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Shigenori Otsuka and Takemasa Miyoshi

are more skillful than initial condition ensemble simulations when the large-scale forcing is weak. In contrast, there are several ways to consider uncertainties in the initial condition and model imperfections simultaneously. Evans et al. (2000) employed two models and two objective analyses using the same 16 initial condition perturbations [multimodel and multianalysis (MMMA) ensembles], and reported that the MMMA ensemble forecasts significantly outperformed single-model single

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

modulated by the slow ocean component. Following their convention, a simulated year is made to correspond to an ocean regime, and the system oscillates between the normal regime, lasting between 3 and 12 yr, and an El Niño regime, lasting only 1 yr, equivalent to 240 time steps in the present experimental setup ( Peña and Kalnay 2004 ). Model error is simulated by altering the model parameters with respect to the nature. We have generated two distinct sets of parametric error: in the forcing parameter

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Patrick Nima Raanes, Alberto Carrassi, and Laurent Bertino

. This, however, renders it indistinguishable from a noise process, even from our omniscient point of view. Thus, this study effectively also pertains to natural noises not generally classified as model error, such as inherent stochasticity (e.g., quantum mechanics) and stochastic, external forcings (e.g., cosmic microwave radiation). Therefore, while model error remains the primary motivation, model noise is henceforth the designation most used. It is left to future studies to recuperate more

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James A. Cummings and Ole Martin Smedstad

Large et al. (1994) . In this study, the ocean model used 3-hourly Navy Operational Global Atmospheric Prediction System (NOGAPS) forcing obtained from the Fleet Numerical Meteorology and Oceanography Center (FNMOC), which includes air temperature at 2 m, surface specific humidity, net surface shortwave and longwave radiation, total (large scale plus convective) precipitation, ground/sea temperature, zonal and meridional wind velocities at 10 m, mean sea level pressure, and dewpoint temperature at 2

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

1. Introduction Forecasting systems for short-range weather and ocean prediction have been run separately at the Met Office for many years with the weather forecasts using prescribed ocean surface temperatures and sea ice fields, and with the ocean forecasts using atmospheric forcing fields from the Met Office’s numerical weather prediction (NWP) system. It has long been known that coupling between the various earth system components (the ocean, atmosphere, sea ice, and land) produces improved

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

days, which is the same as the IAU period used in MOVE-4DVAR-WNP, and it is performed on the 4–6th day in each assimilation window of MOVE-3DVAR-WNP or MOVE-4DVAR-WNP. Here we focus on a specific event in September 2011, and hence the experiments were conducted during the period from 1 August to 31 October 2011. The atmospheric forcing is JRA-25, which is the same as that used for the experiments WNP-3DVAR and WNP-4DVAR. In the following sections, we will evaluate model results using daily outputs

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David Halpern, Dimitris Menemenlis, and Xiaochun Wang

in the model, for example, short-period and tidal internal gravity wave motions and insufficient variability in the atmospheric forcing fields. The 95% statistically significant correlation coefficients were 0.83 and 0.78 at 140° and 110°W, respectively, which are indications that ECCO2 captured the main characteristics of variability of ADCP time series. For monthly-mean values, the correlation coefficients at 140° and 110°W were 0.89 and 0.76, respectively. The correlation coefficients for 3

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Daryl T. Kleist and Kayo Ide

–variational (EnVar)-based algorithm; Lorenc 2013 ] 1 although it is possible that one could utilize an alternate framework [e.g., an ensemble Kalman filter (EnKF)]. Many of these hybrid methods with technically different algorithms have been shown to be theoretically equivalent, whether using a combined covariance through brute force or through a variational-based control variable method ( Wang et al. 2007a ). Various studies have demonstrated that the hybrid algorithm can in fact improve upon stand

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Yicun Zhen and Fuqing Zhang

approximated true covariance Now we design experiments to compare algorithms A0 and A1 in a Lorenz-96 system ( Lorenz 2006 ). Here the system is configured to have 120 variables and 30 uniformly distributed observations, which lie on the model grid points. The external forcing F is set to 8, the time step dt is set to 0.05, and observations appear every two time steps. We use an error variance of 0.04 for all observations in these experiments. Observational operator H is simply the restriction

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