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E. A. Irvine, S. L. Gray, J. Methven, and I. A. Renfrew

does not change the area sampled by the dropsonde data. The TARG_NOGL set is the same as the TARG set except that the two sondes on the Greenland coast are replaced by two sondes released farther away from the coast ( Fig. 2c ). The ALL_NOGL includes all dropsondes except the two on the Greenland coast ( Fig. 2d ). The ALL_NOGL and TARG_NOGL experiments test whether the two Greenland sondes were beneficial to the forecast quality. They were released close to steep orography and so the profiles may

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Ronald Gelaro, Rolf H. Langland, Simon Pellerin, and Ricardo Todling

ships and buoys; and temperature, humidity, and wind from commercial aircraft. Other more-recent observation types, such as the Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer (IASI) are not included in this baseline experiment, but will be considered in future comparisons. In keeping with the narrow focus of the comparison, it was decided that each center would use its usual assimilation algorithm, including data selection and quality control procedures

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Benoît Vié, Olivier Nuissier, and Véronique Ducrocq

deterministic ALADIN forecast, for which LBCs are provided by the ARPEGE deterministic forecast, provided the same coupling conditions for all members, as for the deterministic AROME forecast. In this ensemble experiment, 11 parallel AROME data assimilation cycles were run, as shown by Fig. 2 . The control member (P0), similar to the operational deterministic forecast, used unperturbed observations through the 3-hourly data assimilation cycle. The 10 perturbed members used randomly perturbed

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John E. Janowiak, Peter Bauer, Wanqiu Wang, Phillip A. Arkin, and Jon Gottschalck

the model reaches equilibrium) in ERA-Interim compared to ERA-40 ( Andersson et al. 2007 ). ERA-Interim also has a more extensive use of satellite radiance data employing a data quality control system that draws on experience from ERA-40 and the 25-yr Japanese Re-Analysis (JRA-25) as well as a variational bias correction of satellite radiance data ( Dee 2004 ). In particular the latter is expected to produce more consistent observational impact when, over longer time periods, new observation types

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Thomas M. Hamill, Jeffrey S. Whitaker, Michael Fiorino, and Stanley G. Benjamin

’s quality control algorithms and forward observational operators, the full observational data stream that was ingested into the operational GSI was also ingested into the EnKF, including surface observations, rawinsondes, cloud-track winds, aircraft observations, satellite-based Global Positioning System (GPS) radio occultations, and satellite radiances. Satellite radiances were assimilated from the High Resolution Infrared Radiation Sounder (HIRS), the Advanced Microwave Sounding Unit (AMSU) A and B

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Elizabeth Satterfield and Istvan Szunyogh

and times. In section 3 , we describe the design of the numerical experiments. In section 4 , we examine the ability of the ensemble to accurately capture the magnitude and spectrum of forecast uncertainties. In section 5 , we summarize our conclusions. 2. Diagnostics As in Satterfield and Szunyogh (2010) , we explore the predictive qualities of the ensemble by using linear diagnostics applied to the ensemble perturbations in a small local neighborhood of each model grid point. In what follows

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