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

of CYGNSS from the E2ES, and provide an opportunity to investigate the impacts of temporal resolution and spatial coverage on CYGNSS data. The WRF forecast files with assimilation of DYNAMO data were used as inputs for the CYGNSS E2ES and treated as “truth” to determine and analyze CYGNSS-observed winds. c. The E2ES It is important to know in advance how mesoscale and convective processes appear from the point of view of CYGNSS. To assess CYGNSS capabilities before the satellite was launched

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Yue Ying and Fuqing Zhang

.1175/1520-0493(2002)130<1617:MPOTSS>2.0.CO;2 Zhang , F. , C. Snyder , and R. Rotunno , 2003 : Effects of moist convection on mesoscale predictability . J. Atmos. Sci. , 60 , 1173 – 1185 , doi: 10.1175/1520-0469(2003)060<1173:EOMCOM>2.0.CO;2 . 10.1175/1520-0469(2003)060<1173:EOMCOM>2.0.CO;2 Zhang , F. , A. M. Odins , and J. W. Nielsen-Gammon , 2006 : Mesoscale predictability of an extreme warm-season precipitation event . Wea. Forecasting , 21 , 149 – 166 , doi: 10.1175/WAF909.1 . 10.1175/WAF909.1 Zhang

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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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Nick Guy and David P. Jorgensen

phenomena (e.g., El Niño–Southern Oscillation, North Atlantic Oscillation, Indian Ocean dipole); discussed further in Lau and Waliser (2005) and Zhang (2005) . Given the extensive impact of the MJO on global circulations, it is important to correctly simulate the MJO in forecast and climate models. However, current model simulations do not represent the MJO well ( Lin et al. 2006 ; Benedict and Randall 2009 ). This is due in part to an incomplete understanding of convective dynamics and

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H. Bellenger, K. Yoneyama, M. Katsumata, T. Nishizawa, K. Yasunaga, and R. Shirooka

, 2006 : The mesoscale convection life cycle: Building block or prototype for large-scale tropical waves? Dyn. Atmos. Oceans , 42 , 3 – 29 , doi: 10.1016/j.dynatmoce.2006.03.003 . Masunaga , H. , 2013 : A satellite study of tropical moist convection and environmental variability: A moisture and thermal budget analysis . J. Atmos. Sci. , 70 , 2443 – 2464 , doi: 10.1175/JAS-D-12-0273.1 . Nasuno , T. , 2013 : Forecast skill of Madden–Julian Oscillation events in a global nonhydrostatic

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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

sea temperature measurements from ship, moorings, and a glider are then presented in section 3 to establish the large-scale and mesoscale moisture environments of two MJO events. Section 4 compares results from real-time and hindcast model runs made with a nested, cloud-resolving, and fully coupled (air–ocean–wave) version of the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS). Here specific attention is given to an examination of the effect of air–ocean coupling on the

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

Bretherton 2001 ; Tung and Yanai 2002a , b ; Lin et al. 2005 ). Using Doppler radar data, Houze et al. (2000) identified strong midlevel inflow in the stratiform regions of mesoscale convective systems (MCSs) during the westerly onset and in regions of strong westerly winds associated with the Kelvin–Rossby wave pattern. They postulated that the mesoscale inflow transports easterly momentum downward, reducing the westerlies near the surface in the westerly onset region, while in the strong westerly

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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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James H. Ruppert Jr. and Richard H. Johnson

population alone. For instance, while large-scale subsidence and horizontal moisture advection, exert control over column humidity, and therefore over moist convection, clouds can reduce column radiative cooling. This reduction can in turn reduce large-scale subsidence (e.g., Mapes 2001 ), assuming negligible temperature variations, thereby providing a link between clouds and the large-scale column moisture source ( Chikira 2014 ). Local processes that augment moist convection (e.g., mesoscale organized

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