• Alfieri, L., , Claps P. , , D’Odorico P. , , Laio F. , , and Over T. M. , 2008: An analysis of the soil moisture feedback on convective and stratiform precipitation. J. Hydrometeor., 9, 280291, doi:10.1175/2007JHM863.1.

    • Search Google Scholar
    • Export Citation
  • Atlas, R., , Wolfson N. , , and Terry J. , 1993: The effect of SST and soil moisture anomalies on GLA model simulations of the 1988 U.S. summer drought. J. Climate, 6, 20342048, doi:10.1175/1520-0442(1993)006<2034:TEOSAS>2.0.CO;2.

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

    • Search Google Scholar
    • Export Citation
  • Betts, A., , Chen F. , , Mitchell K. , , and Janjic Z. , 1997: Assessment of the land surface and boundary layer models in two operational versions of the NCEP Eta model using FIFE data. Mon. Wea. Rev., 125, 28962916, doi:10.1175/1520-0493(1997)125<2896:AOTLSA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Burnash, R. J. C., , Ferral R. L. , , and McGuire R. A. , 1973: A generalized streamflow simulation system: Conceptual models for digital computer. Tech. Rep., U.S. National Weather Service and California Dept. of Water Resources, Sacramento, CA, 204 pp.

  • Case, J. L., , Kumar S. V. , , Srikishen J. , , and Jedlovec G. J. , 2011: Improving numerical weather predictions of summertime precipitation over the southeastern United States through a high-resolution initialization of the surface state. Wea. Forecasting, 26, 785807, doi:10.1175/2011WAF2222455.1.

    • Search Google Scholar
    • Export Citation
  • Chen, F., , Janjic Z. , , and Mitchell K. , 1997: Impact of atmospheric surface-layer parameterizations in the new land-surface scheme of the NCEP mesoscale Eta model. Bound.-Layer Meteor., 85, 391421, doi:10.1023/A:1000531001463.

    • Search Google Scholar
    • Export Citation
  • Chen, M., , Shi W. , , Xie P. , , Silva V. B. S. , , Kousky V. E. , , Higgins R. W. , , and Janowiak J. E. , 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
  • Cosgrove, B. A., and et al. , 2003: Real-time and retrospective forcing in the North American Land Data Assimilation System (NLDAS) project. J. Geophys. Res., 108, 8842, doi:10.1029/2002JD003118.

    • Search Google Scholar
    • Export Citation
  • Crow, W. T., , and Wood E. F. , 1999: Multi-scale dynamics of soil moisture variability observed during SGP’97. Geophys. Res. Lett., 26, 34853488, doi:10.1029/1999GL010880.

    • Search Google Scholar
    • Export Citation
  • Crow, W. T., and et al. , 2011: Upscaling sparse ground-based soil moisture observations for the validation of coarse-resolution satellite surface soil moisture products. Rev. Geophys., 50, RG2002, doi:10.1029/2011RG000372.

    • Search Google Scholar
    • Export Citation
  • Daly, C., , Neilson R. P. , , and Phillips D. L. , 1994: A statistical–topographic model for mapping climatological precipitation over mountainous terrain. J. Appl. Meteor., 33, 140158, doi:10.1175/1520-0450(1994)033<0140:ASTMFM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • de Goncalves, L. G. G., , Shuttleworth W. J. , , Chou S. C. , , Xue Y. , , Houser P. R. , , Toll D. L. , , Marengo J. , , and Rodell M. , 2006: Impact of different initial soil moisture fields on Eta model weather forecasts for South America. J. Geophys. Res., 111, D17102, doi:10.1029/2005JD006309.

    • Search Google Scholar
    • Export Citation
  • de Rosnay, P., , Balsamo G. , , Albergel C. , , Muñoz-Sabater J. , , and Isaksen L. , 2013: Initialisation of land surface variables for numerical weather prediction. Surv. Geophys., 35, 607–621, doi:10.1007/s10712-012-9207-x.

