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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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James A. Cummings and Ole Martin Smedstad

, which are used as a proxy for the circulation field. Flow dependence is necessary in the analysis, since error correlations across an ocean front are expected to be characteristically shorter than error correlations along the front. A similar tensor is used to account for the influence of coastlines in the analysis by rotating and stretching horizontal correlations along the coast while minimizing or removing correlations into the land. Background error correlations close to the coast are expected

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

for further details on the model configuration used in the RDPS, especially regarding the parameterization of the physical processes. Both the LAM and the 33-km driving component use the Interactions between Soil, Biosphere, and Atmosphere (ISBA) land surface scheme, which has its own data assimilation and cycling strategy [see Bélair et al. (2003a , b) for complete details]. In the 33-km driver, the surface variables from the GDPS at T − 6 h are used to initialize the DB forecast (see Fig. 2

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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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Hailing Zhang and Zhaoxia Pu

–Fritsch cumulus parameterization scheme ( Kain and Fritsch 1992 ), the Noah land surface model ( Chen and Dudhia 2001a , b ), the Rapid Radiative Transfer Model (RRTM; Mlawer et al. 1997 ), and the longwave and Dudhia shortwave radiation scheme ( Dudhia 1989 ). The cumulus scheme is used only in the 27- and 9-km grid spacing domains (domains 1 and 2). A deterministic forecast was first conducted at 0000 UTC 25 August with WRF initialized by the NCEP GFS Final Analysis (FNL) at 0.5° × 0.5° resolution. Figure

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

domain show wind observation stations used in the verification (see section 4a ). Grayscale indicates surface elevation. Fig . 2. Upper sounding data at 0900 JST 5 Jul 2010 at Tateno. The MCS that produced the Itabashi rainfall event, MCS A ( Fig. 3 ), initiated west of Tokyo around noon, then traveled slowly eastward. It is clear that the MCS was not maintained by orographic effects because the MCS traveled over flat land ( Fig. 1 ). Around 1600 JST, the MCS approached the DWL instrument installed

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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

(EnKF) to NWP for ensemble prediction (e.g., Houtekamer et al. 2014 ). Combinations of variational data assimilation with the EnKF have recently been used to obtain additional improvements in deterministic analysis and forecast accuracy. For example, the Met Office in the United Kingdom recently modified their operational global 4DVar system to include flow-dependent background-error covariances estimated from EnKF ensembles of background states ( Clayton et al. 2013 ). Similarly, the National

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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

1. Introduction Forecast errors from numerical weather prediction (NWP) models arise in part from imperfect initial conditions, as a result of the lack of sufficient observations as well as their suboptimal use. Different data assimilation systems (DASs) have been developed since the objective analysis of meteorological fields was introduced in the midtwentieth century; for example, Cressman (1959) developed the empirical successive corrections method and Gandin (1963) introduced optimal

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Kazumasa Aonashi, Kozo Okamoto, Tomoko Tashima, Takuji Kubota, and Kosuke Ito

experiments using real observation data. Conversely, further examination of the NE forecast perturbations is required, in particular, over land, because the NE forecast perturbations may be influenced by topographic inhomogeneity within the reduced-grid boxes. For this reason, we are planning to employ a 1000-member ensemble forecast ( Kunii 2014 ) that will enable us to analyze the forecast error at each grid point, using sampling numbers similar to those of the DuNE method detailed in the present study

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Daisuke Hotta, Tse-Chun Chen, Eugenia Kalnay, Yoichiro Ota, and Takemasa Miyoshi

) analysis (“nonlinear QC”; see the last paragraph of section 2a ), can mistakenly screen out accurate observations and allow inaccurate observations to be used in “latent dropout” situations, where the background is unreliable and the other observations in the vicinity are either inaccurate or not available. Flow-dependent techniques such as dynamic QC, which is discussed by Onogi (1998) , which allows the thresholds to vary depending on the estimated background accuracy, can alleviate this issue but

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