Assessment of MERRA-2 Land Surface Hydrology Estimates

Rolf H. Reichle Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland

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Clara S. Draper Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland
GESTAR, Universities Space Research Association, Columbia, Maryland

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Q. Liu Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland
Science Systems and Applications, Lanham, Maryland

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Manuela Girotto Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland
GESTAR, Universities Space Research Association, Columbia, Maryland

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Sarith P. P. Mahanama Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland
Science Systems and Applications, Lanham, Maryland

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Randal D. Koster Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland

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Gabrielle J. M. De Lannoy Department of Earth and Environmental Sciences, KU Leuven, Heverlee, Belgium

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Abstract

The MERRA-2 atmospheric reanalysis product provides global, 1-hourly estimates of land surface conditions for 1980–present at ~50-km resolution. MERRA-2 uses observations-based precipitation to force the land (unlike its predecessor, MERRA). This paper evaluates MERRA-2 and MERRA land hydrology estimates, along with those of the land-only MERRA-Land and ERA-Interim/Land products, which also use observations-based precipitation. Overall, MERRA-2 land hydrology estimates are better than those of MERRA-Land and MERRA. A comparison against GRACE satellite observations of terrestrial water storage demonstrates clear improvements in MERRA-2 over MERRA in South America and Africa but also reflects known errors in the observations used to correct the MERRA-2 precipitation. Validation against in situ measurements from 220–320 stations in North America, Europe, and Australia shows that MERRA-2 and MERRA-Land have the highest surface and root zone soil moisture skill, slightly higher than that of ERA-Interim/Land and higher than that of MERRA (significantly for surface soil moisture). Snow amounts from MERRA-2 have lower bias and correlate better against reference data from the Canadian Meteorological Centre than do those of MERRA-Land and MERRA, with MERRA-2 skill roughly matching that of ERA-Interim/Land. Validation with MODIS satellite observations shows that MERRA-2 has a lower snow cover probability of detection and probability of false detection than MERRA, owing partly to MERRA-2’s lower midwinter, midlatitude snow amounts and partly to MERRA-2’s revised snow depletion curve parameter compared to MERRA. Finally, seasonal anomaly R values against naturalized streamflow measurements in the United States are, on balance, highest for MERRA-2 and ERA-Interim/Land, somewhat lower for MERRA-Land, and lower still for MERRA (significantly in four basins).

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Rolf H. Reichle, rolf.reichle@nasa.gov

Abstract

The MERRA-2 atmospheric reanalysis product provides global, 1-hourly estimates of land surface conditions for 1980–present at ~50-km resolution. MERRA-2 uses observations-based precipitation to force the land (unlike its predecessor, MERRA). This paper evaluates MERRA-2 and MERRA land hydrology estimates, along with those of the land-only MERRA-Land and ERA-Interim/Land products, which also use observations-based precipitation. Overall, MERRA-2 land hydrology estimates are better than those of MERRA-Land and MERRA. A comparison against GRACE satellite observations of terrestrial water storage demonstrates clear improvements in MERRA-2 over MERRA in South America and Africa but also reflects known errors in the observations used to correct the MERRA-2 precipitation. Validation against in situ measurements from 220–320 stations in North America, Europe, and Australia shows that MERRA-2 and MERRA-Land have the highest surface and root zone soil moisture skill, slightly higher than that of ERA-Interim/Land and higher than that of MERRA (significantly for surface soil moisture). Snow amounts from MERRA-2 have lower bias and correlate better against reference data from the Canadian Meteorological Centre than do those of MERRA-Land and MERRA, with MERRA-2 skill roughly matching that of ERA-Interim/Land. Validation with MODIS satellite observations shows that MERRA-2 has a lower snow cover probability of detection and probability of false detection than MERRA, owing partly to MERRA-2’s lower midwinter, midlatitude snow amounts and partly to MERRA-2’s revised snow depletion curve parameter compared to MERRA. Finally, seasonal anomaly R values against naturalized streamflow measurements in the United States are, on balance, highest for MERRA-2 and ERA-Interim/Land, somewhat lower for MERRA-Land, and lower still for MERRA (significantly in four basins).

