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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

) . At this stage, we are using a relatively coarse resolution in order to test the system, while as a next step we plan to use a higher-resolution model where local observations such as radar data could also be included. The RNWP system that we are testing here will also provide evolving ensemble boundary conditions for a nested higher-resolution DAS. The main purpose of this work is to compare two possible implementations of the regional data assimilation system based on the single-model and

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Robin J. T. Weber, Alberto Carrassi, and Francisco J. Doblas-Reyes

ranges of several weeks, months, or perhaps years. Where the accuracy of numerical weather predictions are determined by error in the initial conditions, centennial climate projection evaluations are determined by boundary conditions such as atmospheric greenhouse gas concentrations, while the signature of the initial condition is lost ( Hawkins and Sutton 2009 ). Seasonal-to-decadal prediction spans time horizons of up to approximately 10 years, falling between numerical weather prediction and

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David Halpern, Dimitris Menemenlis, and Xiaochun Wang

conditions for the Baseline 2009–11 simulation were the end state of the Optimized 2004–05 solution. The 2004 and 2009 initial conditions were then modified, along with surface boundary conditions, by the adjoint method optimization ( Wunsch and Heimbach 2007 ). The initial conditions influence the mean stratification and magnitudes of EUC and NECC, but they have little impact on transport variations. In the adjoint method, a scalar cost function, which measures the quadratic distance between

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

), the relative mixing ratio of rainwater, and the pseudorelative humidity calculated using the saturation mixing ratio of water vapor that is defined in the background field (only for lateral boundary conditions). d. Experimental design To investigate the effects of DWL assimilation, we conducted three assimilation experiments using data denial. In the first experiment, a forecast was initiated with the first-guess field (hereafter NODA); that is, NODA was a downscaled forecast without assimilation

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James A. Cummings and Ole Martin Smedstad

1. Introduction Assessment of the impact of observations on reducing ocean model forecast error from data assimilation is a fundamental aspect of any ocean analysis and forecasting system. The purpose of assimilation is to reduce the model initial condition error. Improved initial conditions should lead to an improved forecast. However, it is likely that not all observations assimilated have equal value in reducing forecasting error. Estimation of which observations are best and the

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Stefano Migliorini

forward-model uncertainty or with characteristics that make them more sensitive to misspecifications of forecast error uncertainty in observation space (e.g., with multiple gas sensitivities or with Jacobians that have multiple peaks or long tails)—was used at ECMWF ( Collard 2007 ) to determine an optimal set of (currently 373) IASI channels sensitive to atmospheric temperature, water vapor, ozone, and surface conditions in the clear sky for operational monitoring or assimilation. The impact on

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Hailing Zhang and Zhaoxia Pu

performed in the 27- and 9-km domains, while all forecasts are conducted in all three domains. The initial conditions from 3 km are interpolated from the 9-km domain. All domains have 37 vertical levels from the surface to 50 hPa. Results shown in all figures are from the 3-km domain. Physical parameterization options include the WRF single-moment 6-class microphysics scheme (WSM6; Hong and Lim 2006 ), the Mellor–Yamada–Janjic (MYJ) planetary boundary layer scheme ( Mellor and Yamada 1982 ), the Kain

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Shigenori Otsuka and Takemasa Miyoshi

conditions combined with different model physics schemes outperformed experiments with different initial and boundary conditions only or with different model physics schemes only. In their experiments, the initial condition ensemble produced a larger spread in the locations of synoptic systems, whereas the model physics ensemble produced a larger spread in temperature and moisture fields. Combining these two represented the uncertainties better. Meng and Zhang (2007) conducted observing system

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Patrick Nima Raanes, Alberto Carrassi, and Laurent Bertino

) with the background from the climatology; and (iii) the extended Kalman filter ( Rodgers 2000 ). a. The linear advection model The linear advection model evolves according to for , , with , and periodic boundary conditions. The dissipative factor is there to counteract amplitude growth due to model noise. Direct observations of the truth are taken at equidistant locations, with , every fifth time step. The initial ensemble members, , as well as the truth, , are generated as a sum of 25

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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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