Kernel analog forecasting of tropical intraseasonal oscillations

Romeo Alexander, Zhizhen Zhao, Eniko Székely, Dimitrios Giannakis

Research output: Contribution to journalArticle

Abstract

This paper presents the results of forecasting the Madden-Julian oscillation (MJO) and boreal summer intraseasonal oscillation (BSISO) through the use of satellite-obtained global brightness temperature data with a recently developed nonparametric empirical method. This new method, referred to as kernel analog forecasting, adopts specific indices extracted using the technique of nonlinear Laplacian spectral analysis as baseline definitions of the intraseasonal oscillations of interest, which are then extended into forecasts through an iterated weighted averaging scheme that exploits the predictability inherent to those indices. The pattern correlation of the forecasts produced in this manner remains above 0.6 for 50 days for both the MJO and BSISO when 23 yr of training data are used and 37 days for the MJO when 9 yr of data are used.

Original languageEnglish (US)
Pages (from-to)1321-1342
Number of pages22
JournalJournal of the Atmospheric Sciences
Volume74
Issue number4
DOIs
StatePublished - Apr 1 2017

Keywords

  • Intraseasonal variability
  • Madden-Julian oscillation
  • Monsoons
  • Statistical forecasting

ASJC Scopus subject areas

  • Atmospheric Science

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