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William C. Skamarock, Sang-Hun Park, Joseph B. Klemp, and Chris Snyder

European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) analyses from the T1279 operational configuration. The mesoscale transition in the spectra occurs at lower wavenumbers for higher levels in the stratosphere (see Burgess et al. 2013 , their Fig. 1), similar to that found in the MPAS results in Fig. 2 . In the stratosphere, the divergent component of the KE spectrum in the mesoscale is greater than the rotational component (see Burgess et al. 2013 , their Fig

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Fuqing Zhang, Y. Qiang Sun, Linus Magnusson, Roberto Buizza, Shian-Jiann Lin, Jan-Huey Chen, and Kerry Emanuel

. , 43 , RG2003, https://doi.org/10.1029/2004RG000158 . 10.1029/2004RG000158 Zhang , F. , and Y. Weng , 2015 : Predicting hurricane intensity and associated hazards: A five-year real-time forecast experiment with assimilation of airborne Doppler radar observations . Bull. Amer. Meteor. Soc. , 96 , 25 – 32 , https://doi.org/10.1175/BAMS-D-13-00231.1 . 10.1175/BAMS-D-13-00231.1 Zhang , F. , C. Snyder , and R. Rotunno , 2002 : Mesoscale predictability of the “surprise” snowstorm of

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Qiu Yang and Andrew J. Majda

transport between mesoscale disturbances and synoptic-scale propagating waves is also presented in the Weather Research and Forecasting (WRF) Model ( Khouider and Han 2013 ). There is still no clear understanding about scale interactions between synoptic-scale circulation and mesoscale disturbances. Particularly, how do mesoscale disturbances that propagate at various speeds and directions impact synoptic-scale circulation? Answering this question cannot only improve our understanding about multiscale

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Peter A. G. Watson, H. M. Christensen, and T. N. Palmer

to inform how convection parameterizations ought to be structured. However, the complexity of the dynamics means that it is difficult to deduce the best way to parameterize convection based on observations alone. A better indicator of whether a convection scheme is reasonable is a comparison of observations with output from an NWP or climate model using the scheme. Here we test whether the convection scheme of the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast

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Kerry Emanuel and Fuqing Zhang

1. Introduction Much attention has been directed in recent years toward improving tropical cyclone intensity forecasts ( Gall et al. 2013 ), especially since there has been comparatively little improvement in measures of intensity skill in the last few decades ( DeMaria et al. 2014 ). Within the community of scientists working on tropical cyclones, efforts have been directed toward improving dedicated tropical cyclone models (e.g., Gopalakrishnan et al. 2011 ), real-time in situ and remote

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Da-Lin Zhang and J. Michael Fritsch

considerable progress in the developmentand improvement of numerical models during past decades (see review paper by Anthes, 1983), the abilityto predict mesoscale convective systems (MCSs) andc 1988 American Meteorological Society262JOURNAL OF THE ATMOSPHERIC SCIENCESVOL. 45, NO. 2their precipitation still remains poor. It has been notedthat even when the forecast of large-scale circulationand pressure patterns is essentially correct, mesoscaleprocesses often produce embedded "weather" featuresthat depart

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Barry H. Lynn, Wei-Kuo Tao, and Frank Abramopoulos

. Sci., 33, 2152–2169. Kain, J. S., and J. M. Fritsch, 1992: The role of convective (trigger function) in numerical forecasts of mesoscale convective systems. Meteor. Atmos. Phys., 49, 93–106. Klemp, J. B., and R. Wilhelmson, 1978: The simulation of three-dimensional convective storm dynamics. J. Atmos. Sci., 35, 1070–1096. Konrad, T. G., 1970: The dynamics of the convective process in clear air as seen by radar. J. Atmos. Sci., 27, 1138–1147. Lynn, B. H., and W.-K. Tao, 2001

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Qiu Yang and Andrew J. Majda

Moncrieff 2009 ), most of which moved westward. For cluster–supercluster interactions, Yang and Majda (2018) accessed the upscale impact of embedded mesoscale tropical convection on CCKWs based on a simple multiscale model. Early studies about scale interactions of atmospheric flows included wave–mean flow interactions ( Andrews and McIntyre 1976a , b , 1978a , b , c ). Majda (2007) showed that nonlinear interactions across scales that drive the waves include eddy flux divergences of momentum and

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Hongyan Zhu and Alan Thorpe

1. Introduction Factors that contribute to uncertainty in numerical weather prediction are: 1) uncertainty in the physical laws governing atmospheric motions, notably in the numerical approximations used for their solution and the parameterizations of the unresolved motions; 2) uncertainty in the forecast initial conditions arising from systematic and random errors in the observations, inhomogeneity in the spatial and temporal coverage of observations, representativeness a given observation

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C. A. Reynolds and T. N. Palmer

-geostrophic constraints. Mon. Wea. Rev., 115, 998–1008. 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. Farrell, B., 1990: Small error dynamics and the predictability of atmospheric flows. J. Atmos. Sci., 47, 2409–2416. Fisher, M., and P. Courtier, 1995: Estimating the

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