    • Search Google Scholar
    • Export Citation
  • Dharssi, I., , Bovis K. , , Macpherson B. , , and Jones C. , 2011: Operational assimilation of ASCAT surface soil wetness at the Met Office. Hydrol. Earth Syst. Sci., 15, 27292746, doi:10.5194/hess-15-2729-2011.

    • Search Google Scholar
    • Export Citation
  • Dorigo, W. A., and et al. , 2013: Global automated quality control of in situ soil moisture data from the International Soil Moisture Network. Vadose Zone J., 12, doi:10.2136/vzj2012.0097.

    • Search Google Scholar
    • Export Citation
  • Drusch, M., 2007: Initializing numerical weather prediction models with satellite-derived surface soil moisture: Data assimilation experiments with ECMWF’s Integrated Forecast System and the TMI soil moisture data set. J. Geophys. Res., 112, D03102, doi:10.1029/2006JD007478.

    • Search Google Scholar
    • Export Citation
  • Drusch, M., , and Viterbo P. , 2007: Assimilation of screen-level variables in ECMWF’s Integrated Forecast System: A study on the impact on the forecast quality and analyzed soil moisture. Mon. Wea. Rev., 135, 300314, doi:10.1175/MWR3309.1.

    • Search Google Scholar
    • Export Citation
  • Drusch, M., , Scipal K. , , de Rosnay P. , , Balsamo G. , , Andersson E. , , Bougeault P. , , and Viterbo P. , 2009: Towards a Kalman filter based soil moisture analysis system for the operational ECMWF Integrated Forecast System. Geophys. Res. Lett., 36, L10401, doi:10.1029/2009GL037716.

    • Search Google Scholar
    • Export Citation
  • Ek, M. B., , and Holtslag A. A. M. , 2004: Influence of soil moisture on boundary layer cloud development. J. Hydrometeor., 5, 8699, doi:10.1175/1525-7541(2004)005<0086:IOSMOB>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Ek, M. B., , Mitchell K. E. , , Lin Y. , , Rodgers E. , , Grunman P. , , Koren V. , , Gayno G. , , and Tarpley J. D. , 2003: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J. Geophys. Res., 108, 8851, doi:10.1029/2002JD003296.

    • Search Google Scholar
    • Export Citation
  • Entin, J., , Robock A. , , Vinnikov K. Y. , , Qiu S. , , Zabelin V. , , Liu S. , , Namkhai A. , , and Adyasuren T. , 1999: Evaluation of Global Soil Wetness Project soil moisture simulations. J. Meteor. Soc. Japan, 77, 183198.

    • Search Google Scholar
    • Export Citation
  • Fan, Y., , Van den Dool H. M. , , Lohmann D. , , and Mitchell K. , 2006: 1948–98 U.S. hydrological reanalysis by the Noah land data assimilation system. J. Climate, 19, 12141237, doi:10.1175/JCLI3681.1.

    • Search Google Scholar
    • Export Citation
  • Ford, T. W., , and Quiring S. M. , 2013: Influence of MODIS-derived dynamic vegetation on VIC-simulated soil moisture in Oklahoma. J. Hydrometeor., 14, 19101921, doi:10.1175/JHM-D-13-037.1.

    • Search Google Scholar
    • Export Citation
  • Ford, T. W., , and Quiring S. M. , 2014a: Comparison and application of multiple methods for temporal interpolation of daily soil moisture. Int. J. Climatol., 34, 26042621, doi:10.1002/joc.3862.

    • Search Google Scholar
    • Export Citation
  • Ford, T. W., , Harris E. K. , , and Quiring S. M. , 2014b: Estimating root zone soil moisture using near-surface observations from SMOS. Hydrol. Earth Syst. Sci., 18, 139154, doi:10.5194/hess-18-139-2014.

    • Search Google Scholar
    • Export Citation
  • Frye, J. D., , and Mote T. L. , 2010: Convection initiation along soil moisture boundaries in the southern Great Plains. Mon. Wea. Rev., 138, 11401151, doi:10.1175/2009MWR2865.1.

