. II 94 (3), 257-267, 2016

  Meteorological Society of Japan

Keywords

• tropical meteorology
• MJO
• prediction
• Madden-Julian oscillation

Details

• CRID: 1390282681480542592
• NII Article ID: 130005159718
• NII Book ID: AA00702524
• DOI: 10.2151/jmsj.2016-014
• ISSN: 21869057
• ISSN: 00261165
• NDL BIB ID: 027445467
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I027445467
• Web Site: https://www.jstage.jst.go.jp/article/jmsj/94/3/94_2016-014/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • NDL
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed