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Gloria L. Manney, Michelle L. Santee, Zachary D. Lawrence, Krzysztof Wargan, and Michael J. Schwartz

that have been shown to provide robust results for UTLS studies. Reanalysis datasets are one of the most powerful tools available to characterize large-scale dynamical processes, but they must be used with care since the underlying forecast models and data assimilation systems they rely on have limitations (e.g., Fujiwara et al. 2017 ). Numerous studies highlight the importance of comparing results from multiple reanalyses for UTLS studies (e.g., Manney et al. 2017 ; Manney and Hegglin 2018

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Kevin Hodges, Alison Cobb, and Pier Luigi Vidale

between whether a tropical disturbance should be classified as a tropical depression and counted in best track, or is some other tropical storm such as a subtropical or hybrid cyclone, difficult and dependent on subjective forecaster interpretation. Gyakum (2011) states that “there is presently no single set of objective criteria that, if applied operationally, would irrefutably support a forecaster’s analysis of cyclone type (subtropical, hybrid or tropical)” (p. 1.6.23). It is also unclear whether

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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

analyzed aerosol fields that are radiatively coupled to the atmosphere. To our knowledge, this is the first multidecadal reanalysis within which meteorological and aerosol observations are jointly assimilated into a global assimilation system, although other operational forecasting centers are actively developing similar capabilities (e.g., Benedetti et al. 2009 ; Sekiyama et al. 2010 ; Lynch et al. 2016 ). Previously, the GMAO had performed an offline aerosol reanalysis (the MERRA Aerosol

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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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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

1. Introduction Reanalysis is the process whereby an unchanging data assimilation system is used to provide a consistent reprocessing of meteorological observations, typically spanning an extended segment of the historical data record. The process relies on an underlying forecast model to combine disparate observations in a physically consistent manner, enabling production of gridded datasets for a broad range of variables, including ones that are sparsely or not directly observed. As such, and

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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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Franklin R. Robertson, Michael G. Bosilovich, and Jason B. Roberts

; Kobayashi et al. 2015 ) that blend diverse measurements of wind, moisture, and temperature as well as other observations with first-guess estimates from model short-term forecasts. While reanalyses effectively reconcile observations with physically based dynamical models, there are a number of practical problems that result in moisture transport fields typically having substantial systematic time-dependent biases ( Trenberth et al. 2011 ; Robertson et al. 2011 ; Lorenz and Kunstmann 2012 ; Trenberth

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

. Ideally these two components related to stability should be formulated as one term. APPENDIX B Acronyms 20CR NOAA Twentieth Century Reanalysis AIRS Atmospheric Infrared Sounder AMIP Atmospheric Model Intercomparison Project (prescribed SST) AMSU Advanced Microwave Sounding Unit ATOVS Advanced TIROS Operational Vertical Sounder C-C Clausius–Clapeyron CFSR Climate Forecast System Reanalysis CPCU NOAA Climate Prediction Center (CPC) unified precipitation dataset ECMWF European Centre for Medium

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Lawrence Coy, Paul A. Newman, Steven Pawson, and Leslie R. Lait

analyses provide an opportunity for comparing their representation of the tropical zonal mean momentum budget during the QBO disruption. Here, we use a ±5° latitude average and examine the same momentum budget terms for MERRA-2 as presented in Osprey et al. (2016 , their Fig. 2b) for the European Centre for Medium-Range Weather Forecasts (ECMWF) operational analysis. Four terms of the 40-hPa zonal mean momentum budget for November 2015 through April 2016 are plotted in Fig. 4 . They consist of the

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Young-Kwon Lim, Robin M. Kovach, Steven Pawson, and Guillaume Vernieres

.1029/95JC00382 . 10.1029/95JC00382 Kim , D. , and Coauthors , 2009 : Application of MJO simulation diagnostics to climate models . J. Climate , 22 , 6413 – 6436 , doi: 10.1175/2009JCLI3063.1 . 10.1175/2009JCLI3063.1 Kim , H.-M. , P. J. Webster , V. E. Toma , and D. Kim , 2014 : Predictability and prediction skill of the MJO in two operational forecasting systems . J. Climate , 27 , 5364 – 5378 , doi: 10.1175/JCLI-D-13-00480.1 . 10.1175/JCLI-D-13-00480.1 Kug , J.-S. , F.-F. Jin

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