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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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Hyo-Jong Song and In-Hyuk Kwon

1. Introduction In a massive computing environment, the parallel scalability that a numerical method can guarantee is a primary issue in developing the dynamical core for atmospheric modeling. If used effectively, a huge computing resource can allow us to achieve a resolution under which decoupling between global and regional modeling is not required, and a seamless approach to unified atmospheric modeling is possible. The spectral element method (SEM) using cubed-sphere grids (CSGs) is a

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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

, although the technique is limited to relatively calm atmospheric conditions in the lower atmosphere (e.g., Kusunoki 2002 ). For instance, Kawabata et al. (2007) reproduced the entire life cycle of an isolated cumulonimbus by assimilating clear-air echoes as environmental observations. Rennie et al. (2011) reported that assimilating clear-air echoes in 3D-Var had small but positive impacts for three convective cases. Another available technique for this purpose is Doppler wind lidar (DWL), which

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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

improvements were made to both the forecast model and the data assimilation system. Some significant changes include the following: extending the model and assimilation domain in the vertical to fully include the stratosphere starting on 22 June 2009 ( Charron et al. 2012 ); improving model forecasts of tropical cyclones starting on 12 July 2011 ( Zadra et al. 2014a ); adding new observations [Infrared Atmospheric Sounding Interferometer (IASI), SSMIS, and a reduced thinning of all radiances] starting on

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Lars Nerger

reduce the estimated state error variance. Whitaker et al. (2008) also compared the assimilation performance of the EnSRF with the LETKF when applied with a global atmospheric model and found only small differences. Similarly, Holland and Wang (2013) compared the LETKF with the EnSRF without particular observation ordering for the assimilation with a simplified atmospheric model. They found only small differences in the state estimates with slightly smaller errors in the LETKF estimates. While

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Hailing Zhang and Zhaoxia Pu

forecasting have been found with the assimilation of airborne in situ, satellite, and radar observations (e.g., Franklin and DeMaria 1992 ; Velden et al. 1992 ; Franklin et al. 1993 ; Burpee et al. 1996 ; Velden et al. 1998 ; Pu et al. 2002 ; Aberson and Etherton 2006 ; Zhao and Jin 2008 ; Pu et al. 2008 ; Pu and Zhang 2010 ; Zhang et al. 2011 ; Wang and Huang 2012 ; Wu et al. 2014 ). Among the literature on atmospheric data assimilation in hurricane studies, only a few papers (e.g., Zhao

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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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Nicholas A. Gasperoni and Xuguang Wang

westerly flow. The distance between the peaks of about 40° in longitude is likely a representation of underlying Rossby waves. For example, for an observation placed at a trough these additional peaks represent the adjacent ridges associated with a trough. As observations get closer to the tropics (e.g., observations 4 and 5), RCF begins to take a different, sometimes complex shape, including stretching eastward along the equator upstream of the main flow. Fig . 4. Examples of RCF functions for seven

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Andrew C. Lorenc, Neill E. Bowler, Adam M. Clayton, Stephen R. Pring, and David Fairbairn

imbalance within 4DEnVar. Thus, the increments produced by hybrid-4DEnVar are poorly balanced, making it necessary to deal with the high-frequency gravity–inertia waves that are generated when the increments are added to the model. 6 A simple method of filtering high-frequency oscillations is the IAU scheme ( Bloom et al. 1996 ). Here a fraction of the same 3D analysis increment is added each model time step over a time window. This weighted displacement has a time-filtering effect on the increments

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