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Rolf H. Reichle, rolf.reichle@nasa.gov
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  • Albergel, C., and Coauthors, 2008: From near-surface to root-zone soil moisture using an exponential filter: An assessment of the method based on in situ observations and model simulations. Hydrol. Earth Syst. Sci., 12, 13231337, doi:10.5194/hess-12-1323-2008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Balsamo, G., and Coauthors, 2015: ERA-Interim/Land: A global land surface reanalysis data set. Hydrol. Earth Syst. Sci., 19, 389407, doi:10.5194/hess-19-389-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Beck, H. E., and Coauthors, 2017: MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data. Hydrol. Earth Syst. Sci., 21, 589615, doi:10.5194/hess-2016-236.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bell, J., and Coauthors, 2013: U.S. Climate Reference Network soil moisture and temperature observations. J. Hydrometeor., 14, 977988, doi:10.1175/JHM-D-12-0146.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bosilovich, M. G., and Coauthors, 2015: MERRA-2: Initial evaluation of the climate. NASA Tech. Rep. NASA/TM-2015-104606, Vol. 43, 139 pp. [Available online at https://gmao.gsfc.nasa.gov/pubs/docs/Bosilovich803.pdf.]

  • Bosilovich, M. G., R. Lucchesi, and M. Suarez, 2016: MERRA-2: File specification. NASA GMAO Office Note 9, 75 pp. [Available online at https://gmao.gsfc.nasa.gov/pubs/docs/Bosilovich785.pdf.]

  • Boussetta, S., and Coauthors, 2013: Natural land carbon dioxide exchanges in the ECMWF integrated forecasting system: Implementation and offline validation. J. Geophys. Res. Atmos., 118, 59235946, doi:10.1002/jgrd.50488.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brasnett, B., 1999: A global analysis of snow depth for numerical weather prediction. J. Appl. Meteor., 38, 726740, doi:10.1175/1520-0450(1999)038<0726:AGAOSD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brown, R. D., and B. Brasnett, 2010: Canadian Meteorological Centre (CMC) daily snow depth analysis data, version 1. National Snow and Ice Data Center, accessed 1 July 2016, doi:10.5067/W9FOYWH0EQZ3.

    • Crossref
    • Export Citation
  • Chen, M., W. Shi, P. Xie, V. B. S. Silva, V. E. Kousky, R. Wayne Higgins, and J. E. Janowiak, 2008: Assessing objective techniques for gauge-based analyses of global daily precipitation. J. Geophys. Res., 113, D04110, doi:10.1029/2007JD009132.

    • Search Google Scholar
    • Export Citation
  • Cosby, B., G. Hornberger, R. Clapp, and T. Ginn, 1984: A statistical exploration of the relationships of soil moisture characteristics to the physical properties of soil. Water Resour. Res., 20, 682690, doi:10.1029/WR020i006p00682.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cullather, R. I., S. M. J. Nowicki, B. Zhao, and M. J. Suarez, 2014: Evaluation of the surface representation of the Greenland ice sheet in a general circulation model. J. Climate, 27, 48354856, doi:10.1175/JCLI-D-13-00635.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • De Lannoy, G. J. M., and R. H. Reichle, 2016: Global assimilation of multiangle and multipolarization SMOS brightness temperature observations into the GEOS-5 Catchment land surface model for soil moisture estimation. J. Hydrometeor., 17, 669691, doi:10.1175/JHM-D-15-0037.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • De Lannoy, G. J. M., R. D. Koster, R. H. Reichle, S. P. P. Mahanama, and Q. Liu, 2014: An updated treatment of soil texture and associated hydraulic properties in a global land modeling system. J. Adv. Model. Earth Syst., 6, 957979, doi:10.1002/2014MS000330.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dee, D., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, doi:10.1002/qj.828.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Diamond, H., and Coauthors, 2013: U.S. Climate Reference Network after one decade of operations: Status and assessment. Bull. Amer. Meteor. Soc., 94, 485498, doi:10.1175/BAMS-D-12-00170.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dirmeyer, P., and T. Oki, 2002: The Second Global Soil Wetness Project (GSWP-2) science and implementation plan. IGPO Publ. 37, 64 pp.

  • Dorigo, W. A., and Coauthors, 2011: The International Soil Moisture Network: A data hosting facility for global in situ soil moisture measurements. Hydrol. Earth Syst. Sci., 15, 16751698, doi:10.5194/hess-15-1675-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Entekhabi, D., R. H. Reichle, R. D. Koster, and W. T. Crow, 2010: Performance metrics for soil moisture retrievals and application requirements. J. Hydrometeor., 11, 832840, doi:10.1175/2010JHM1223.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gash, J. H. C., 1979: An analytical model of rainfall interception by forests. Quart. J. Roy. Meteor. Soc., 105, 4355, doi:10.1002/qj.49710544304.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • GMAO, 2015a: MERRA-2 tavgM_2d_lnd_Nx: 2d, Monthly mean, time-averaged, single-level, assimilation, land surface diagnostics, V5.12.4. Goddard Earth Sciences Data and Information Services Center (GES DISC), accessed 1 September 2016, doi:10.5067/8S35XF81C28F.

    • Crossref
    • Export Citation
  • GMAO, 2015b: MERRA-2 tavg1_2d_lnd_Nx: 2d, 1-hourly, time-averaged, single-level, assimilation, land surface diagnostics, V5.12.4. Goddard Earth Sciences Data and Information Services Center (GES DISC), accessed 1 September 2016, doi:10.5067/RKPHT8KC1Y1T.

    • Crossref
    • Export Citation
  • GMAO, 2015c: MERRA-2 const_2d_asm_Nx: 2d, constants, V5.12.4. Goddard Earth Sciences Data and Information Services Center (GES DISC), accessed 1 September 2016, doi:10.5067/ME5QX6Q5IGGU.

    • Crossref
    • Export Citation
  • Hall, D. K., V. V. Salomonson, and G. A. Riggs, 2006: MODIS/Terra snow cover daily L3 global 0.05° CMG, version 5. NASA National Snow and Ice Data Center Distributed Active Archive Center, accessed 1 June 2016, doi:10.5067/EI5HGLM2NNHN.

    • Crossref
    • Export Citation
  • Helfand, H. M., and S. D. Schubert, 1995: Climatology of the simulated Great Plains low-level jet and its contribution to the continental moisture budget of the United States. J. Climate, 8, 784806, doi:10.1175/1520-0442(1995)008<0784:COTSGP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., R. F. Adler, D. T. Bolvin, and G. Gu, 2009: Improving the global precipitation record: GPCP version 2.1. Geophys. Res. Lett., 36, L17808, doi:10.1029/2009GL040000.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koster, R. D., and G. K. Walker, 2015: Interactive vegetation phenology, soil moisture, and monthly temperature forecasts. J. Hydrometeor., 16, 14561465, doi:10.1175/JHM-D-14-0205.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koster, R. D., M. J. Suarez, A. Ducharne, M. Stieglitz, and P. Kumar, 2000: A catchment-based approach to modeling land surface processes in a general circulation model: 1. Model structure. J. Geophys. Res., 105, 24 80924 822, doi:10.1029/2000JD900327.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Landerer, F. W., and S. C. Swenson, 2012: Accuracy of scaled GRACE terrestrial water storage estimates. Water Resour. Res., 48, W04531, doi:10.1029/2011WR011453.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Livneh, B., and D. P. Lettenmaier, 2012: Multi-criteria parameter estimation for the Unified Land Model. Hydrol. Earth Syst. Sci., 16, 30293048, doi:10.5194/hess-16-3029-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lorenz, C., and H. Kunstmann, 2012: The hydrological cycle in three state-of-the-art reanalyses: Intercomparison and performance analysis. J. Hydrometeor., 13, 13971420, doi:10.1175/JHM-D-11-088.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Louis, J. E., 1979: A parametric model of vertical eddy fluxes in the atmosphere. Bound.-Layer Meteor., 17, 187202, doi:10.1007/BF00117978.

