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Bin Guan, Duane E. Waliser, and F. Martin Ralph

-observed ARs and ARs in reanalysis products independently identified and measured based on different methodologies. For the dropsonde observations, the AR identification procedure is specific to the manner in which these observations were taken, that is, along transects across the ARs that attempt to sample the center part of ARs ( Ralph et al. 2017b ). For reanalyses, AR identification is based on applying a global AR detection algorithm that considers the 2D geometry of ARs ( Guan and Waliser 2015

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

products. Data product file “Collection” and ECS short names are for 1-hourly products. Monthly average products are also available. b. Precipitation observations and selection criteria Precipitation data products suitable for use in the MERRA-2 precipitation correction algorithm must 1) provide global or near-global coverage, 2) be available from 1979 to the present, 3) be updated operationally with a latency less than about one week, and 4) include gauge measurements where available. The first three

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

have been processed with the version 8.6 retrieval (V8.6) algorithm ( Bhartia et al. 2013 ), an update to the version 8 data assimilated in MERRA. In the V8.6 algorithm, the ozone cross sections are taken from ( Daumont et al. 1992 ), which are superior in resolution, temperature dependence, and quality to the Bass and Paur (1985) cross sections used in prior retrievals. A more accurate cloud height climatology has been developed using the UV rotational Raman filling technique ( Vasilkov et al

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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

; Benedetti et al. 2009 ; Schutgens et al. 2010 ). Similarly, many aerosol observations such as those from remote sensing platforms, both satellite- and ground-based, suffer from limited coverage (e.g., due to their orbit and/or cloud contamination), contextual biases such as “clear-sky” bias, and biases due to assumptions made in retrieval algorithms ( Zhang and Reid 2009 ; Shi et al. 2011 ; Colarco et al. 2014 ). Reanalyses attempt to take advantage of the best features of both models and

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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

background forecast (i.e., aerosol speciation, size, and vertical structure, plus the assumed optical properties that are used to convert aerosol mass to AOD). Furthermore, like the AOD, these diagnostics also depend on the parameterization of error covariances in the aerosol data assimilation algorithm. This paper is the second of a pair that summarize our effort to evaluate MERRA-2 aerosol fields. Part I focused on describing the aerosol data assimilation system as well as the performance of MERRA-2

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

withheld from the algorithm, the average root-mean-square error (RMSE) with the withheld data was 15 W m −2 , for both LH and SH, and the average anomaly correlation was 0.57 for LH and 0.60 for SH ( Jung et al. 2011 ). In general, the MTE method is better suited to estimating spatial variability and the seasonal cycle than it is to capturing interannual anomaly patterns ( Jung et al. 2009 ). 3) CRU temperature data CRU time series version 4.00 (TS v4.00) provides gridded monthly means of the daily

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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

and Antarctica ( Cullather et al. 2014 ). However, MERRA-2 does not include a land surface analysis (besides the precipitation corrections discussed next). The MERRA-2 precipitation corrections algorithm is a refined version of that used in MERRA-Land as discussed in detail by Reichle et al. (2017) ; in particular, see their section 2 and their Figs. 1 and 2. MERRA-Land used the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) unified gauge-based analysis of

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Laura M. Hinkelman

. Comparisons between EBAF and the original version of MERRA are included in order to help identify where algorithm changes have improved or degraded the product. Simultaneous evaluation of MERRA and MERRA-2 may also aid researchers in deciding which product is best for their purposes. 2. Data and method a. MERRA NASA’s MERRA ( Reinecker et al. 2008 , 2011 ; no longer in production) was created to provide context for NASA satellite data, with a specific goal of better representing the hydrologic cycle than

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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

assimilated within a global data assimilation system. Other new developments in MERRA-2 relevant to IESA focus on aspects of the cryosphere and stratosphere, including the representation of ozone, and on the use of precipitation observations to force the land surface. At the same time, basic aspects of the MERRA-2 system, such as the variational analysis algorithm and observation handling, are largely unchanged since MERRA. Also unchanged is the preparation of most conventional data sources used

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Kevin Hodges, Alison Cobb, and Pier Luigi Vidale

scaled by −1). These are initially linked together using a nearest-neighbor approach and then refined by minimizing a cost function for track smoothness, subject to adaptive constraints on displacement distance and track smoothness ( Hodges 1999 ). The use of the vertically averaged vorticity is different from some previous studies using this tracking algorithm, where the single level of 850-hPa vorticity reduced to T42 resolution was used ( Strachan et al. 2013 ; Roberts et al. 2015 ; Bell et al

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