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            "title": "MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications",
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                    "firstName": "Michele M.",
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                    "firstName": "Max J.",
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                    "firstName": "Michael G.",
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                    "creatorType": "author",
                    "firstName": "Siegfried D.",
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                    "firstName": "Franklin R.",
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                    "firstName": "Albert G.",
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            "publicationTitle": "Journal of Climate",
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            "place": "",
            "date": "07/2011",
            "volume": "24",
            "issue": "14",
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            "shortTitle": "MERRA",
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            "title": "Evaluation of multireanalysis products with in situ observations over the Tibetan Plateau",
            "creators": [
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                    "creatorType": "author",
                    "firstName": "Aihui",
                    "lastName": "Wang"
                },
                {
                    "creatorType": "author",
                    "firstName": "Xubin",
                    "lastName": "Zeng"
                }
            ],
            "abstractNote": "As the highest plateau in the world, the Tibetan Plateau (TP) strongly affects regional weather and climate as well as global atmospheric circulations. Here six reanalysis products (i.e., MERRA, NCEP/NCAR-1, CFSR, ERA-40, ERA-Interim, and GLDAS) are evaluated using in situ measurements at 63 weather stations over the TP from the Chinese Meteorological Administration (CMA) for 1992–2001 and at nine stations from field campaigns (CAMP/Tibet) for 2002–2004. The measurement variables include daily and monthly precipitation and air temperature at all CMA and CAMP/Tibet stations as well as radiation (downward and upward shortwave and longwave), wind speed, humidity, and surface pressure at CAMP stations. Four statistical quantities (correlation coefficient, ratio of standard deviations, standard deviation of differences, and bias) are computed, and a ranking approach is also utilized to quantify the relative performance of reanalyses with respect to each variable and each statistical quantity. Compared with measurements at the 63 CMA stations, ERA-Interim has the best overall performance in both daily and monthly air temperatures, while MERRA has a high correlation with observations. GLDAS has the best overall performance in both daily and monthly precipitation because it is primarily based on the merged precipitation product from surface measurements and satellite remote sensing, while ERA-40 and MERRA have the highest correlation coefficients for daily and monthly precipitation, respectively. Compared with measurements at the nine CAMP stations, CFSR shows the best overall performance, followed by GLDAS, although the best ranking scores are different for different variables. It is also found that NCEP/NCAR-1 reanalysis shows the worst overall performance compared with both CMA and CAMP data. Since no reanalysis product is superior to others in all variables at both daily and monthly time scales, various reanalysis products should be combined for the study of weather and climate over the TP.",
            "publicationTitle": "Journal of Geophysical Research: Atmospheres",
            "publisher": "",
            "place": "",
            "date": "2012",
            "volume": "117",
            "issue": "D5",
            "section": "",
            "partNumber": "",
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            "pages": "n/a–n/a",
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            "seriesTitle": "",
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            "DOI": "10.1029/2011JD016553",
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            "language": "en",
            "libraryCatalog": "Wiley Online Library",
            "callNumber": "",
            "rights": "Copyright 2012 by the American Geophysical Union",
            "extra": "",
            "tags": [
                {
                    "tag": "Reanalysis",
                    "type": 1
                },
                {
                    "tag": "Tibetan Plateau",
                    "type": 1
                },
                {
                    "tag": "comparison",
                    "type": 1
                },
                {
                    "tag": "observation",
                    "type": 1
                }
            ],
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    {
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            "creatorSummary": "Decker et al.",
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            "title": "Evaluation of the Reanalysis Products from GSFC, NCEP, and ECMWF Using Flux Tower Observations",
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                    "creatorType": "author",
                    "firstName": "Mark",
                    "lastName": "Decker"
                },
                {
                    "creatorType": "author",
                    "firstName": "Michael A.",
                    "lastName": "Brunke"
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                    "creatorType": "author",
                    "firstName": "Zhuo",
                    "lastName": "Wang"
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                {
                    "creatorType": "author",
                    "firstName": "Koichi",
                    "lastName": "Sakaguchi"
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                {
                    "creatorType": "author",
                    "firstName": "Xubin",
                    "lastName": "Zeng"
                },
                {
                    "creatorType": "author",
                    "firstName": "Michael G.",
                    "lastName": "Bosilovich"
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            ],
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            "publicationTitle": "Journal of Climate",
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            "date": "03/2012",
            "volume": "25",
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            "pages": "1916-1944",
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                    "creatorType": "author",
                    "firstName": "Behnjamin J.",
                    "lastName": "Zib"
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                    "creatorType": "author",
                    "firstName": "Xiquan",
                    "lastName": "Dong"
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                    "firstName": "Baike",
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                    "firstName": "Aaron",
                    "lastName": "Kennedy"
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            "publicationTitle": "Journal of Climate",
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            "date": "04/2012",
            "volume": "25",
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            "pages": "2291-2305",
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    {
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        "version": 25,
        "library": {
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            "name": "NARR v CFSR v MERRA",
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            "creatorSummary": "Jakobson et al.",
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        "data": {
            "key": "JWZ22TGT",
            "version": 25,
            "itemType": "journalArticle",
            "title": "Validation of atmospheric reanalyses over the central Arctic Ocean",
            "creators": [