    • Search Google Scholar
    • Export Citation
  • Gao, H., , Wood E. F. , , and Drusch M. , 2007: Copula derived observation operators for assimilating TMI and AMSR-E soil moisture into land surface models. J. Hydrometeor., 8, 413428, doi:10.1175/JHM570.1.

    • Search Google Scholar
    • Export Citation
  • Gruber, A., , Dorigo W. A. , , Zwieback S. , , Xaver A. , , and Wagner W. , 2013: Characterizing coarse-scale representativeness of in-situ soil moisture measurements from the International Soil Moisture Network. Vadose Zone J., 12, doi:10.2136/vzj2012.0170.

    • Search Google Scholar
    • Export Citation
  • Hain, C. R., , Mecikalski J. R. , , and Anderson M. C. , 2009: Retrieval of an available water-based soil moisture proxy from thermal infrared remote sensing. Part I: Methodology and validation. J. Hydrometeor., 10, 665683, doi:10.1175/2008JHM1024.1.

    • Search Google Scholar
    • Export Citation
  • Kerr, Y., , Waldteufel P. , , Wigneron J.-P. , , Martinuzzi J.-M. , , Font J. , , and Berger M. , 2001: Soil Moisture retrieval from space: the Soil Moisture and Ocean Salinity (SMOS) mission. IEEE Trans. Geosci. Remote Sens., 39, 17291736, doi:10.1109/36.942551.

    • Search Google Scholar
    • Export Citation
  • Koren, V., , Schaake J. , , Mitchell K. E. , , Duan Q. , , Chen F. , , and Baker J. , 1999: A parameterization of snowpack and frozen ground intended for NCEP weather and climate models. J. Geophys. Res., 104, 19 56919 585, doi:10.1029/1999JD900232.

    • Search Google Scholar
    • Export Citation
  • Koren, V., , Smith M. , , Cui Z. , , Cosgrove B. , , Werner K. , , and Zamora R. , 2010: Modification of Sacramento soil moisture accounting heat transfer component (SAC-HT) for enhanced evapotranspiration. NOAA Tech. Rep. NWS 53, 73 pp.

  • Koster, R. D., , and Suarez M. , 1994: The components of the SVAT scheme and their effects on a GCM’s hydrological cycle. Adv. Water Resour., 17, 6178, doi:10.1016/0309-1708(94)90024-8.

    • Search Google Scholar
    • Export Citation
  • Koster, R. D., , and Suarez M. , 1996: Energy and water balance calculations in the Mosaic LSM. NASA Tech. Memo. 104606, Vol. 9, 60 pp. [Available online at http://gmao.gsfc.nasa.gov/pubs/docs/Koster130.pdf.]

  • Koster, R. D., , Guo Z. , , Yang R. , , Dirmeyer P. A. , , Mitchell K. , , and Puma M. J. , 2009: On the nature of soil moisture in land surface models. J. Climate, 22, 43224335, doi:10.1175/2009JCLI2832.1.

    • Search Google Scholar
    • Export Citation
  • Koster, R. D., , Walker G. K. , , Mahanama S. P. P. , , and Reichle R. H. , 2014: Soil moisture initialization error and subgrid variability of precipitation in seasonal streamflow forecasting. J. Hydrometeor., 15, 6988, doi:10.1175/JHM-D-13-050.1.

    • Search Google Scholar
    • Export Citation
  • Kumar, A., , Chen F. , , Barlage M. , , Ek M. B. , , and Niyogi D. , 2014: Assessing impacts of integrating MODIS vegetation data in the Weather Research and Forecasting (WRF) Model coupled to two different canopy-resistance approaches. J. Appl. Meteor. Climatol., 53, 13621380, doi:10.1175/JAMC-D-13-0247.1.

    • Search Google Scholar
    • Export Citation
  • Li, F., , Crow W. T. , , and Kustas W. P. , 2010: Towards the estimation root-zone soil moisture via the simultaneous assimilation of thermal and microwave soil moisture retrievals. Adv. Water Resour., 33, 201214, doi:10.1016/j.advwatres.2009.11.007.

