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Rupa Kamineni, T. N. Krishnamurti, S. Pattnaik, Edward V. Browell, Syed Ismail, and Richard A. Ferrare

measurements during CAMEX, but in the future, this capability could be on several aircraft and eventually in space. In the meantime, global humidity profiling will be provided by the Aqua satellite; this points to a natural extension of the present work to examine the impact of moisture profiling datasets on global weather forecasts. A major thrust on mesoscale high-resolution modeling of hurricanes has emerged in recent years; see, for example, Kurihara et al. (1995 , 1998 ), Cocke (1998) , Zhang

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Michael L. Waite and Chris Snyder

use in ensemble prediction systems. Quart. J. Roy. Meteor. Soc. , 131 , 3079 – 3102 . Skamarock , W. C. , 2004 : Evaluating mesoscale NWP models using kinetic energy spectra. Mon. Wea. Rev. , 132 , 3019 – 3032 . Skamarock , W. C. , and J. B. Klemp , 2008 : A time-split nonhydrostatic atmospheric model for weather research and forecasting applications. J. Comput. Phys. , 227 , 3465 – 3485 . Skamarock , W. C. , J. B. Klemp , J. Dudhia , D. O. Gill , D. M. Barker

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Zhe-Min Tan, Fuqing Zhang, Richard Rotunno, and Chris Snyder

-generation Pennsylvania State University (PSU)–National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5; Dudhia 1993 ), ZSR02 investigated the 0–36-h forecast for the same case; MM5 forecasts initialized from several different operational initial analyses showed, consistent with Langland et al. (2002) , similarly good skill from a large-scale perspective, but rather large differences among the forecasts in their mesoscale precipitation patterns. By comparing simulations of the same case in which the

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Corey K. Potvin, Elisa M. Murillo, Montgomery L. Flora, and Dustan M. Wheatley

simulations of thunderstorms to horizontal resolution and turbulence parameterization . Quart. J. Roy. Meteor. Soc. , 141 , 433 – 448 , doi: 10.1002/qj.2363 . Wandishin , M. S. , D. J. Stensrud , S. L. Mullen , and L. J. Wicker , 2008 : On the predictability of mesoscale convective systems: Two-dimensional simulations . Wea. Forecasting , 23 , 773 – 785 , doi: 10.1175/2008WAF2007057.1 . Wandishin , M. S. , D. J. Stensrud , S. L. Mullen , and L. J. Wicker , 2010 : On the

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Jun-Ichi Yano and Mitchell W. Moncrieff

1. Introduction Although organized convection on scales of tens to hundreds of kilometers (mesoscales) is a well-recognized atmospheric process, the impact of the accompanying momentum transport on the large scales of motion is poorly understood and has not been much studied. The purpose of this paper is to investigate, at a fundamental level, the large-scale response of the tropical atmosphere to momentum transport by the most highly organized regime of deep convection, namely, squall lines

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Jason C. Knievel and Richard H. Johnson

1. Introduction In this paper we present a vorticity budget for a mesoscale convective vortex (MCV) generated by a mesoscale convective system (MCS) that traversed Kansas and Oklahoma on 1 August 1996. The budget discriminates between sources and sinks of vorticity within the mesoscale wind of the MCS and within the synoptic background wind. a. Background MCSs often generate MCVs in the lower and middle troposphere. An MCV's tangential wind speed is of orders 1 and 10 m s −1 , its diameter of

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R. Buizza and A. Montani

Atlantic storm-track. Preprints, Seventh Conf. on Mesoscale Processes, Reading, United Kingdom, Amer. Meteor. Soc., 391–392. Noble, B., and J. W. Daniel, 1977: Applied Linear Algebra. Prenctice-Hall, 477 pp. Palmer, T. N., R. Gelaro, J. Barkmeijer, and R. Buizza, 1998: Singular vectors, metrics, and adaptive observations. J. Atmos. Sci., 55., 633–653. Pu, Z.-X., E. Kalnay, J. Sela, and I. Szunyogh, 1997: Sensitivity of forecast error to initial conditions with a quasi-inverse linear

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Jun Peng, Lifeng Zhang, and Jiping Guan

1. Introduction The mesoscale dynamics producing the lower-stratospheric energy spectra, with a focus on the importance of moist processes, were investigated with numerical simulations of idealized moist baroclinic waves in a companion paper by Peng et al. (2015 , hereafter Part I) . In Part I , a newly developed formulation of the spectral energy budget by Peng et al. (2014) was employed to perform a detailed spectral budget analysis of kinetic energy (KE) and available potential energy

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R. Gelaro, R. Buizza, T. N. Palmer, and E. Klinker

., 123, 1007–1033. Ehrendorfer, M., and R. M. Errico, 1995: Mesoscale predictability and the spectrum of optimal perturbations. J. Atmos. Sci., 52, 3475–3500. ——, and J. J. Tribbia, 1997: Optimal prediction of forecast error covariances through singular vectors. J. Atmos. Sci., 54, 286–313. Errico, R. M., and T. Vukićević, 1992: Sensitivity analysis using an adjoint of the PSU–NCAR mesoscale model. Mon. Wea. Rev., 120, 1644–1660. ——, ——, and K. Raeder, 1993: Examination of the

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C. Cardinali and R. Buizza

1. Introduction Rapidly developing cyclones that form toward the end of the Atlantic and Pacific storm tracks are sometimes difficult to forecast. The sparsity of observational data over the oceans can result in analysis errors, which may grow rapidly in the ensuing forecast. Following the first ideas discussed at a workshop in 1995 ( Snyder 1996 ), several field experiments have been carried out to observe atmospheric circulations in traditionally data-sparse regions and to assess whether the

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