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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

TRMM data were also used to identify the atmospheric large-scale waves by applying the space–time FFT filtering technique ( Yang et al. 2003 ; Roundy 2008 ). This technique is also well known to provide an ability to separate the signals associated with the eastward (Kelvin, MJO, eastward MRG, and eastward inertio-gravity waves) and westward (equatorial Rossby, westward MRG, and westward inertio-gravity) propagating waves. The advantage of using TRMM precipitation is that the dataset contains only

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

features to be studied with this campaign. The importance of this preconditioning for deep convection associated with the MJO has been stressed by many observational (e.g., Johnson et al. 1999 ; Kikuchi and Takayabu 2004 ; Holloway and Neelin 2009 ) and modeling studies (e.g., Zhang and Song 2009 ; Cai et al. 2013 ). A possible consequence of our lack of understanding of the origin of this preconditioning is the limitation of the forecast skill of the timing of the MJO triggering. Indeed, forecast

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James H. Ruppert Jr. and Richard H. Johnson

indicated. Locations of the NCAR S-PolKa radar (Addu Atoll) and air–sea flux site (R/V Revelle ) are also indicated. A comprehensive set of quality-control techniques has been applied to the DYNAMO soundings, including mitigation of the low-level heat island and flow blocking effects in the Colombo soundings due to the large island of Sri Lanka ( Ciesielski et al. 2014a , b ). Following quality control, the sounding observations were horizontally interpolated onto a 1° mesh using the multiquadric

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Richard H. Johnson and Paul E. Ciesielski

(SST) up to 2°–3°C existed. It is becoming increasingly clear that a realistic representation of the boundary layer throughout the life cycle of the MJO is important for successful numerical simulations of the phenomenon. The sensitivity of simulations of the MJO to the treatment of the boundary layer was recently demonstrated by Qian et al. (2016) , who showed using the Weather Research and Forecasting (WRF) Model that the simulated precipitation and surface moisture fluxes over the Indian Ocean

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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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Weixin Xu and Steven A. Rutledge

; Awaka et al. 2009 ) uses a somewhat more complicated technique that considers both horizontal and vertical gradients of reflectivity, as well as the existence of a reflectivity bright band. The rain rate–reflectivity ( Z – R ) relationship for convective rain is Z = 134 R 1.44 . For stratiform rain, Z = 300 R 1.55 is used. These Z – R relationships (as well as the C-band attenuation correction algorithm) were derived from drop size distribution measurement on Gan Island (DYNAMO) and Manus

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Jun-Ichi Yano and Joseph J. Tribbia

performed in Hough-function space. Nonlinearities are evaluated in gridpoint space and converted back to Hough-function space (i.e., transformed spectrum method) and added as an extra forcing term for the evaluations of the temporal tendencies. Linear-wave tendencies are treated analytically using exponentials in the time integration, which allows a longer time step than otherwise. Time stepping is based on a leapfrog technique with an averaging procedure every 100 time steps. Otherwise, the code

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Weixin Xu and Steven A. Rutledge

horizontal and 0.5-km vertical grid. The 10-min output Cartesian-based radar volume covers 150 × 150 km 2 horizontally and 20 km in the vertical. 2) Radar-derived products Radar reflectivity was first classified into convective and stratiform precipitation components. The classification technique developed by Steiner et al. (1995) was applied to the gridded reflectivity field at 2-km altitude. Pixels with reflectivities greater than 40 dB Z are first classified as convective centers. Secondly, pixels

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

-term weather forecasts and long-term climate projections ( Waliser 2005 ). Observational studies of the seasonality of tropical intraseasonal variability have shown that the MJO signals migrate latitudinally with the seasonal cycle, peaking during boreal winter (e.g., Wang and Rui 1990 ; Wheeler and Hendon 2004 ; Zhang and Dong 2004 ; Masunaga 2007 ; Kikuchi et al. 2012 ). The strongest boreal winter MJO signals in deep convection and precipitation are asymmetric about the equator, especially in the

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

Maldivian islands are contained within the domain, the simulations were run as ocean only using weekly (8 day) observed Moderate Resolution Imaging Spectroradiometer (MODIS) SSTs ( Esaias et al. 1998 ). SSTs were therefore updated every 8 days. Between the 8-day MODIS SST updates, SSTs were linearly interpolated between the previous and next MODIS SST files. The control simulations were initialized using 0.25° × 0.25° European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA

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