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Shigenori Otsuka and Takemasa Miyoshi

) have been studied in recent years. For example, Matsueda et al. (2006) reported that ensemble-mean forecasts of multicenter grand ensembles outperformed single-center ensemble-mean forecasts. Utilizing a multimodel ensemble in ensemble-based data assimilation methods has also been studied (e.g., Fujita et al. 2007 ; Meng and Zhang 2007 ; Houtekamer et al. 2009 ). Fujita et al. (2007) showed that an experiment with an ensemble Kalman filter (EnKF) using different initial and boundary

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

integration of UV , which can potentially introduce nonphysical errors to its analysis increment fields. In addition, they pointed out that another difficulty in using ψχ as control variables on a limited-area domain arises from the treatment of lateral boundary conditions in solving the Poisson equations to convert between ψχ and the forecast variables UV . The study by Xie and MacDonald (2012) raises the question whether using ψχ as the control momentum variables is still beneficial when an

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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

components that were modified in the new 4DEnVar-based system relative to the 4DVar-based system that became operational on 13 Feb 2013. a. Replacement of 4DVar with 4DEnVar The current 4DVar approach was replaced by 4DEnVar using a configuration very similar to what was described and evaluated by Buehner et al. (2013) . In that study, the impact of only changing the data assimilation approach from 4DVar to 4DEnVar was evaluated. Compared with the experimental configuration of 4DEnVar tested in that

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

short-term variations compared to the present 3DVAR system, which are considered to be important in coastal areas. Actually, Vialard et al. (2003) showed that 4DVAR significantly enhances short-term variability at a 30–40-day time scale related to tropical instability waves by comparing assimilated fields of their 3DVAR and 4DVAR assimilation systems in the tropical Pacific. Such an effect of 4DVAR would be expected in midlatitude western boundary current regions, which exhibit energetic mesoscale

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

. Since there is currently no operational equivalent to the global EnKF ( Houtekamer et al. 2014 ) at the regional scale at EC, we simply based our 4DEnVar scheme for the RDPS limited-area analysis on the use of 4D ensemble covariances derived from the global EnKF as in the GDPS configuration described in Part I . Our approach is thus similar to the National Centers for Environmental Prediction (NCEP), which recently replaced the 3DVar scheme in the North American Mesoscale Forecast System (NAM) and

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Stefano Migliorini

1. Introduction Over the last decade or recent decades there has been a formidable increase in the amount of data that is being acquired by satellite sounding instruments and disseminated to operational meteorological centers for assimilation, particularly in the infrared spectral region. At ECMWF, the infrared sounding instruments that are currently monitored or assimilated are the High Resolution Infrared Radiation Sounder (HIRS), on board the EUMETSAT Polar System MetOp polar

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Daryl T. Kleist and Kayo Ide

et al. 2002 ; Kleist et al. 2009b ) that has been made operational for several NCEP applications including the global data assimilation system (GDAS) and initialization of the GFS model, used to produce global medium-range deterministic forecast guidance as well as boundary conditions for other applications. Although a variety of minimization algorithms exist within the code, a preconditioned double-conjugate gradient solver with a full-rank background error covariance ( Derber and Rosati 1989

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Hailing Zhang and Zhaoxia Pu

(GTS), profiler and U.S. radar-derived winds, Special Sensor Microwave Imager (SSM/I) oceanic winds and total cloud water (TCW) retrievals, and satellite wind data from the National Environmental Satellite Data and Information Service (NESDIS). We noticed that the NCEP conventional observations do not include surface mesonet data over land during our study period. Surface mesonet observations are currently not commonly assimilated into NCEP operational global and regional models. Prior to Katrina

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Patrick Nima Raanes, Alberto Carrassi, and Laurent Bertino

is not to provide a full comparison of all of the alternatives under all relevant circumstances, but to focus on the square root approach. Techniques not considered any further here include using more complicated stochastic parameterizations ( Arnold et al. 2013 ; Berry and Harlim 2014 ), physics-based forcings such as stochastic kinetic energy backscatter ( Shutts 2005 ), relaxation ( Zhang et al. 2004 ), and boundary condition forcings. c. Framework Suppose the state and observation, and

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

deployments. For example, this information could impact potential targeting scenarios, such as activating and focusing satellite rapid-scan operations, or directing aircraft assets. In current operational NWP centers, AMV data from various geostationary and polar-orbiting platforms are routinely assimilated mostly via variational data assimilation systems in addition to conventional and additional satellite observations including radiance (for more information, please visit http

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