  • Mahanama, S., B. Livneh, R. Koster, D. Lettenmaier, and R. Reichle, 2012: Soil moisture, snow, and seasonal streamflow forecasts in the United States. J. Hydrometeor., 13, 189203, doi:10.1175/JHM-D-11-046.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mahanama, S., and Coauthors, 2015: Land boundary conditions for the Goddard Earth Observing System Model version 5 (GEOS-5) climate modeling system: Recent updates and data file descriptions. NASA Tech. Memo. NASA/TM-2015-104606, Vol. 39, 55 pp. [Available online at https://ntrs.nasa.gov/search.jsp?R=20160002967.]

  • Miralles, D. G., J. H. Gash, T. R. H. Holmes, R. A. M. de Jeu, and A. J. Dolman, 2010: Global canopy interception from satellite observations. J. Geophys. Res., 115, D16122, doi:10.1029/2009JD013530.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Miralles, D. G., T. R. H. Holmes, R. A. M. de Jeu, J. H. Gash, A. G. C. A. Meesters, and A. J. Dolman, 2011: Global land-surface evaporation estimated from satellite-based observations. Hydrol. Earth Syst. Sci., 15, 453469, doi:10.5194/hess-15-453-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Molod, A., L. Takacs, M. Suarez, J. Bacmeister, I.-S. Song, and A. Eichmann, 2012: The GEOS-5 atmospheric general circulation model: Mean climate and development from MERRA to Fortuna. NASA Tech. Memo. NASA/TM-2012-104606, Vol. 28, 117 pp. [Available online at https://gmao.gsfc.nasa.gov/pubs/docs/tm28.pdf.]

  • Molod, A., L. Takacs, M. Suarez, and J. Bacmeister, 2015: Development of the GEOS-5 atmospheric general circulation model: Evolution from MERRA to MERRA-2. Geosci. Model Dev., 8, 13391356, doi:10.5194/gmd-8-1339-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Moody, E. G., M. D. King, C. B. Schaaf, and S. Platnick, 2008: MODIS-derived spatially complete surface albedo products: Spatial and temporal pixel distribution and zonal averages. J. Appl. Meteor. Climatol., 47, 28792894, doi:10.1175/2008JAMC1795.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mudryk, L. R., C. Derksen, P. J. Kushner, and R. Brown, 2015: Characterization of Northern Hemisphere snow water equivalent datasets, 1981–2010. J. Climate, 28, 80378051, doi:10.1175/JCLI-D-15-0229.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Randles, C. A., and Coauthors, 2016: The MERRA-2 aerosol assimilation. NASA Tech. Memo. NASA/TM-2016-104606, Vol. 45, 132 pp. [Available online at https://gmao.gsfc.nasa.gov/pubs/docs/Randles887.pdf.]

  • Reichle, R. H., 2012: The MERRA-land data product. NASA GMAO Office Note 3 (version 1.2), 38 pp. [Available online at https://gmao.gsfc.nasa.gov/pubs/docs/Reichle541.pdf.]

  • Reichle, R. H., R. D. Koster, G. J. M. De Lannoy, B. A. Forman, Q. Liu, S. P. P. Mahanama, and A. Toure, 2011: Assessment and enhancement of MERRA land surface hydrology estimates. J. Climate, 24, 63226338, doi:10.1175/JCLI-D-10-05033.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reichle, R. H., and Coauthors, 2016: Soil moisture active passive mission L4_SM data product assessment (version 2 validated release). NASA GMAO Office Note 12 (version 1.0), 55 pp.

