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Mark Buehner, Ron McTaggart-Cowan, Alain Beaulne, Cécilien Charette, Louis Garand, Sylvain Heilliette, Ervig Lapalme, Stéphane Laroche, Stephen R. Macpherson, Josée Morneau, and Ayrton Zadra

1. Introduction For more than a decade, numerous operational numerical weather prediction (NWP) centers have had significant improvements in analysis and forecast accuracy by adopting the four-dimensional variational data assimilation (4DVar) approach (e.g., Rabier et al. 2000 ; Rabier 2005 ; Rawlins et al. 2007 ; Gauthier et al. 2007 ). In parallel with these developments, a significant research and development effort has led to the successful application of the ensemble Kalman filter

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Jean-François Caron, Thomas Milewski, Mark Buehner, Luc Fillion, Mateusz Reszka, Stephen Macpherson, and Judy St-James

1. Introduction In the first part of this study ( Buehner et al. 2015 , hereafter Part I ), the latest modifications to the Global Deterministic Prediction System (GDPS) implemented operationally at Environment Canada (EC) in the fall of 2014 were described. The most notable change is the replacement of the four-dimensional variational data assimilation (4DVar) scheme by a four-dimensional ensemble–variational data assimilation (4DEnVar) scheme in which the background-error covariances are

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James A. Cummings and Ole Martin Smedstad

determined by estimating changes in the forecasts compared to a run when all observations are assimilated ( Oke and Schiller 2007 ; Balmaseda and Anderson 2009 ; Lellouche et al. 2013 ; Lea et al. 2014 ). Given the large number of ocean observing systems, an OSE is computationally a very expensive method for determining the value of observations assimilated if it is applied systematically to all observation datasets. An OSE has a further disadvantage in that modifications to the observing systems

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Takuya Kawabata, Hironori Iwai, Hiromu Seko, Yoshinori Shoji, Kazuo Saito, Shoken Ishii, and Kohei Mizutani

1. Introduction Numerical weather prediction (NWP) technologies can reduce the damage to human lives and social resources caused by heavy rainfalls; their successes have however been confined to heavy rainfalls induced by strong forcings, such as large-scale low-pressure systems, fronts, and orography. Operational NWP systems have a limited capacity to forecast small-scale heavy rainfalls (10–50 km) with weak forcings owing to their coarse resolution, parameterization of cumulus convection, and

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Stefano Migliorini

difference between this work and that discussed in Martinet et al. (2014) is that the sensitivity of channel selection results on cloud is assessed in this study in the assumption that the cloud fields (cloud fraction and liquid and ice water contents) are not part of the data assimilation system control vector, so as to be consistent with the assumptions currently made within operational numerical weather prediction (NWP) data assimilation systems. Also, in this study the iterative channel selection

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Yicun Zhen and Fuqing Zhang

covariance. Some of the sampling errors can be eliminated when the physical correlation radius is small compared to the spatial dimension of the state, which is usually the case in numerical weather prediction. Houtekamer and Mitchell (2001) and Hamill et al. (2001) used localization to remove spurious correlations between distant variables. The localization method can be used in all ensemble-based filters and it has been shown to be a powerful tool for limiting sampling error due to a small ensemble

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Daryl T. Kleist and Kayo Ide

1. Introduction Four-dimensional variational data assimilation (4DVar) techniques that use tangent-linear ( Lewis and Derber 1985 ; Courtier et al. 1994 ) or linear perturbation models ( Rawlins et al. 2007 ) and their corresponding adjoints have been shown to be powerful natural extensions to the 3DVar technique. In fact, 4DVar is the method of choice for initialization of single deterministic numerical weather prediction (NWP) applications at many operational centers ( Rabier et al. 2000

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David Halpern, Dimitris Menemenlis, and Xiaochun Wang

, wind stress and its curl are modified by the data assimilation. Because the OGCM is nonlinear, several such forward-adjoint iterations are needed to reach a solution that is statistically consistent with model and data error estimates. Table 1 lists assimilated datasets and prescribed errors in the cost function, which differ for 2004–05 and 2009–11. The European Centre for Medium-Range Weather Forecasts (ECMWF) operational analysis and the Japan Meteorological Agency (JMA) reanalysis of surface

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Ting-Chi Wu, Christopher S. Velden, Sharanya J. Majumdar, Hui Liu, and Jeffrey L. Anderson

1. Introduction Atmospheric motion vectors (AMVs) are proxies for the local horizontal wind, and are derived from sequential multispectral satellite images by tracking the motion of targets that include cirrus clouds, gradients in water vapor, and lower-tropospheric cumulus clouds ( Velden et al. 1997 ). AMV data are assimilated routinely into operational global numerical weather prediction (NWP) systems, and have been found to improve forecasts of tropical cyclone (TC) tracks (e.g., Goerss

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