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John E. Janowiak, Peter Bauer, Wanqiu Wang, Phillip A. Arkin, and Jon Gottschalck

predictions have been conducted over the past few decades. Janowiak (1992) evaluated the performance of short-range forecasts (12–36 h) of precipitation that were generated by the operational numerical weather prediction models (ca. 1989) at the European Centre for Medium-Range Weather Forecasts (ECMWF) and at the National Centers for Environmental Prediction (NCEP) in the United States. One of the conclusions in that paper was that while the models did a very good job of representing the seasonal and

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E. A. Irvine, S. L. Gray, J. Methven, and I. A. Renfrew

1. Introduction The aim of making targeted observations is to improve the forecast for a specified region through the addition of information in regions where the forecast is sensitive to initial-condition errors. Over the past 10 years or so field campaigns and idealized modeling studies have tested the idea that adding a small number of profile observations, over a limited area, can have a significant (positive) downstream impact on the forecast. The results of these studies

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Benoît Vié, Olivier Nuissier, and Véronique Ducrocq

convective-scale ICs and synoptic-scale LBCs, assuming the perfect-model hypothesis. For that purpose, it sets up a new framework in an operational context. Four distinct ensembles are designed to separately sample these two uncertainty sources, and are evaluated first over a one-month period and then for two specific case studies. Based upon the operational forecast suite at Météo-France, the high-resolution ensemble experiments benefit from convective-scale data assimilation in a 3-hourly rapid

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Elizabeth Satterfield and Istvan Szunyogh

of the performance of an ensemble forecast system: Predictability of the space of uncertanties . Mon. Wea. Rev. , 138 , 962 –– 981 . Szunyogh , I. , and Coauthors , 2007 : The local ensemble transform Kalman filter and its implementation on the NCEP global model at the University of Maryland . Proc. Workshop on Flow Dependent Aspects of Data Assimilation, Reading, United Kingdom, ECMWF, 47––63 . Szunyogh , I. , E. J. Kostelich , G. Gyarmati , E. Kalnay , B. R. Hunt , E

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Thomas M. Hamill, Jeffrey S. Whitaker, Michael Fiorino, and Stanley G. Benjamin

consistency between the error and spread, presuming the two are measured in similar ways. Accordingly, a consistent way of measuring spread S ( t ) for the i th sample, is where D i , j is the distance of the j th of n members from the ensemble-mean position. The average spread (i.e., “spread”) is defined as Note that model performance will only be evaluated with statistics across all basins; samples from the western, central, and eastern Pacific, and the

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Warren J. Tennant, Glenn J. Shutts, Alberto Arribas, and Simon A. Thompson

latitude. e. Model experiments and verification A number of month-long trials were designed to test the impact of SKEB2 on the MOGREPS performance. The main aim was to quantify the improvement in forecast spread and determine whether the skill of the ensemble-mean (EM) forecast also improved. By using the full MOGREPS system in the trials, the impact of SKEB2 on the cycling of the ETKF was also tested. To separately assess the relative contribution of model error estimates from SKEB2 and RP, and

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Mio Matsueda, Masayuki Kyouda, Zoltan Toth, H. L. Tanaka, and Tadashi Tsuyuki

extreme weather events related to blocking, the mechanism of blocking, and model performance in simulating blocking ( Matsueda 2011 , 2009 ; Matsueda et al. 2010 , 2009 ; Tyrlis and Hoskins 2008 ; Black et al. 2004 ; Carrera et al. 2004 ; Mauritsen and Källén 2004 ; Trigo et al. 2004 ; Pelly and Hoskins 2003a , b ; Quadrelli et al. 2001 ; Cash and Lee 2000 ; D’Andrea et al. 1998 ; Nakamura et al. 1997 ; Kimoto et al. 1992 ; Tanaka and Milkovich 1990 ; Shutts 1986 , 1983 ). It is well

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Ronald Gelaro, Rolf H. Langland, Simon Pellerin, and Ricardo Todling

analysis scheme and g is a vector in model space given by where 𝗠 b T and 𝗠 a T represent the adjoint of the forecast model evaluated along the trajectories x b f and x a f , respectively. The subscript N in (3) is used to distinguish this form of the impact calculation from augmented forms required for the EC and GMAO schemes, described below. With g given by (4) , (3) provides a nonlinear (essentially third order) approximation of δe in terms of d ( Errico

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William A. Komaromi, Sharanya J. Majumdar, and Eric D. Rappin

(if assumed) is perfect. In this paper, we employ a more direct approach to investigate the means by which TC forecasts may be modified via changes to the analysis, by subjectively perturbing the initial conditions in specified environmental features and integrating the forecast model forward. Initial perturbations are created via the amplification or weakening of relative vorticity within a chosen area and layer in the synoptic environment of the TC, followed by a rebalancing of the newly

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Sharanya J. Majumdar, Kathryn J. Sellwood, Daniel Hodyss, Zoltan Toth, and Yucheng Song

– 2820 . Szunyogh , I. , E. J. Kostelich , G. Gyarmati , E. Kalnay , B. R. Hunt , E. Ott , E. Satterfield , and J. A. Yorke , 2008 : A local ensemble transform Kalman filter data assimilation system for the NCEP global model. Tellus , 60 , 113 – 130 . Torn , R. D. , and G. J. Hakim , 2008 : Performance characteristics of a pseudo-operational ensemble Kalman filter. Mon. Wea. Rev. , 136 , 3947 – 3963 . Toth , Z. , and E. Kalnay , 1997 : Ensemble forecasting

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