                {
                    "creatorType": "author",
                    "firstName": "Erko",
                    "lastName": "Jakobson"
                },
                {
                    "creatorType": "author",
                    "firstName": "Timo",
                    "lastName": "Vihma"
                },
                {
                    "creatorType": "author",
                    "firstName": "Timo",
                    "lastName": "Palo"
                },
                {
                    "creatorType": "author",
                    "firstName": "Liisi",
                    "lastName": "Jakobson"
                },
                {
                    "creatorType": "author",
                    "firstName": "Hannes",
                    "lastName": "Keernik"
                },
                {
                    "creatorType": "author",
                    "firstName": "Jaak",
                    "lastName": "Jaagus"
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            ],
            "abstractNote": "Atmospheric reanalyses were validated against tethersonde sounding data on air temperature, air humidity and wind speed, collected during the drifting ice station Tara in the central Arctic in April–August 2007. The data were not assimilated into the reanalyses, providing a rare possibility for their independent validation, which was here made for the lowermost 890 m layer. The following reanalyses were included in the study: the European ERA-Interim, the Japanese JCDAS, and the U.S. NCEP-CFSR, NCEP-DOE, and NASA-MERRA. All reanalyses included large errors. ERA-Interim was ranked first; it outperformed the other reanalyses in the bias and root-mean-square-error (RMSE) for air temperature as well as in the bias, RMSE, and correlation coefficient for the wind speed. ERA-Interim suffered, however, from a warm bias of up to 2°C in the lowermost 400 m layer and a moist bias of 0.3 to 0.5 g kg−1throughout the 890 m layer. The NCEP-CFSR, NCEP-DOE, and NASA-MERRA reanalyses outperformed the other reanalyses with respect to 2-m air temperature and specific humidity and 10-m wind speed, which makes them, especially NCEP-CSFR, better in providing turbulent flux forcing for sea ice models. Considering the whole vertical profile, however, the older NCEP-DOE got the second highest overall ranking, being better than the new NCEP-CFSR. Considering the whole group of reanalyses, the largest air temperature errors surprisingly occurred during higher-than-average wind speeds. The observed biases in temperature, humidity, and wind speed were in many cases comparable or even larger than the climatological trends during the latest decades.",
            "publicationTitle": "Geophysical Research Letters",
            "publisher": "",
            "place": "",
            "date": "2012",
            "volume": "39",
            "issue": "10",
            "section": "",
            "partNumber": "",
            "partTitle": "",
            "pages": "n/a–n/a",
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            "DOI": "10.1029/2012GL051591",
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            "url": "http://onlinelibrary.wiley.com/doi/10.1029/2012GL051591/abstract",
            "accessDate": "2014-02-21T18:20:05Z",
            "PMID": "",
            "PMCID": "",
            "ISSN": "1944-8007",
            "archive": "",
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            "shortTitle": "",
            "language": "en",
            "libraryCatalog": "Wiley Online Library",
            "callNumber": "",
            "rights": "Copyright 2012 by the American Geophysical Union",
            "extra": "",
            "tags": [
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                    "tag": "Artic",
                    "type": 1
                },
                {
                    "tag": "Reanalysis",
                    "type": 1
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                    "tag": "boundary layer",
                    "type": 1
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                    "tag": "polar",
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            "dateAdded": "2014-02-21T18:20:05Z",
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    },
    {
        "key": "VWQF45HD",
        "version": 23,
        "library": {
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            "name": "NARR v CFSR v MERRA",
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            "creatorSummary": "Lorenz and Kunstmann",
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                    "firstName": "Christof",
                    "lastName": "Lorenz"
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                    "firstName": "Harald",
                    "lastName": "Kunstmann"
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            "publicationTitle": "Journal of Hydrometeorology",
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            "date": "10/2012",
            "volume": "13",
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            "partNumber": "",
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            "pages": "1397-1420",
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            "shortTitle": "The Hydrological Cycle in Three State-of-the-Art Reanalyses",
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            "title": "How well do reanalyses represent the southern African precipitation?",
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            "place": "",
            "date": "FEB 2013",
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            "pages": "951-962",
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            "journalAbbreviation": "Clim. Dyn.",
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                    "tag": "general-circulation model",
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                    "tag": "indian-ocean",
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                    "firstName": "Paul",
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                    "creatorType": "author",
                    "firstName": "Joshua",
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            "abstractNote": "One of the greatest challenges of wind resources an\nalysts is the finding of reliable data sources\nto be applied in the long-term correction of measur\ned wind statistics. A long-term correction has\nto be performed in order to decrease the influences\nof year-to-year wind variations on the short-\nterm wind statistics.\nThe objective of this study is to evaluate the use\nof different reanalysis data sets as long-term\nreferences for energy production estimation. Wind d\nata from three different datasets are\nanalyzed in six different onshore sites located in\nBrazil, North-Africa, South-Africa, Poland and\nScandinavia.\nThe three data sets are two reanalysis datasets and\ncommercial Mesoscale Data.\nThe two reanalysis datasets are the NCEP/NCAR (Nati\nonal Center for Environmental\nPrediction/National Center for Atmospheric Research\n) and the MERRA (Modern-Era\nRetrospective analysis for Research and Application\ns) reanalysis datasets. NCEP/NCAR has\nbeen commonly used in wind resource analysis during\nthe last decade, while MERRA were\nrecently released.\nThe MERRA reanalysis data show a higher spatial and\ntemporal resolution (hourly) in\ncomparison with the NCEP/NCAR (6 hourly) which allo\nws a better representation of the local\nwind climate. The use of MERRA reanalysis wind data\ncould be a relevant improvement in\naccuracy for energy production estimation.\nThe commercial mesoscale data use in this study are\n: Vortex, Windtrend and 3TIER.\nIn general the MERRA reanalysis data lead to an imp\nrovement in the correlation coefficient.\nHowever, the choice of the most appropriate dataset\nshould be carefully analyzed for each\nspecific case.\nFurthermore, wind measurements and available ground\n-based meteorological data have also\nbeen used in this investigation.\nA comparison of all considered reanalysis data, com\nmercial mesoscale data and meteorological\nlong-term stations is presented.",
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