    • Search Google Scholar
    • Export Citation
  • Li, Z., , Zhou T. , , Chen H. , , Ni D. , , and Zhang R.-H. , 2014: Modelling the effect of soil moisture variability on summer precipitation variability over East Asia. Int. J. Climatol., 35, 879–887, doi:10.1002/joc.4023.

    • Search Google Scholar
    • Export Citation
  • Liang, X., , Lettenmaier D. P. , , Wood E. F. , , and Burges S. J. , 1994: A simple hydrologically based model of land surface water and energy fluxes for GCMs. J. Geophys. Res., 99, 14 41514 428, doi:10.1029/94JD00483.

    • Search Google Scholar
    • Export Citation
  • Liu, Q., and et al. , 2011: The contributions of precipitation and soil moisture observations to the skill of soil moisture estimates in a land data assimilation system. J. Hydrometeor., 12, 750765, doi:10.1175/JHM-D-10-05000.1.

    • Search Google Scholar
    • Export Citation
  • Livneh, B., , Xia Y. , , Mitchell K. E. , , Ek M. B. , , and Lettenmaier D. P. , 2010: Noah LSM snow model diagnostics and enhancements. J. Hydrometeor., 11, 721738, doi:10.1175/2009JHM1174.1.

    • Search Google Scholar
    • Export Citation
  • Lohmann, D., and et al. , 2004: Streamflow and water balance intercomparisons of four land surface models in the North American Land Data Assimilation System project. J. Geophys. Res., 109, D07S91, doi:10.1029/2003JD003517.

    • Search Google Scholar
    • Export Citation
  • Martinis, S., , Twele A. , , and Voigt S. , 2009: Towards operational near real-time flood detection using a split-based automatic thresholding procedure on high resolution TerraSAR-X data. Nat. Hazards Earth Syst. Sci., 9, 303314, doi:10.5194/nhess-9-303-2009.

    • Search Google Scholar
    • Export Citation
  • Maurer, E. P., , and Lettenmaier D. P. , 2004: Potential effects of long-lead hydrologic predictability on Missouri River main-stem reservoirs. J. Climate, 17, 174186, doi:10.1175/1520-0442(2004)017<0174:PEOLHP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Mesinger, F., and et al. , 2006: North American Regional Reanalysis. Bull. Amer. Meteor. Soc., 87, 343360, doi:10.1175/BAMS-87-3-343.

    • Search Google Scholar
    • Export Citation
  • Mitchell, K. E., and et al. , 2004: The multi-institution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system. J. Geophys. Res., 109, D07S90, doi:10.1029/2003JD003823.

    • Search Google Scholar
    • Export Citation
  • Mo, K., , and Lettenmaier D. P. , 2014: Objective drought classification using multiple land surface models. J. Hydrometeor., 15, 990–1010, doi:10.1175/JHM-D-13-071.1.

    • Search Google Scholar
    • Export Citation
  • Monteith, J. L., 1965: Evaporation and environment. Symp. Soc. Exp. Biol., 19, 205224.

  • Nijssen, B., , Lettenmaier D. P. , , Liang X. , , Wetzel S. , , and Wood E. F. , 1997: Streamflow simulations for continental-scale river basins. Water Resour. Res., 33, 711724, doi:10.1029/96WR03517.

    • Search Google Scholar
    • Export Citation
  • Ochsner, T. E., and et al. , 2013: State of the art in large-scale soil moisture monitoring. Soil. Sci. Soc. Amer. J., 77, 1888–1919, doi:10.2136/sssaj2013.03.0093.

    • Search Google Scholar
    • Export Citation
  • Pal, J. S., , and Eltahir E. A. B. , 2002: Teleconnections of soil moisture and rainfall during the 1993 Midwest summer flood. Geophys. Res. Lett., 29, 1865, doi:10.1029/2002GL014815.

    • Search Google Scholar
    • Export Citation
  • Peters-Lidard, C. D., , Mocko D. M. , , Garcia M. , , Santanello J. A. Jr., , Tischler M. A. , , Moran M. S. , , and Wu Y. , 2008: Role of precipitation uncertainty in the estimation of hydrologic soil properties using remotely sensed soil moisture in a semiarid environment. Water Resour. Res., 44, W05S18, doi:10.1029/2007WR005884.

