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George N. Kiladis, Juliana Dias, Katherine H. Straub, Matthew C. Wheeler, Stefan N. Tulich, Kazuyoshi Kikuchi, Klaus M. Weickmann, and Michael J. Ventrice

) , as long as the spatial EOFs derived from the fully filtered fields are used for projection. Real-time data can be easily smoothed for this, using running averages up to the endpoint, as shown in section 5 . Nevertheless, for studies where retrospective data allow for better filtering, the OMI and FMO certainly do a better job in categorizing the convective evolution of individual events, as shown in the preceding sections of this paper. With regard to the evaluation of model output using MJO

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Weixin Xu and Steven A. Rutledge

Roundy 2013 ), ENSO ( Zhang 2005 ; Lau 2012 ), and extratropical climate modes ( Lin et al. 2009 ; L’Heureux and Higgins 2008 ). Despite decades of study, the MJO is not well understood and therefore MJO prediction skill is limited, especially concerning initiation over the Indian Ocean ( Bechtold et al. 2008 ; Kim et al. 2009 ; Vitart and Molteni 2010 ). Meanwhile, the MJO has been poorly simulated by several generations of general circulation models (GCMs) ( Lin et al. 2006 ; Hung et al. 2013

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Jean-Philippe Duvel

–2012 for which both ERA-I and RSMC evaluations are expected to be more reliable for this region thanks to additional satellite data (e.g., relocation of Meteosat-5 over the Indian Ocean in July 1998). Fig . 8. MJO modulation of TD and TS initiations over the Indian Ocean during austral summer. (a) Probability (number per day) of TD and TS initiations in different phases of the MJO and for “no MJO” periods; up (down) triangles represent significant increase (decrease) of the initiation probability at

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