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P. D. Thompson and W. Lawrence Gates

betweenpredicted and observed 24-hr height changes of only 0.62, show considerable synoptic "skill," especiallyover the eastern United States. These forecasts are felt to demonstrate conclusively the applicability ofnumerical prediction techniques to operational forecasting. Further research on the effects of both thephysical and mathematical approximations of the methods of numerical prediction is suggested.1. IntroductionIt is by now a widely accepted fact that the behaviorof large-scale disturbances in the

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D. L. A. Flack, P. A. Clark, C. E. Halliwell, N. M. Roberts, S. L. Gray, R. S. Plant, and H. W. Lean

1. Introduction Forecasting of convective events has had a “step change” in ability since the advent of convection-permitting models (e.g., Lean et al. 2008 ; Clark et al. 2016 ). In turn, this has led to improvements in the prediction of floods with a rapid rate of rise, i.e., both surface water and flash flooding (e.g., Roberts et al. 2009 ; Cuo et al. 2011 ). However, quantitative forecasting of convective precipitation still remains a key challenge due to uncertainty in spatial

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Xianglei Huang, Xiuhong Chen, Daniel K. Zhou, and Xu Liu

. Section 2 describes the techniques used to model the surface spectral emissivity. The comparison with available laboratory measurements is also presented in section 2 . Section 3 describes the construction of the global surface spectral emissivity dataset and provides comparisons with IASI retrievals. Section 4 evaluates the impact of the spectral emissivity dataset on the TOA radiation budget. Further discussions and conclusions are given in section 5 . 2. Modeling the surface emissivity

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C. A. Reynolds and T. N. Palmer

( Andersson et al. 1994 ; Baker et al. 1995 ) and the development of advanced analysis techniques such as four-dimensional variational data assimilation (4DVAR, Derber 1987 ; Rabier et al. 1993 ; Zupanski 1993 ; Rabier et al. 1996a ) and Kalman filtering ( Fisher and Courtier 1995 ; Ménard and Daley 1996 ). Several operational centers have also introduced ensemble forecasts as an attempt to deal quantitatively with the inevitable uncertainty in the initial analysis ( Toth and Kalnay 1993 ; Molteni

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Eugene J. Aubert, Iver A. Lund, and Albert Thomasell Jr.

application to meteorological prediction problems and were programmed foruse with electronic computers. These two techniquesare stepwise regression, also called screening [6] andfactor analysis, which employs empirical orthogonalfunctions [4]. The present paper describes a study totest the forecast utility of these techniques for certainmeteorological elements. It differs from most previousinvestigations because a wider variety of weatherelements and a shorter prognostic period were used.This study also

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R. M. White, D. S. Cooley, R. C. Derby, and F. A. Seaver

have recently been engaged in aninquiry into sea-level pressure forecasting. Among theproblems encountered in developing statistical forecasting techniques are those of a mathematical andprocedural nature as well as those which are moremeteorological in character. The first group ofproblems is concerned with the development ofmathematical procedures which enable one to dealwith large numbers of correlated variables and toextract their informational and predictive content inan efficient manner. The

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Jason A. Sippel and Fuqing Zhang

likely minimized the immediate impact to storm intensity. Indeed, similar development of Humberto was achieved in another EnKF analysis and forecast experiment (not shown) in which sea surface temperatures were updated hourly with U. S. Navy mesoscale analyses. The use of an EnKF for the analyses here is a major difference from the methodology of SZ08 . In the analysis of SZ08 , a cold-start technique was used, which possibly resulted in some artificial overreaction of initial convection to ambient

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R. Buizza and A. Montani

the forecast state as cost function and proposed that observations should be targeted in the region where the sensitivity field is maximum. This proposal is closely related to the singular vector technique proposed in this paper, both being adjoint-based methods [on this aspect see also Gelaro et al. (1998) ]. Note that a similar technique, based on the use of a quasi-inverse linear model, was also proposed by Pu et al. (1997 , 1998) . Bishop and Toth ( 1998) introduced the ensemble transform

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David Orrell

1. Introduction Ensemble techniques have become established in recent years as a method for generating probabilistic weather forecasts. By running forward an array of slightly perturbed initial conditions, the ensemble forecast is intended to provide an approximation to the probability density function of the weather’s future state ( Palmer 2000 ). While ensemble schemes have proved to be useful tools in understanding the role of initial condition error for weather models, their verification

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H. J. Eskes, A. J. M. Piters, P. F. Levelt, M. A. F. Allaart, and H. M. Kelder

consistent with the dynamics of the model. Because of this we showed the analyzed fields at t = T /2. This efficient use of the data results in a large improvement of the estimated error, as shown in Fig. 9 . In some sense up to twice as many measurements are available in the 4D-Var approach as compared to sequential assimilation techniques. In the present approach we avoided the problem of distributing the forecast–measurement mismatch in the total column over the various vertical layers of a 3D

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