    • Search Google Scholar
    • Export Citation
  • Pinker, R. T., and et al. , 2003: Surface radiation budgets in support of the GEWEX Continental-Scale International Project (GCIP) and the GEWEX Americas Prediction Project (GAPP), including the North American Land Data Assimilation System (NLDAS) project. J. Geophys. Res., 108, 8844, doi:10.1029/2002JD003301.

    • Search Google Scholar
    • Export Citation
  • Ray, R. M., , Jacobs J. M. , , and de Alba P. , 2010: Impacts of unsaturated zone soil moisture and ground water table on slope instability. J. Geotech. Geoenviron. Eng., 136, 1448–1458, doi:10.1061/(ASCE)GT.1943-5606.0000357.

    • Search Google Scholar
    • Export Citation
  • Reale, O., , Lau W. K. , , Kim K.-M. , , and Brin E. , 2009: Atlantic tropical cyclogenetic processes during SOP-3 NAMMA in the GEOS-5 Global Data Assimilation and Forecast System. J. Atmos. Sci., 66, 35633578, doi:10.1175/2009JAS3123.1.

    • Search Google Scholar
    • Export Citation
  • Reichle, R. H., , Koster R. D. , , Liu P. , , Mahanama S. P. P. , , Njoku E. G. , , and Owe M. , 2007: Comparison and assimilation of global soil moisture retrievals from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and the Scanning Multichannel Microwave Radiometer (SMMR). J. Geophys. Res., 112, D09108, doi:10.1029/2006JD008033.

    • Search Google Scholar
    • Export Citation
  • Richards, L. A., 1931: Capillary conduction of liquids through porous mediums. J. Appl. Phys., 1, 318333, doi:10.1063/1.1745010.

  • Robock, A., and et al. , 2003: Evaluation of the North American Land Data Assimilation System over the southern Great Plains during warm season. J. Geophys. Res., 108, 8846, doi:10.1029/2002JD003245.

    • Search Google Scholar
    • Export Citation
  • Sabater, J. M., , Jarlan L. , , Calvet J.-C. , , Bouyssel F. , , and De Rosnay D. , 2007: From near-surface to root-zone soil moisture using different assimilation techniques. J. Hydrometeor., 8, 194206, doi:10.1175/JHM571.1.

    • Search Google Scholar
    • Export Citation
  • Santanello, J. A., , Peters-Lidard C. D. , , Kumar S. V. , , Alonge C. , , and Tao W.-K. , 2009: A modeling and observational framework for diagnosing local land–atmosphere coupling on diurnal time scales. J. Hydrometeor., 10, 577388, doi:10.1175/2009JHM1066.1.

    • Search Google Scholar
    • Export Citation
  • Schaake, J. C., and et al. , 2004: An intercomparison of soil moisture fields in the North American Land Data Assimilation System (NLDAS). J. Geophys. Res., 109, D01S90, doi:10.1029/2002JD003309.

    • Search Google Scholar
    • Export Citation
  • Schaefer, G. L., , and Paetzold R. F. , 2001: SNOTEL (SNOwpack TELemetry) and SCAN (Soil Climate Analysis Network). Automated Weather Stations for Applications in Agriculture and Water Resources Management: Current Use and Future Perspectives, K. G. Hubbard and M. V. K. Sivakumar, Eds., WMO/TD No. 1074, WMO, 187–194. [Available online at http://www.wamis.org/agm/pubs/agm3/WMO-TD1074.pdf.]

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

    • Search Google Scholar
    • Export Citation
  • Schroeder, J. L., , Burgett W. S. , , Haynie K. B. , , Sonmez I. , , Skwira G. D. , , Doggett A. L. , , and Lipe J. W. , 2005: The West Texas Mesonet: A technical overview. J. Atmos. Oceanic Technol., 22, 211222, doi:10.1175/JTECH-1690.1.

