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Brian Mapes, Arunchandra S. Chandra, Zhiming Kuang, Siwon Song, and Paquita Zuidema

verifications of high-resolution simulations driven by NWP analyses (e.g., Takemi 2015 ; Hagos et al. 2014 ) or by sounding array–derived forcing sets. Given the sheer abundance of global operational data entering cutting-edge NWP, and the technical hurdles of research with such complex systems, special field data may not find their highest and best use there. Still, analysis needs some framework. Here we use only basic statistics (regression coefficients). The first step in any statistical analysis is a

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

momentum budget in modulating the circulation associated with the MJO, we regress the individual forcing terms shown in Fig. 4 , as well as the budget residual, at all grid points within the NSA onto TRMM 3B42 precipitation averaged over the NSA. Although special caution is required when interpreting the budget residual owing to contamination by errors in the other terms of Eq. (1) and unknown analysis increment, the residual in the free troposphere is considered to largely account for the effect of

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Michael S. Pritchard and Christopher S. Bretherton

–Julian oscillation, which is inextricable from a broad spectrum of nonlinear diabatic and dynamical feedback processes. Observational analysis and high degree of freedom global atmospheric modeling are thus especially attractive tools for understanding MJO physics. Recent experience with reanalyses and global numerical models has proved consistent with a moisture-mode paradigm and suggests an especially critical role for horizontal moisture advection in controlling the eastward propagation of the MJO. Tightening

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

and re-emit it from the high, cold cloud tops, leading to low OLR. These points form the outliers at high x and high y . Given the physical realism of these outliers, it is reasonable to exclude them from the regression analysis. Hence, the 5% of the data points that lie farthest from the regression lines are labeled with a blue circle in Fig. A1b . The regression was performed on the remaining 95% of the data, and the new regression line is shown by the green dashed line in Fig. A1b . This

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

datasets against them. Appendix A provides a detailed description of each variable in DYNAMO surface meteorology and flux dataset from the Research Vessel (R/V) Roger Revelle . The methods used for isolating and compositing equatorial waves and the MJO are described in section 3 , with more details in appendix B . In section 4 we present observed time series of daily and subdaily variability from DYNAMO and TOGA COARE, and the analysis of 27 years of the daily time–longitude structure air

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Walter M. Hannah, Brian E. Mapes, and Gregory S. Elsaesser

regression analysis. Fig . 11. ECMWF Lagrangian tendency binned by CWV over the equatorial Indian Ocean (10°S–10°N, 50°–100°E). The previous figures show that the Lagrangian tendency acts as a positive feedback onto the CWV when considered over a relatively large area and time period. Figure 12 further confirms that the relationship also holds for both short and long scales in space–time variability. Results for high and low temporal frequencies (blue and red lines, respectively) are isolated using a

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Aurélie J. Moulin, James N. Moum, and Emily L. Shroyer

production and buoyancy suppression roughly balance one another, leading to a near-steady state in ε . Details of the measurements and analysis methods ( section 2 ) are followed by a summary of the observations of temperature ( section 3 ) and turbulence ( section 4 ). We then provide a quantitative evaluation of decay and growth rates of turbulence via an evolution equation for ε ( section 5 ). We close with a short discussion of these results ( section 6 ). 2. Measurements The principal

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Hungjui Yu, Paul E. Ciesielski, Junhong Wang, Hung-Chi Kuo, Holger Vömel, and Ruud Dirksen

local time (LT) at Gan and the TPW dip at 0100 LT at Manus. We perform regression analysis of GPS and MWR TPW to better characterize the nature of the GPS dry bias ( Fig. 9 ). Here rain-contaminated MWR estimates, indicated with red pluses, are excluded in the analysis. While the GPS dry bias is present over the full range of TPW values at all sites, the smaller-than-one regression slopes indicate that the dry bias increases as the column moisture increases. Similar results were also observed from

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

in this first part. These objectives require meticulous analysis procedures, for convectively coupled tropical motions are highly nonlinear and multiscaled in time and space. Substantial advances in the understanding of tropical waves, MJO, and their linear theories have been guided for decades by linear methods, including Fourier-based space–time filtering, regression, and empirical orthogonal functions (EOFs) (e.g., Hayashi 1979 , 1982 ; Salby and Hendon 1994 ; Lau and Chan 1985 ; Kiladis

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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

, P. E. , and W. M. Frank , 2004 : Applications of a multiple linear regression model to the analysis of relationships between eastward- and westward-moving intraseasonal modes . J. Atmos. Sci. , 61 , 3041 – 3048 , doi:10.1175/JAS-3349.1 . Roundy , P. E. , and C. J. Schreck , 2009 : A combined wave-number–frequency and time-extended EOF approach for tracking the progress of modes of large-scale organized tropical convection . Quart. J. Roy. Meteor. Soc. , 135 , 161 – 173 , doi

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