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Nicholas J. Weber, Daehyun Kim, and Clifford F. Mass

the maximum integrated precipitation between 0600 UTC 22 Nov and 0000 UTC 27 Nov 2011. Using the global model runs from Weber and Mass (2019) , this study 1) examines the kinematic and thermodynamic structures of the simulated CCKW in the CPM simulation, and 2) evaluates the difference between the two model configurations (15- and 3-km simulations), with a particular focus on the wave–convection coupling. Section 2 details these model configurations, along with the verification datasets, the

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Xianan Jiang, Ngar-Cheung Lau, Isaac M. Held, and Jeffrey J. Ploshay

. , 118 , 151 – 163 . Frisch , A. S. , B. W. Orr , and B. E. Martner , 1992 : Doppler radar observations of the development of a boundary-layer nocturnal jet. Mon. Wea. Rev. , 120 , 3 – 16 . GFDL Global Atmospheric Model Development Team , 2004 : The new GFDL global atmosphere and land model AM2-LM2: Evaluation with prescribed SST simulations. J. Climate , 17 , 4641 – 4673 . Helfand , H. M. , and S. D. Schubert , 1995 : Climatology of the Great Plains low-level jet and its

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Joshua M. King, Christian D. Kummerow, Susan C. van den Heever, and Matthew R. Igel

RAMS rainfall occurrence results in Figs. 4 , 5 , and 8b remained qualitatively consistent for this averaged RAMS distribution. Therefore, we conclude that if progress is to be made in evaluating model performance in simulating shallow clouds and their rainfall on near-global scales, it is advantageous if the resolution of the simulation is readily verifiable against observations obtained over climatically relevant spatial and temporal scales, as is done here. While the CloudSat -derived

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Elinor R. Martin and Courtney Schumacher

convective events whereas the bulk and ZM groups overestimate the frequency of deep convective events. Much of this overestimation of precipitation is in deep convective regimes between −10 and −50 hPa day −1 . As for the regime sorting by SST, AMIP models using the spectral parameterizations outperform other parameterization groups in the Caribbean. However, this good performance of the spectral models is in part because the errors in the ω 500 PDF ( Fig. 5a ) are compensated by the errors in the

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Takamichi Iguchi, Teruyuki Nakajima, Alexander P. Khain, Kazuo Saito, Toshihiko Takemura, Hajime Okamoto, Tomoaki Nishizawa, and Wei-Kuo Tao

physics. To evaluate the performance of the model cloud microphysics, relaxation of the error is attempted by obtaining a temporal average of the result. A quantitative comparison is shown in the forms of the normalized dBZe–height histogram ( Fig. 4 ) and the line chart describing the vertical profiles of the 24-h-averaged dBZe that sampled the range from −40 to 20 dB ( Fig. 5a ). The radome correction for the dBZe measurement contributes +0.8 dB to the 24-h-averaged dBZe. Fig . 3. Time–height cross

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Xingqin Fang and Ying-Hwa Kuo

the fact that the values are much more sensitive than the ACC values to the short-range, small-scale forecast errors and are thus more useful for detecting the varying practical predictability of the model, especially during the early stage of forecasts. 6. Conclusions and discussion The ACC is one of the standard methods for the evaluation and comparison of the performance of global NWP models. The correlation coefficient of the forecast anomaly field with the analysis anomaly field both valid

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Tarek Ayash, Sunling Gong, and Charles Q. Jia

radiative fluxes in climate models can hardly be overemphasized, given that a radiative inaccuracy of even a few W m −2 can seriously compromise the evaluation of heating rates and climate forcings ( Fouquart et al. 1991 ). Several attempts have been made to evaluate the accuracy of the various solar radiation transfer schemes used in climate models. The Intercomparison of Radiation Codes in Climate Models (ICRCCM) international program ( Fouquart et al. 1991 ) showed that, with the same atmospheric

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Kentaroh Suzuki, Graeme L. Stephens, Susan C. van den Heever, and Takashi Y. Nakajima

. Factors that determine the time scale of this process, however, are not well understood (e.g., Khain et al. 2000 ), especially on the global scale. These water conversion processes are usually represented in numerical weather and climate prediction models by bulk parameterization schemes developed in terms of the mass and number densities of cloud water and rainwater. To improve our understanding and prediction of the precipitation formation process, it is essential to evaluate how realistically

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Jaison Thomas Ambadan and Youmin Tang

generated by adding normally distributed noise N (0, 2 ) to the true states. Also, the interval of observation is set to 10; i.e., the observed states are assimilated to the nonlinear model at every 10 time steps. A more detailed discussion of the model and its characteristics can be found in Lorenz (2006 , 2005 ) and Lorenz and Emmanuel (1998) . c. Performance and evaluation For all the cases to be discussed, we assume that the model and observation errors are uncorrelated in both space and time

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Nedjeljka Žagar and Istvan Szunyogh

1. Introduction Zhang et al. (2019 , hereafter Zetal2019 ) investigated the gap between the inherent and current-day practical limit of midlatitude weather predictability by perfect model experiments. Their methodology and conclusions raise a number of questions, which we summarize as follows: 1) the experiments of Zetal2019 do not provide realistic simulations of either the current-day operational, or the future ideal evolution of the forecast errors; 2) estimates of the extension of the

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