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Daryl T. Kleist and Kayo Ide

OSSE allows one to directly evaluate the actual analysis error since the truth is known. This is in contrast to the aforementioned studies that utilized real observations and focused on forecast impact, where verification is complicated by the fact that the components from which the verification is being performed have errors (analyses, observations, etc.). Additionally, the use of an OSSE makes it possible to evaluate verification for variables such as moisture, which are typically difficult to

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

atmosphere–land and ocean–sea ice components are coupled at hourly intervals using the OASIS coupler ( Valcke 2006 ) as described in Hewitt et al. (2011) . The initial conditions of the coupled model are corrected using two separate 6-h window data assimilation systems: a 4DVAR system for the atmosphere with associated soil moisture content nudging and snow analysis schemes, and a 3DVAR–first-guess-at-appropriate-time (FGAT) system for the ocean and sea ice. The background information for all of the DA

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Hailing Zhang and Zhaoxia Pu

temperature at grid points and an average temperature within a 300-km radius from the simulated Katrina’s center at 0600 UTC 26 August (12-h forecasts after the end of the data assimilation cycle at 1800 UTC 25 August). It is apparent that changes of temperature and moisture occur in both the vortex core region and the outer bands in all experiments (SFC, ADP, and ADP_SFC) throughout the troposphere due to the cycled data assimilation. With assimilation of surface data only, SFC ( Fig. 14a ) leads to a

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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

two analysis times results in an incomplete spatial sampling of the bias for each sensor since the orbits at 0000 and 1200 UTC generally cover similar areas. Consequently, this change is currently being reevaluated. Table 2. Bias model predictors for satellite radiance observations. e. Additional AIRS/IASI channels assimilated In the existing 4DVar-based system, only 87 Atmospheric Infrared Sounder (AIRS; from Aqua ) and 62 IASI (from MetOp-A ) channels were assimilated. No IASI moisture

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

) and no update of these variables is made in DF at time T (i.e., DF starts from 6-h forecast surface variables). The LAM component uses a continuous data assimilation cycling strategy where surface temperatures and soil moisture contents are updated every 24 h at 0000 UTC. LAM forecasts (LB and LF) initialized at 0600, 1200, and 1800 UTC simply rely on the forecast surface variables from the most recent 0000 UTC surface analysis. b. Data assimilation approaches 1) Limited-area 4DVar and global 3

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Nicholas A. Gasperoni and Xuguang Wang

-layer model used here, while able to well represent realistic midlatitude baroclinic instability, is not a realistic representation for the tropics due in part to the simplifying exclusion of moisture. Hendon and Hartmann (1985) analyzed the variability of a similar dry two-layer model and noted that the tropics are dominated by internal normal modes consistent with Matsuno (1966) . Additionally, these waves are equatorially trapped, with minimal activity propagating from the tropics to the

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

linear transform can be used in to give a different but equally valid localization method. In the experiments described here we followed Clayton et al. (2013) and used the transform from our 3DVar and 4DVar system ( Lorenc et al. 2000 ). Since localization can affect balance ( Mitchell et al. 2002 ) the choice of which variables to localize can be important; our localization in Eq. (16) is applied to streamfunction, divergence, unbalanced pressure, and unbalanced moisture ( Ingleby et al

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