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Simon P. de Szoeke, James B. Edson, June R. Marion, Christopher W. Fairall, and Ludovic Bariteau

is balanced by adiabatic cooling of buoyant rising air (e.g., Riehl and Malkus 1958 ), and convection quickly redistributes moist static energy anomalies from the boundary layer throughout the troposphere to an equilibrium temperature profile. Neelin et al. (1987) and Emanuel (1987) developed quasi-equilibrium models with wind–evaporation or wind-induced surface heat exchange (WISHE) to explain growth and eastward propagation of convective anomalies. In WISHE, evaporation from the ocean

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

. The NSA region is located just east of the Maldive Islands, which are known to influence the surface current and wave propagation in the equatorial Indian Ocean (e.g., Yoon 1981 ; Han et al. 1999 ; Han 2005 ). The Maldives are not resolved at our 40-km resolution grid, and this can influence the model–data comparison of the currents and SST in the NSA region. We will focus on the SST, heat flux, and convection during these two 5-day periods of the suppressed and active phases. A slight

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Tim Li, Chongbo Zhao, Pang-chi Hsu, and Tomoe Nasuno

1. Introduction The Madden–Julian oscillation [MJO; i.e., the intraseasonal oscillation (ISO)] is the most prominent mode of intraseasonal variability in the tropics. First identified by Madden and Julian (1971) using single station data from Canton Island (2.5°S, 171.4°W), the MJO is characterized by a wavenumber one structure with a thermal direct vertical cell propagating eastward along the equator ( Madden and Julian 1972 ). Later analysis of satellite data, such as outgoing longwave

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Adrian J. Matthews, Dariusz B. Baranowski, Karen J. Heywood, Piotr J. Flatau, and Sunke Schmidtko

convection over the ocean can behave similarly to that over land, with a strong diurnal cycle, growth of cumulus congestus, and a precipitation maximum in the late evening ( Johnson et al. 1999 ). This diurnal variability can then impact onto longer time scales, such as the MJO ( Bellenger and Duvel 2009 ). For example, precipitation over the seas and islands of the Maritime Continent is predominantly accounted for by the diurnal cycle. Over this region, 80% of the MJO precipitation signal is directly

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Paul E. Ciesielski, Richard H. Johnson, Wayne H. Schubert, and James H. Ruppert Jr.

temporal resolution covering the domain 20°N–20°S, 35° –155°E. These fields, constructed using multiquadric objective analyses ( Nuss and Titley 1994 ), are referred to here as the CSU V3b (Colorado State University version 3b) gridded product. Input to these analyses came primarily from quality-controlled, bias-corrected, high vertical-resolution sounding data ( Ciesielski et al. 2014a ). A procedure was developed to mitigate heat island and flow blocking effects on Colombo sounding data ( Ciesielski

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Kai-Chih Tseng, Chung-Hsiung Sui, and Tim Li

. Section 4 presents the diagnostic moisture budget of the MJO. Section 5 presents concluding remarks. 2. Data and method In this study, we use the following three datasets for the DYNAMO/CINDY period from 1 October to 31 December 2011. a. Sounding observations The DYNAMO/CINDY upper-air sounding network comprises two quadrilateral arrays, one north and one south of the equator ( Fig. 1 ), with a total of six sounding sites, including three atolls, one island, and two scientific ships. Intensive

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Shuguang Wang, Adam H. Sobel, Fuqing Zhang, Y. Qiang Sun, Ying Yue, and Lei Zhou

islands over the Maritime Continent (e.g., Sumatra). These fast signals are in phase with diurnal variations, and closely related to the so-called 2-day waves ( Zhou and Kang 2013 ; Tulich and Kiladis 2012 ), which were observed by the precipitation radar deployed at the Gan radar supersite during DYNAMO (e.g., Zuluaga and Houze 2013 ). The high-frequency variability in the time–longitude diagram of 3-hourly precipitation is also apparent in spectral space. Figure 6 shows the wavenumber

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