    • Search Google Scholar
    • Export Citation
  • Scott, B. L., , Ochsner T. E. , , Illston B. G. , , Fiebrich C. A. , , Basara J. B. , , and Sutherland A. J. , 2013: New soil property database Improves Oklahoma Mesonet soil moisture estimates. J. Atmos. Oceanic Technol., 30, 25852595, doi:10.1175/JTECH-D-13-00084.1.

    • Search Google Scholar
    • Export Citation
  • Sridhar, V., , Hubbard K. G. , , You J. , , and Hunt E. D. , 2008: Development of the soil moisture index to quantify agricultural drought and its “user friendliness” in severity–area–duration assessment. J. Hydrometeor., 9, 660676, doi:10.1175/2007JHM892.1.

    • Search Google Scholar
    • Export Citation
  • Taylor, K. E., 2001: Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res., 106, 71837192, doi:10.1029/2000JD900719.

    • Search Google Scholar
    • Export Citation
  • Wagner, W., , Lemoine G. , , and Rott H. , 1999: A method for estimating soil moisture from ERS scatterometer and soil data. Remote Sens. Environ., 70, 191207, doi:10.1016/S0034-4257(99)00036-X.

    • Search Google Scholar
    • Export Citation
  • Wood, E. F., , Lettenmaier D. P. , , Liang X. , , Nijssen B. , , and Wetzel S. W. , 1997: Hydrological modeling of continental-scale basins. Annu. Rev. Earth Planet. Sci., 25, 279300, doi:10.1146/annurev.earth.25.1.279.

    • Search Google Scholar
    • Export Citation
  • Xia, Y., and et al. , 2012a: Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Comparison analysis and application of model products. J. Geophys. Res., 117, D03109, doi:10.1029/2011JD016048.

    • Search Google Scholar
    • Export Citation
  • Xia, Y., and et al. , 2012b: Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 2. Validation of model-simulated streamflow. J. Geophys. Res., 117, D03110, doi:10.1029/2011JD016051.

    • Search Google Scholar
    • Export Citation
  • Xia, Y., , Ek M. B. , , Peters-Lidard C. D. , , Mocko D. , , Svoboda M. , , Sheffield J. , , and Wood E. F. , 2014a: Application of USDM statistics in NLDAS-2: Optimal blended NLDAS drought index over the continental United States. J. Geophys. Res. Atmos., 119, 2947–2965, doi:10.1002/2013JD020994.

    • Search Google Scholar
    • Export Citation
  • Xia, Y., , Sheffield J. , , Ek M. B. , , Dong J. , , Chaney N. , , Wei H. , , Meng J. , , and Wood E. F. , 2014b: Evaluation of multi-model simulated soil moisture in NLDAS-2. J. Hydrol., 512, 107–125, doi:10.1016/j.jhydrol.2014.02.027.

    • Search Google Scholar
    • Export Citation
  • Xia, Y., , Ek M. B. , , Wu Y. , , Ford T. W. , , and Quiring S. M. , 2015a: Comparison of NLDAS-2 simulated and NASMD observed daily soil moisture. Part II: Impact of soil texture and vegetation type mismatch. J. Hydrometeor., 16, 19812000, doi:10.1175/JHM-D-14-0097.1.

    • Search Google Scholar
    • Export Citation
  • Xia, Y., , Ford T. W. , , Wu Y. , , Quiring S. , , and Ek M. B. , 2015b: Automated quality control of in situ soil moisture from the North American Soil Moisture Database using NLDAS-2 products. J. Appl. Meteor. Climatol., 54, 12671282, doi:10.1175/JAMC-D-14-0275.1

    • Search Google Scholar
    • Export Citation
  • Xia, Y., , Peters-Lidard C. D. , , Huang M. , , Wei H. , , and Ek M. , 2015c: Improved NLDAS-2 Noah-simulated hydrometeorological products with an interim run. Hydrol. Processes, 29, 780–792, doi: 10.1002/hyp.10190.

