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H. Bellenger, R. Wilson, J. L. Davison, J. P. Duvel, W. Xu, F. Lott, and M. Katsumata

/V Mirai , some of which have been noted by Kerns and Chen (2014) (between 30 November–1 December for Gan and 21–24 November for Diego Garcia and R/V Mirai ). To characterize turbulence over open ocean (i.e., with minimal land influence), we concentrate on observations obtained from ships and from very small and flat atoll islands. We also limit the ship dataset to stationary periods for simplicity (this impacts only marginally the number of soundings that are used). Table 1 summarizes observation

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Elizabeth J. Thompson, Steven A. Rutledge, Brenda Dolan, and Merhala Thurai

China Sea, as well as a mean of many west Pacific warm pool events. A separation line between convective and stratiform rain was determined by BR09 using the Darwin, Australia, datasets. DSDs were considered convective (stratiform) if N w was greater (less) than a naturally emerging separator line: log 10 = −1.6 D 0 + 6.3. This partitioning method was found to be consistent with data from selected rain events in BR03 and with more data from Darwin by TH10 and Penide et al. (2013) . TH10

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Richard H. Johnson, Paul E. Ciesielski, James H. Ruppert Jr., and Masaki Katsumata

.g., Ooyama 1990 ). However, owing to the lack of direct measurements of microphysical processes and precipitation rates over the sounding arrays, we use the formulation of Yanai et al. (1973) . 2 In addition to the cirrus cloud issue, it has been pointed out to the authors by Adam Sobel that the lower boundary for the surface longwave flux in CombRet was taken to be land, which is unrepresentative of the surrounding ocean and differs from the corresponding CERES value by several tens of watts per

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James H. Ruppert Jr. and Fuqing Zhang

1. Introduction As a dominant source of latent heating, the tropical Maritime Continent (MC) exerts profound influence over global circulation ( Ramage 1968 ; Krishnamurti et al. 1973 ). The persistence of upward motion here likely owes to its unique abundance of land, sea, and coastline: the resulting combination of moisture availability from a warm sea surface and strong diurnal solenoidal circulations ensures daily thunderstorm activity, and hence strong tropospheric heating in the mean

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Richard H. Johnson and Paul E. Ciesielski

. In an investigation for a limited period of DYNAMO, Chen et al. (2016) found using aircraft dropsonde data that the height of the atmospheric boundary layer was ~100 m greater during the suppressed phase of the November DYNAMO MJO than during the convectively active phase. This observation is consistent with the findings of Johnson et al. (2001) , who used sounding data from the Tropical Ocean and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) to investigate the

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Emily M. Riley Dellaripa, Eric Maloney, and Susan C. van den Heever

with the microphysics and surface schemes. The Land Ecosystem–Atmosphere Feedback model, version 3 (LEAF-3), submodel within RAMS is used to represent surface–atmosphere heat and moisture exchange ( Walko et al. 2000 ). The RAMS simulations were approximately centered over the DYNAMO northern sounding array (NSA; Fig. 1 ). Simulations were run at two resolutions. A 1.5-km horizontal simulation was conducted with interactive LHFLXs to evaluate the convective-scale relationship of MJO precipitation

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Naoko Sakaeda, Scott W. Powell, Juliana Dias, and George N. Kiladis

). However, many details of the relationship between the diurnal cycle of rainfall and the MJO remain to be answered. The physical processes underlying the relationship between the MJO and the diurnal evolution of cloud and rain types were unclear in Sakaeda et al. (2017) because the analysis was limited to satellite estimates of cloud types and rain rates. Here we extend the results of Sakaeda et al. (2017) by using observations collected during the Dynamics of the MJO (DYNAMO; Yoneyama et al. 2013

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Weixin Xu, Steven A. Rutledge, Courtney Schumacher, and Masaki Katsumata

al. (2014) examined the cloud/precipitation and moisture evolution during the MJO events and convectively coupled Kelvin waves (KWs) using the Shared Mobile Atmospheric Research and Teaching Radar (SMART-R) also deployed on Gan. The KW moisture signatures were found to vary during different MJO periods ( DePasquale et al. 2014 ). During the suppressed MJO period, moisture builds up vertically prior to the KW passage, but this signature was not clear during the developing and active MJO periods

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Yue Ying and Fuqing Zhang

as the prediction skill, is the ability to predict given realistic uncertainties in both the forecast model and initial and boundary conditions ( Lorenz 1982 , 1996 ; Zhang et al. 2002 , 2006 ) that can both be large at present. The limit of practical predictability can potentially be extended through the use of more accurate initial conditions (resulting from better data assimilation methods and/or observations) and/or better forecast models (better model physics, numerics, and/or resolution

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Wen-wen Tung, Dimitrios Giannakis, and Andrew J. Majda

temperature ( T B ) data over the tropical belt, extracted using an averaging method (e.g., Yanai and Murakami 1970 ; Wheeler and Kiladis 1999 ). The differences and similarities of the associated spatiotemporal patterns elucidate the interactions of the MJO with other important weather and climate processes, including the diurnal cycle and ENSO. Our methods and results are presented in a two-part series, in which the present paper addresses the extraction of spatiotemporal modes of variability

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