  • Reichle, R. H., Q. Liu, R. D. Koster, C. S. Draper, S. P. P. Mahanama, and G. S. Partyka, 2017: Land surface precipitation in MERRA-2. J. Climate, 30, 16431664, doi:10.1175/JCLI-D-16-0570.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reynolds, C. A., T. J. Jackson, and W. J. Rawls, 2000: Estimating soil water-holding capacities by linking the Food and Agriculture Organization Soil map of the world with global pedon databases and continuous pedotransfer functions. Water Resour. Res., 36, 36533662, doi:10.1029/2000WR900130.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rienecker, M. M., and Coauthors, 2011: MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications. J. Climate, 24, 36243648, doi:10.1175/JCLI-D-11-00015.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Robertson, F. R., M. G. Bosilovich, J. B. Roberts, R. H. Reichle, R. Adler, L. Ricciardulli, W. Berg, and G. J. Huffman, 2014: Consistency of estimated global water cycle variations over the satellite era. J. Climate, 27, 61356154, doi:10.1175/JCLI-D-13-00384.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rodell, M., I. Velicogna, and J. Famiglietti, 2009: Satellite-based estimates of groundwater depletion in India. Nature, 460, 9991002, doi:10.1038/nature08238.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rowlands, D. D., S. B. Luthcke, S. M. Klosko, F. G. R. Lemoine, D. S. Chinn, J. J. McCarthy, C. M. Cox, and O. B. Anderson, 2005: Resolving mass flux at high spatial and temporal resolution using GRACE intersatellite measurements. Geophys. Res. Lett., 32, L04310, doi:10.1029/2004GL021908.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sakaguchi, K., and X. Zeng, 2009: Effects of soil wetness, plant litter, and under-canopy atmospheric stability on ground evaporation in the Community Land Model (CLM3.5). J. Geophys. Res., 114, D01107, doi:10.1029/2008JD010834.

    • Search Google Scholar
    • Export Citation
  • Schaefer, G. L., M. H. Cosh, and T. J. Jackson, 2007: The USDA Natural Resources Conservation Service Soil Climate Analysis Network (SCAN). J. Atmos. Oceanic Technol., 24, 20732077, doi:10.1175/2007JTECHA930.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, A., and Coauthors, 2012: The Murrumbidgee soil moisture monitoring network data set. Water Resour. Res., 48, W07701, doi:10.1029/2011WR010641.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stieglitz, M., A. Ducharne, R. Koster, and M. Suarez, 2001: The impact of detailed snow physics on the simulation of snow cover and subsurface thermodynamics at continental scales. J. Hydrometeor., 2, 228242, doi:10.1175/1525-7541(2001)002<0228:TIODSP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Swenson, S. C., 2012: GRACE monthly land water mass grids NETCDF RELEASE 5.0, version 5.0. Physical Oceanography Distributed Active Archive Center, accessed 31 May 2016, doi:10.5067/TELND-NC005.

  • Swenson, S. C., and J. Wahr, 2006: Post-processing removal of correlated errors in GRACE data. Geophys. Res. Lett., 33, L08402, doi:10.1029/2005GL025285.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Swenson, S. C., P. J.-F. Yeh, J. Wahr, and J. Famiglietti, 2006: A comparison of terrestrial water storage variations from GRACE with in situ measurements from Illinois. Geophys. Res. Lett., 33, L16401, doi:10.1029/2006GL026962.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Takacs, L. L., M. J. Suárez, and R. Todling, 2016: Maintaining atmospheric mass and water balance in reanalyses. Quart. J. Roy. Meteor. Soc., 142, 15651573, doi:10.1002/qj.2763.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • USGS, 1996: Global 30 arc-second elevation (GTOPO30): Global topographic data. EROS Data Center Distributed Active Archive Center (EDC DAAC), accessed 1 July 2015. [Available online at https://lta.cr.usgs.gov/GTOPO30.]

  • USGS, 2000: Global land cover characteristics data base, version 2.0. Accessed 27 September 2016. [Available online at https://lta.cr.usgs.gov/glcc/globdoc2_0.]

  • Wahr, J., S. Swenson, and I. Velicogna, 2006: Accuracy of GRACE mass estimates. Geophys. Res. Lett., 33, L06401, doi:10.1029/2005GL025305.

  • Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences. 2nd ed. Academic Press, 627 pp.

  • Wu, W.-S., R. J. Purser, and D. F. Parrish, 2002: Three-dimensional variational analysis with spatially inhomogeneous covariances. Mon. Wea. Rev., 130, 29052916, doi:10.1175/1520-0493(2002)130<2905:TDVAWS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xie, P., and P. A. Arkin, 1997: Global precipitation: A 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull. Amer. Meteor. Soc., 78, 25392558, doi:10.1175/1520-0477(1997)078<2539:GPAYMA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xie, P., M. Chen, S. Yang, A. Yatagai, T. Hayasaka, Y. Fukushima, and C. Liu, 2007: A gauge-based analysis of daily precipitation over East Asia. J. Hydrometeor., 8, 607626, doi:10.1175/JHM583.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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