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

represented by a blend of homogeneous and isotropic covariances and 4D flow-dependent covariances derived from a global ensemble Kalman filter (EnKF). In this paper we report on the implementation of the same 4DEnVar scheme in the Regional Deterministic Prediction System (RDPS), a forecasting system based on a limited-area version of the Global Environmental Multiscale (GEM) model ( Côté et al. 1998 ) covering North America ( Fig. 1 ) with an approximate 10-km horizontal grid spacing. The previous RDPS

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María E. Dillon, Yanina García Skabar, Juan Ruiz, Eugenia Kalnay, Estela A. Collini, Pablo Echevarría, Marcos Saucedo, Takemasa Miyoshi, and Masaru Kunii

multianalysis, multimodel, and multiphysics configurations, providing useful and critical information to forecasters across the North American domain ( Du et al. 2014 ). Moreover, additional spread provided by the multischeme approach is state dependent (e.g., additional ensemble spread will be larger in areas where a particular parameterization is active, for example, over a heavy convective rain situation). In addition, multischeme ensembles tend to have lower biases than ensembles that use one single

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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

code. The impact of these changes to the assimilation of radiosonde data on forecast accuracy is positive and statistically significant at short range in the upper troposphere and lower stratosphere where the balloon drift can be large (as much as 200 km in the Northern Hemisphere winter season). The impact of the new static temperature bias correction from aircraft reports, not shown here, is significant over North America and Europe where a warm bias between 300 and 100 hPa can be clearly seen

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Norihisa Usui, Yosuke Fujii, Kei Sakamoto, and Masafumi Kamachi

assimilation systems (e.g., Cummings et al. 2009 ; Dombrowsky et al. 2009 ; Hurlburt et al. 2009 ), some of which adopt the variational assimilation scheme. Weaver et al. (2003) applied an incremental 3DVAR and 4DVAR approach to the Parallel Ocean model ( Madec et al. 1998 ) in the tropical Pacific Ocean. Ishikawa et al. (2009) developed a 4DVAR assimilation system with an eddy-resolving ocean general circulation model (OGCM) in the western North Pacific to represent realistic mesoscale features in

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Juanzhen Sun, Hongli Wang, Wenxue Tong, Ying Zhang, Chung-Yi Lin, and Dongmei Xu

-observation test. Such a test was performed by placing a single observation of u with an innovation (difference between observation and background) of 1 m s −1 on the 20th model level in the middle of the inner analysis domain shown in Fig. 1 . CV_ ψχ and CV_UV can result in different u increments due to their different BE covariance matrices. The background field for the single-observation experiments is the North American Mesoscale Forecast System (NAM) analysis at 0000 UTC 9 August 2008, which is the

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Hyo-Jong Song and In-Hyuk Kwon

, the error reduction around this area is significantly larger than others ( Fig. 10f ). As the background of specific humidity is distributed over tropical and low-latitudinal regions, the error reduction at high latitude was not obvious. Dense observation regions such as Europe, East Asia, and North America have positive error reductions ( Fig. 10g ) but the error reduction is mostly neutral over the Pacific Ocean because of a lack of Q observation ( Fig. 10h ). Fig . 10. (left) The background

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D. J. Lea, I. Mirouze, M. J. Martin, R. R. King, A. Hines, D. Walters, and M. Thurlow

coupled DA, while the August–September and October sets were longer 10-day forecasts originally chosen to correspond to the peak of the Indian monsoon and to the peak of the North Atlantic hurricane season, respectively. We primarily assess the forecasts by comparing them to observations. More detailed case studies are left for future work. Table 2. Experiments performed to test the impact of coupled data assimilation. The data assimilation experiments are assessed in the following sections by

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