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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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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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George N. Kiladis, Juliana Dias, Katherine H. Straub, Matthew C. Wheeler, Stefan N. Tulich, Kazuyoshi Kikuchi, Klaus M. Weickmann, and Michael J. Ventrice

can be optimized with appropriate one-sided filtering when tuned to a fully filtered PC time series. A similar procedure can also be employed for model output where OLR or precipitation from short-period runs used in forecast experiments would also require smoothing. As was pointed out by Kikuchi et al. (2012) , it is likely that the smoothing algorithm utilized here could be improved upon by using more sophisticated techniques for one-sided filtering, as described for example by Arguez et al

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Kunio Yoneyama, Chidong Zhang, and Charles N. Long

regionally stretched grid system technique ( Tomita 2008 ). These and other forecast products were delivered to and archived by the DYNAMO field catalog and available to the field operation in real time. Based on dynamical and statistical forecast products, an international MJO forecast team, led by NCEP CPC, conducted weekly teleconference briefings to field participants on past and current large-scale atmospheric conditions including MJO activities and outlook of potential MJO development in the coming

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

simulation and forecasting of the MJO ( Woolnough et al. 2007 ), and also errors in the mean climate. Ocean gliders are a relatively new technology for observing the ocean ( Eriksen et al. 2001 ). They can provide very high-resolution data right to the surface, without the drawbacks of a shipboard CTD, and are relatively inexpensive to operate. In this paper, the diurnal warm layer is analyzed using measurements from an ocean glider deployed as part of the Cooperative Indian Ocean Experiment on

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Samson M. Hagos, Zhe Feng, Casey D. Burleyson, Chun Zhao, Matus N. Martini, and Larry K. Berg

sounding arrays (multiple sounding sites enclosing an area) were available during AMIE/DYNAMO ( Johnson and Ciesielski 2013 ), they enclosed areas of roughly 8° × 8° that are significantly larger than a single ground-based precipitation radar. Because of the lack of a sounding array that corresponds to the size of the radar domain, the forcing data were developed based on the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) but constrained with the observed

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

experiments and observation dataset a. Numerical model and experiment design The Weather Research and Forecasting Model version 3.4.1 (WRF3.4.1; Skamarock et al. 2008 ) is used in this study. ERA-Interim ( Dee et al. 2011 ) is adopted to construct the initial, bottom, and lateral boundary conditions for the regional simulation. The lateral boundary consists of a narrow transition zone of 5 grid points, where the tendencies at the outmost grids are prescribed from ERA-Interim every 6 h, and gradually

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Zhe Feng, Sally A. McFarlane, Courtney Schumacher, Scott Ellis, Jennifer Comstock, and Nitin Bharadwaj

but does not detect clouds below 2-km height due to pulse compression techniques), with a maximum range of about 18 km. The single-pulse minimum observable reflectivity is −19.7 dB Z at 1 km and −15.7 dB Z at 10 km for general and cirrus modes, respectively. Spectral processing is used to enhance the sensitivity of the radar by performing an equivalent coherent integration in the spectral domain, which adds approximately 20 dB Z sensitivity to KAZR (i.e., KAZR data have a sensitivity of

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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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