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Michele M. Rienecker, Max J. Suarez, Ronald Gelaro, Ricardo Todling, Julio Bacmeister, Emily Liu, Michael G. Bosilovich, Siegfried D. Schubert, Lawrence Takacs, Gi-Kong Kim, Stephen Bloom, Junye Chen, Douglas Collins, Austin Conaty, Arlindo da Silva, Wei Gu, Joanna Joiner, Randal D. Koster, Robert Lucchesi, Andrea Molod, Tommy Owens, Steven Pawson, Philip Pegion, Christopher R. Redder, Rolf Reichle, Franklin R. Robertson, Albert G. Ruddick, Meta Sienkiewicz, and Jack Woollen

1. Introduction Reanalyses combine model fields with observations distributed irregularly in space and time into a spatially complete gridded meteorological dataset, with an unchanging model and analysis system spanning the historical data record. The earlier generations of reanalyses from the National Oceanic and Atmospheric Administration/National Centers for Environmental Prediction (NOAA/NCEP), the European Centre for Medium-Range Weather Forecasts (ECMWF), and the Japan Meteorological

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Behnjamin J. Zib, Xiquan Dong, Baike Xi, and Aaron Kennedy

are used for a variety of applications, including as a source for the development and verification of climate models, forcing data for numerous user models, examining forecast skill, estimation of renewable energy resources, investigation of extreme weather and climatic events, and health risk assessments. These datasets may also be an essential tool for performing studies in data-sparse regions such as the Arctic. Given its unique environmental characteristics and extreme surface conditions

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Kyle F. Itterly and Patrick C. Taylor

1. Introduction A comprehensive representation of the Earth system is required to understand the physical processes that drive climate variability and change. Retrospective analysis—referred to as reanalysis—is one technique for generating such a dataset. Reanalysis data products are generated using data assimilation techniques to constrain a forecast model solution against in situ, satellite, and other observations (e.g., Bosilovich et al. 2011 ). Reanalysis provides continuous and consistent

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David H. Bromwich, Julien P. Nicolas, and Andrew J. Monaghan

a numerical weather prediction model anchored with a variety of meteorological observations. Importantly, these observations do not include precipitation rates. Three new global reanalyses have been produced since 2008: the European Centre for Medium-Range Weather Forecasts “Interim” reanalysis (ERA-Interim) ( Simmons et al. 2006 ; Uppala et al. 2008 ); the National Aeronautics and Space Administration Modern Era Retrospective-Analysis for Research and Applications (MERRA) ( Bosilovich et al

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Aaron D. Kennedy, Xiquan Dong, Baike Xi, Shaocheng Xie, Yunyan Zhang, and Junye Chen

has been done to derive forcing using constrained variational analysis from observations during intensive observation periods (IOPs) at the Department of Energy (DOE) Atmospheric Radiation Measurement Program (ARM) sites ( Zhang and Lin 1997 ; Zhang et al. 2001 ). More recently, Xie et al. (2003) evaluated the forcing datasets derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) during three IOPs at the ARM Southern Great Plains (SGP) site. They found that although the

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Brian E. Mapes and Julio T. Bacmeister

1. Introduction There are many ways to learn from the confrontation of an atmosphere model with observations, in service of model improvement. The study of initial tendencies [or errors in one-time-step forecasts, Klinker and Sardeshmukh (1992) ] is appealing because the effect of a model process error is localized. However, initialization shock may dominate the results, making interpretation subtle (e.g., Judd et al. 2008 ). At the other extreme of time scale, the biases of unconstrained

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Man-Li C. Wu, Oreste Reale, Siegfried D. Schubert, Max J. Suarez, and Chris D. Thorncroft

centers such as the European Centre for Medium-Range Weather Forecasts (ECMWF), the National Centers for Environmental Prediction (NCEP), and the Japan Meteorological Agency (JMA). In addition, a set of experiments was performed to investigate the forcings contributing to the AEJ structure and position. WA09 results confirmed in part previous findings by Thorncroft and Blackburn (1999) , on the importance of low-level heating in controlling the AEJ, and by Cook (1999) , on the prominent role of

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Franklin R. Robertson, Michael G. Bosilovich, Junye Chen, and Timothy L. Miller

more recent products. Dee and Todling (2000) used rawinsonde humidity data to correct slowly evolving background guess systematic error in the forecast model component of the Goddard Earth Observing System (GEOS) data assimilation system. Andersson et al. (2005) were able to diagnose a posteriori the incompatibility of European Centre for Medium-Range Weather Forecasts (ECMWF) model background dry humidities in cloud-free areas with satellite moisture retrievals that ultimately resulted in

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Mark Decker, Michael A. Brunke, Zhuo Wang, Koichi Sakaguchi, Xubin Zeng, and Michael G. Bosilovich

–atmosphere–sea ice) model to derive the best estimate of the state of the atmosphere and land surface. The National Centers for Environmental Prediction (NCEP), the European Centre for Medium-Range Weather Forecasts (ECMWF), and the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC)’s Global Modeling and Assimilation Office (GMAO) are three major centers that have recently produced “second generation” reanalysis datasets. While they are the best approximation of the state of

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Benjamin A. Schenkel and Robert E. Hart

pp . Cocks , S. , and W. Gray , 2002 : Variability of the outer wind profiles of western North Pacific typhoons: Classifications and techniques for analysis and forecasting . Mon. Wea. Rev. , 130 , 1989 – 2005 . Cordeira , J. , 2010 : Tropical–extratropical interactions conducive to intraseasonal variability in Northern Hemisphere available potential energy . Preprints, 29th Conf. on Hurricanes and Tropical Meteorology, Tuscon, AZ, Amer. Meteor. Soc, 8D.7. [Available online at

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