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Michael G. Bosilovich, Franklin R. Robertson, Lawrence Takacs, Andrea Molod, and David Mocko

1. Introduction Reanalyses aim to construct a continuous and complete picture of the weather and climate by constraining evolving model forecasts with a large but heterogeneous mix of observations having different temporal availability, accuracy, and degree of correspondence to model state variables and fluxes. The resulting reanalysis products have proven useful in characterizing the climate ( Trenberth et al. 2011 ) and weather (O. Reale and M. Cordero-Fuentes 2016, unpublished manuscript

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Ronald Gelaro, Will McCarty, Max J. Suárez, Ricardo Todling, Andrea Molod, Lawrence Takacs, Cynthia A. Randles, Anton Darmenov, Michael G. Bosilovich, Rolf Reichle, Krzysztof Wargan, Lawrence Coy, Richard Cullather, Clara Draper, Santha Akella, Virginie Buchard, Austin Conaty, Arlindo M. da Silva, Wei Gu, Gi-Kong Kim, Randal Koster, Robert Lucchesi, Dagmar Merkova, Jon Eric Nielsen, Gary Partyka, Steven Pawson, William Putman, Michele Rienecker, Siegfried D. Schubert, Meta Sienkiewicz, and Bin Zhao

with appropriate consideration of the inherent uncertainties, reanalysis products have not only become a staple of the atmospheric research community, but are used increasingly for climate monitoring as well as for business applications in, for example, energy and agriculture. Recent reanalyses from the National Oceanic and Atmospheric Administration (NOAA)/National Centers for Environmental Prediction (NCEP), the European Centre for Medium-Range Weather Forecasts (ECMWF), the National Aeronautics

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C. A. Randles, A. M. da Silva, V. Buchard, P. R. Colarco, A. Darmenov, R. Govindaraju, A. Smirnov, B. Holben, R. Ferrare, J. Hair, Y. Shinozuka, and C. J. Flynn

( Zhang et al. 2012 ; Giordano et al. 2015 ; Buchard et al. 2016 ), as a tool to investigate aerosol–climate or aerosol–weather interactions ( Bellouin et al. 2013 ; Reale et al. 2014 ), for use as a priori profiles used in satellite retrievals of other atmospheric constituents ( Kessner et al. 2013 ; Inness et al. 2013 ), and for optimal network/satellite sensor design in the context of Observing System Simulation Experiments (OSSEs; Bocquet et al. 2015 ). Because standard minimum variance data

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V. Buchard, C. A. Randles, A. M. da Silva, A. Darmenov, P. R. Colarco, R. Govindaraju, R. Ferrare, J. Hair, A. J. Beyersdorf, L. D. Ziemba, and H. Yu

constraints provided by available observations, the development of data assimilation capabilities can potentially provide a better characterization of aerosols than either a model or observational network alone. Several operational and weather and climate research centers have developed aerosol data assimilation capabilities on a global scale recently ( Tanaka et al. 2003 ; Zhang et al. 2008 ; Benedetti et al. 2009 ; Sekiyama et al. 2010 ; Pérez et al. 2011 ; Buchard et al. 2015 ; Rubin et al. 2016

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Lawrence Coy, Krzysztof Wargan, Andrea M. Molod, William R. McCarty, and Steven Pawson

analyses that are especially appropriate for QBO studies, as reanalyses lack the possibly disruptive upgrades of archived operational numerical weather prediction systems. Since the observations are not uniformly distributed in the stratosphere, improvements in the model dynamics to better represent the QBO along with improved algorithms for weighting and blending observations should lead to improved equatorial zonal mean winds and temperatures. Another consideration when using reanalyses to study the

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Allison B. Marquardt Collow and Mark A. Miller

( Rutan et al. 2015 ). c. MERRA-2 Developed by the National Aeronautics and Space Administration’s Global Modeling and Assimilation Office, the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), provides a spatially and temporally consistent view of weather and climate around the globe by assimilating observations into a numerical model. Data from MERRA-2 are available beginning in January 1980 through the present at 0.5° latitude by 0.625° longitude spatial

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