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Jian Ling, Peter Bauer, Peter Bechtold, Anton Beljaars, Richard Forbes, Frederic Vitart, Marcela Ulate, and Chidong Zhang

Program (ARM) MJO Investigation Experiment (AMIE), and Littoral Air–Sea Process (LASP). Hereafter, it is referred to in brief as the DYNAMO field campaign. Yoneyama et al. (2013) provided detailed descriptions of this field campaign. Three MJO events were observed during the DYNAMO field campaign ( Fig. 1a ). They are described in detail by Gottschalck et al. (2013) . Real-time forecasts produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) during the field campaign captured

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Xiouhua Fu, Wanqiu Wang, June-Yi Lee, Bin Wang, Kazuyoshi Kikuchi, Jingwei Xu, Juan Li, and Scott Weaver

). Through upscale/downscale impacts and tropical–extratropical teleconnections, the MJO modulates the weather and climate activities over the globe ( Donald et al. 2006 ; Zhang 2013 ). The recurrent nature of the MJO with a period of 30–60 days also offers an opportunity to bridge the forecasting gap between medium-range weather forecast (~1 week) and seasonal prediction (~1 month and longer) (e.g., Waliser 2005 ; Fu et al. 2008a ; Brunet et al. 2010 ; Hoskins 2013 ). However, most global research

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Yue Ying and Fuqing Zhang

predictability can be grossly categorized into intrinsic versus practical predictability ( Lorenz 1996 ; Melhauser and Zhang 2012 ). Intrinsic predictability refers to the ability to predict given nearly perfect representation of the dynamical system (by a forecast model) and nearly perfect initial/boundary conditions, an inherent limit due to the chaotic nature of the atmosphere ( Lorenz 1963 , 1969 ; Zhang et al. 2003 , 2007 ; Sun and Zhang 2016 ). Practical predictability, sometimes also referred to

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Hyodae Seo, Aneesh C. Subramanian, Arthur J. Miller, and Nicholas R. Cavanaugh

exerts a profound influence on global weather and climate ( Zhang 2005 , 2013 ), the complexities of multiscale interaction of the circumequatorial tropical atmospheric circulation with individual cloud systems and upper-ocean processes make it difficult for the climate models to accurately represent the MJO (e.g., Zhang 2005 ; Madden and Julian 2005 ; Lin et al. 2006 ; Hung et al. 2013 ). Despite recent advancements in MJO simulation and prediction in climate and forecast models (e

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Kacie E. Hoover, John R. Mecikalski, Timothy J. Lang, Xuanli Li, Tyler J. Castillo, and Themis Chronis

1. Introduction The world’s oceans are vast data-void regions. To combat the lack of high-resolution ocean surface wind data, the Cyclone Global Navigation Satellite System (CYGNSS) mission was launched to retrieve wind speeds ( Ruf et al. 2016 ). The main objective for CYGNSS, which is composed of eight bistatic microscatterometers, is to accurately and quickly retrieve rapidly changing wind speeds within precipitating regions of tropical cyclones, in order to improve the forecasting

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Tomoe Nasuno, Tim Li, and Kazuyoshi Kikuchi

1. Introduction The Madden–Julian oscillation (MJO; Madden and Julian 1971 , 1972 ) is a prominent tropical disturbance that has a broad impact on the global weather and climate ( Zhang 2013 ; Gottschalck et al. 2010 ). The MJO is related to a wide variety of tropical and extratropical ocean and atmosphere phenomena, ranging from local to global spatial scales and diurnal to interannual time scales. Therefore, it is an important target of extended-range weather forecasting. However

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Kunio Yoneyama, Chidong Zhang, and Charles N. Long

motivated growing interest in its real-time monitoring ( Wheeler and Hendon 2004 ) and forecasts ( Gottschalck et al. 2010 ). Tremendous efforts have been made to improve our knowledge of the MJO from viewpoints of observations, numerical modeling, and theories ( Zhang 2005 ; Lau and Waliser 2012 ). While there has been progress in MJO prediction ( Bechtold et al. 2008 ; Vitart and Molteni 2010 ) and simulation ( Miura et al. 2007 ; Benedict and Randall 2009 ), they are still significant unmet

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Ji-Eun Kim, Chidong Zhang, George N. Kiladis, and Peter Bechtold

terms from parameterization schemes with global and long-term coverage. Examples of such products are the Modern-Era Retrospective Analysis for Research and Applications (MERRA; Rienecker et al. 2011 ; Mapes and Bacmeister 2012 ) and Year of Tropical Convection (YOTC) European Centre for Medium-Range Weather Forecasts database, known as the YOTC analysis ( Moncrieff et al. 2012 ; Waliser et al. 2012 ). Obviously, these products include errors from parameterization schemes. Cloud-permitting model

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Ji-Hyun Oh, Xianan Jiang, Duane E. Waliser, Mitchell W. Moncrieff, Richard H. Johnson, and Paul Ciesielski

–Julian Oscillation (DYNAMO) field campaign was conducted over the IO from October 2011 to February 2012 to investigate the initiation of the MJO over the IO and improve our forecasting skill of the MJO ( Yoneyama et al. 2013 ). This international effort yielded unprecedented high-quality datasets including observations from ground-based radars, upper-air soundings, aircraft, ships, and satellites to benefit comprehensive studies of the mechanisms related to the initiation of the MJO over the IO ( Johnson and

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Sue Chen, Maria Flatau, Tommy G. Jensen, Toshiaki Shinoda, Jerome Schmidt, Paul May, James Cummings, Ming Liu, Paul E. Ciesielski, Christopher W. Fairall, Ren-Chieh Lien, Dariusz B. Baranowski, Nan-Hsun Chi, Simon de Szoeke, and James Edson

. COAMPS real-data simulations During the CINDY2011/DYNAMO campaign, a research version of the fully air–ocean–wave coupled COAMPS provided real-time forecast support for the field campaign. COAMPS used a 6-hourly data assimilation cycle in the atmosphere and ocean from 17 August 2011 to 15 January 2012 and issued a 4-day forecast once a day at 1200 UTC. The atmospheric coarse domain covers the area of 25°S–25°N and 30°–150°E while the 3-km atmospheric nest was designed to encompass the entire CINDY

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