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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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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

operational aerosol optical depth data assimilation over global oceans . J. Geophys. Res. , 113 , D10208 , doi: 10.1029/2007JD009065 . 10.1029/2007JD009065 Zhang , Y. , M. Bocquet , V. Mallet , C. Seigneur , and A. Baklanov , 2012 : Real-time air quality forecasting. Part I: History, techniques, and current status . Atmos. Environ. , 60 , 632 – 655 , doi: 10.1016/j.atmosenv.2012.06.031 . 10.1016/j.atmosenv.2012.06.031

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Kevin Hodges, Alison Cobb, and Pier Luigi Vidale

loss of life and disruption in vulnerable societies ( ECLAC 2009 ). It is therefore important to utilize the available data and new analysis techniques to better understand their properties and behavior, with the aim of mitigating their societal, economic, and environmental impacts. Because of the relatively short observational record of TCs, and problems with sampling within the record, there is considerable uncertainty in the variability of TCs in terms of frequency over climate time scales of

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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

geographical and temporal coverage due to cloud contamination, uncertainties in aerosol retrievals, and sensor-specific data gaps. Although they provide continuity, aerosol models experience uncertainties due to emissions and physical parameterizations. One approach to provide a better representation of aerosols in the atmosphere is to take advantage of both models and sparse observations using data assimilation techniques. By combining the high temporal and spatial coverage of a global model with

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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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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

applications ranging from air quality forecasting to studies of aerosol–climate and aerosol–weather interactions (e.g., Bocquet et al. 2015 ). An analysis splitting technique ( Randles et al. 2017 ) is used to assimilate AOD at 550 nm, in which a two-dimensional analysis is performed first using error covariances derived from innovation data, and then the horizontal increments are projected vertically and across species using an ensemble method. AOD observations are derived from several sources, including

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Krzysztof Wargan, Gordon Labow, Stacey Frith, Steven Pawson, Nathaniel Livesey, and Gary Partyka

quality of these fields has not encouraged the atmospheric ozone community to use them in scientific research. Typically, researchers prefer to utilize satellite and in situ ozone data along with assimilated meteorological variables. To our knowledge, the only comprehensively validated reanalysis ozone fields are those from two European Centre for Medium-Range Weather Forecasts (ECMWF) reanalyses: ERA-40 ( Dethof and Hólm 2004 ) and ERA-Interim ( Dragani 2011 ). On the other hand, a large body of

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Clara S. Draper, Rolf H. Reichle, and Randal D. Koster

MERRA to MERRA-Land). An additional reanalysis, ERA-Interim, from the European Centre for Medium-Range Weather Forecasts ( Dee et al. 2011 ), is included in the evaluation of the temporal behavior of the turbulent fluxes. In contrast to the NASA reanalyses, ERA-Interim includes a land surface updating scheme ( de Rosnay et al. 2014 ). Specifically, the soil moisture, soil temperature, and snow temperatures are updated to minimize errors in the forecast screen-level relative humidity and temperature

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Rolf H. Reichle, Clara S. Draper, Q. Liu, Manuela Girotto, Sarith P. P. Mahanama, Randal D. Koster, and Gabrielle J. M. De Lannoy

-depth assessment of the MERRA-2 land surface energy balance is left for future study. The MERRA-2 skill is compared to that of MERRA-Land, MERRA, and, where possible, ERA-Interim/Land, a land-only reanalysis dataset produced recently by the European Centre for Medium-Range Weather Forecasts (ECMWF). The paper is organized as follows. Section 2 provides a brief description of the MERRA-2 system and the data used in this study. Next, the MERRA-2 estimates of terrestrial water storage ( section 3a ), soil

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Rolf H. Reichle, Q. Liu, Randal D. Koster, Clara S. Draper, Sarith P. P. Mahanama, and Gary S. Partyka

-generated precipitation, however, is corrected with precipitation observations before reaching the land surface. Observation-corrected precipitation was also used in the Climate Forecast System Reanalysis (CFSR; Saha et al. 2010 ; Meng et al. 2012 ) and in MERRA-Land ( Reichle et al. 2011 ; Reichle 2012 ). The latter is an offline, land-only reanalysis product. It provides significantly better land surface moisture storage dynamics than the original MERRA product because the MERRA-Land precipitation forcing was

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