  • Yang, R., , Mitchel K. E. , , Meng J. , , and Ek M. B. , 2011: Summer-season forecast experiments with the NCEP Climate Forecast System using different land models and different initial land states. J. Climate, 24, 23192334, doi:10.1175/2010JCLI3797.1.

    • Search Google Scholar
    • Export Citation
  • Yeh, T. C., , Lee C. H. , , Hsu K.-C. , , and Tian Y.-C. , 2013: Fusion of active and passive hydrologic and geophysical tomography surveys: The future of subsurface characterization. Subsurface Hydrology: Data Integration for Properties and Processes, Geophys. Monogr., Vol. 171, Amer. Geophys. Union, 109–120.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 134 134 37
PDF Downloads 102 102 26

Comparison of NLDAS-2 Simulated and NASMD Observed Daily Soil Moisture. Part I: Comparison and Analysis

View More View Less
  • 1 National Centers for Environmental Prediction/Environmental Modeling Center, and I. M. System Group at NCEP/EMC, College Park, Maryland
  • | 2 National Centers for Environmental Prediction/Environmental Modeling Center, College Park, Maryland
  • | 3 National Centers for Environmental Prediction/Environmental Modeling Center, and I. M. System Group at NCEP/EMC, College Park, Maryland
  • | 4 Department of Geography, Texas A&M University, College Station, Texas
© Get Permissions
Restricted access

Abstract

Soil moisture observations from seven observational networks (spanning portions of seven states) with different biome and climate conditions were used in this study to evaluate multimodel simulated soil moisture products. The four land surface models, including Noah, Mosaic, Sacramento soil moisture accounting (SAC), and the Variable Infiltration Capacity model (VIC), were run within phase 2 of the North American Land Data Assimilation System (NLDAS-2), with a ⅛° spatial resolution and hourly temporal resolution. Hundreds of sites in Alabama, Colorado, Michigan, Nebraska, Oklahoma, West Texas, and Utah were used to evaluate simulated soil moisture in the 0–10-, 10–40-, and 40–100-cm soil layers. Soil moisture was spatially averaged in each state to reduce noise. In general, the four models captured broad features (e.g., seasonal variation) of soil moisture variations in all three soil layers in seven states, except for the 10–40-cm soil layer in West Texas and the 40–100-cm soil layer in Alabama, where the anomaly correlations are weak. Overall, Mosaic, SAC, and the ensemble mean have the highest simulation skill and VIC has the lowest simulation skill. The results show that Noah and VIC are wetter than the observations while Mosaic and SAC are drier than the observations, mostly likely because of systematic errors in model evapotranspiration.

Corresponding author address: Youlong Xia, IMSG at NCEP/EMC, NOAA Center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740. E-mail: youlong.xia@noaa.gov

Abstract

Soil moisture observations from seven observational networks (spanning portions of seven states) with different biome and climate conditions were used in this study to evaluate multimodel simulated soil moisture products. The four land surface models, including Noah, Mosaic, Sacramento soil moisture accounting (SAC), and the Variable Infiltration Capacity model (VIC), were run within phase 2 of the North American Land Data Assimilation System (NLDAS-2), with a ⅛° spatial resolution and hourly temporal resolution. Hundreds of sites in Alabama, Colorado, Michigan, Nebraska, Oklahoma, West Texas, and Utah were used to evaluate simulated soil moisture in the 0–10-, 10–40-, and 40–100-cm soil layers. Soil moisture was spatially averaged in each state to reduce noise. In general, the four models captured broad features (e.g., seasonal variation) of soil moisture variations in all three soil layers in seven states, except for the 10–40-cm soil layer in West Texas and the 40–100-cm soil layer in Alabama, where the anomaly correlations are weak. Overall, Mosaic, SAC, and the ensemble mean have the highest simulation skill and VIC has the lowest simulation skill. The results show that Noah and VIC are wetter than the observations while Mosaic and SAC are drier than the observations, mostly likely because of systematic errors in model evapotranspiration.

Corresponding author address: Youlong Xia, IMSG at NCEP/EMC, NOAA Center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740. E-mail: youlong.xia@noaa.gov
Save