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Ofer Shamir, Chen Schwartz, Chaim I. Garfinkel, and Nathan Paldor

is integrated for 40 years at a spectral resolution of T42, i.e., a triangular truncation where the highest retained wavenumber and total wavenumber both equal 42, and 40 evenly spaced pressure levels. The time–space analysis is performed on the last 38 years of the integration, after a 2-yr spinup time. d. Shallow-water model simulations To understand the governing mechanism behind the effects of upscale energy cascade on the parity distribution, we use the framework of the forced

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Brian E. Sheppard, Merhala Thurai, Peter Rodriguez, Patrick C. Kennedy, and David R. Hudak

the model components required for the forward simulation of the POSS Doppler spectrum. Section 4 explains the Doppler spectral modal frequency of occurrence matrix and plots. Section 5 explores the effect of various factors on the modal centroid distributions. Section 6 presents the multiple discriminant analysis (MDA) of the centroid distributions for the identification of type. Section 7 gives several case studies comparing POSS precipitation classification to collocated images of

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Linyun Yang, Shuyu Wang, Jianping Tang, Xiaorui Niu, and Congbin Fu

consistent with the enforced inflows and outflows at the boundaries ( Leung et al. 2003 ). When integrating over a large domain, the model biases could accumulate continuously in the inner domain and lead to the deviation of RCMs’ large-scale circulation from observations. To ensure the dynamic consistency between the simulated large-scale variables and the driving fields, the interior nudging technique, including grid nudging ( Bowden et al. 2012 ) and spectral nudging ( von Storch et al. 2000 ; Tang

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Giuliana Pallotta and Benjamin D. Santer

: information on global-scale TMT changes is available from three satellite research groups ( Mears and Wentz 2016 ; Zou et al. 2018 ; Spencer et al. 2017 ), and “synthetic” TMT has been calculated from over three dozen climate models ( Santer et al. 2018 ). Operating in the frequency domain has a number of advantages ( Vyushin and Kushner 2009 ). Spectral analysis facilitates model-versus-data comparisons of the overall spectral “shape,” the interdecadal variance relevant to detection of an anthropogenic

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Gustavo Naumann and Walter M. Vargas

dynamic forecasting models ( Straus and Shukla 1981 ; Lau and Chang 1992 ; Doblas-Reyes et al. 1998 ). Figure 2 shows that the variability in the 30- and 60-day bands for 1976 displays greater spectral energy during winter months. This result raises the question of whether this type of linear or nonlinear effect tends to be observed in a particular season. Figure 3 shows the distribution of the occurrence of significant spectral densities in the bandwidth between 30 and 60 days as a function of

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Hui Sun, Wei Zhao, Qingxuan Yang, Shuqun Cai, Xinfeng Liang, and Jiwei Tian

z , ω ) can be obtained finally. However, it is notable that for the component with a horizontal wavelength smaller than the minimum array spacing, the MLM may give a white azimuthal spectrum because of the aliasing caused by sparse array spacing. In such a case, only the integral of spectral energy along θ , not the distribution of spectral energy along θ , makes sense. 3. Validation and analysis of results a. Validation of 4D internal wave spectrum Based on the Fourier analysis, the

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S. Lakshmivarahan, John M. Lewis, and Junjun Hu

theoretical paper by Malkus and Veronis (1958) established the two-dimensional spectral framework that he followed. Saltzman (1962) meticulously developed the structure of a family of LOM ( n ) for n ≤ 52. This family of models has been the basis for numerous subsequent studies. The well-known LOM (3), known as the Lorenz 1963 model ( Lorenz 1963 ), is a member of this family of models as are the ones in Curry (1978) . By way of illustration, Saltzman (1962) concluded his paper with a preliminary

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Dale Durran, Jonathan A. Weyn, and Maximo Q. Menchaca

1. Introduction When computing spectra from observed or model data, the main focus is often on the slope of the spectrum as a function of wavenumber on a log–log plot. Nevertheless, if such spectra are to be quantitatively compared with those obtained in other studies (as in, e.g., Skamarock 2004 ; Hamilton et al. 2008 ), it is important to be able to correctly compute the magnitude of the energy spectral density. The goal of this article is to facilitate such comparisons. The discrete

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Juan Fang and Fuqing Zhang

inflow and updraft) are successfully captured by the Sawyer–Eliassen model ( Figs. 5e–h ). This is consistent with findings from the spectral analysis that the system-scale vortex of Dolly maintains approximate gradient balance. From Figs. 5e–h , one notices that, under the heating and momentum forcing in combination with the constraint of the inherent thermal wind balance, considerable system-scale convergent inflow developed in the lower troposphere. This is also consistent with the foregoing

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Nils P. Wedi, Mats Hamrud, and George Mozdzynski

transform method has been successfully applied at the European Centre for Medium-Range Weather Forecasts (ECMWF) for approximately 30 years, with the first spectral model introduced into operations at ECMWF in April 1983. Spectral transforms on the sphere involve discrete spherical harmonics transformations between physical (gridpoint) space and spectral (spherical harmonics) space. The spectral transform method was introduced to NWP following the work of Eliasen et al. (1970) and Orszag (1970) , who

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