• Abatzoglou, J. T., 2013: Development of gridded surface meteorological data for ecological applications and modeling. Int. J. Climatol., 33, 121131, doi:10.1002/joc.3413.

    • Search Google Scholar
    • Export Citation
  • AghaKouchak, A., Nasrollahi N. , and Habib E. , 2009: Accounting for uncertainties of the TRMM satellite estimates. Remote Sens., 1, 606619, doi:10.3390/rs1030606.

    • Search Google Scholar
    • Export Citation
  • AghaKouchak, A., Habib E. , and Bárdossy A. , 2010: A comparison of three remotely sensed rainfall ensemble generators. Atmos. Res., 98, 387399, doi:10.1016/j.atmosres.2010.07.016.

    • Search Google Scholar
    • Export Citation
  • Alemohammad, S. H., 2014: Characterization of uncertainty in remotely-sensed precipitation estimates. Ph.D. thesis, Massachusetts Institute of Technology, 156 pp. [Available online at http://hamed.mit.edu/sites/default/files/Alemohammad_PhD_Thesis.pdf.]

  • Anderson, E. A., 1973: National Weather Service River Forecast System–Snow accumulation and ablation model. NOAA Tech. Memo. NWS HYDRO-17, 87 pp. [Available online at ftp://ftp.wcc.nrcs.usda.gov/wntsc/H&H/snow/AndersonHYDRO17.pdf.]

  • Austin, P. M., 1987: Relation between measured radar reflectivity and surface rainfall. Mon. Wea. Rev., 115, 10531070, doi:10.1175/1520-0493(1987)115<1053:RBMRRA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bauer, P., Mahfouf J.-F. , Olson W. S. , Marzano F. S. , Michele S. D. , Tassa A. , and Mugnai A. , 2002: Error analysis of TMI rainfall estimates over ocean for variational data assimilation. Quart. J. Roy. Meteor. Soc., 128, 21292214, doi:10.1256/003590002320603575.

    • Search Google Scholar
    • Export Citation
  • Bellerby, T., 2007: Satellite rainfall satellite rainfall uncertainty estimation using an artificial neural network. J. Hydrometeor., 8, 13971412, doi:10.1175/2007JHM846.1.

    • Search Google Scholar
    • Export Citation
  • Bellerby, T., 2013: Ensemble representation of uncertainty in Lagrangian satellite rainfall estimates. J. Hydrometeor., 14, 14831499, doi:10.1175/JHM-D-12-0121.1.

    • Search Google Scholar
    • Export Citation
  • Bellerby, T., and Sun J. , 2005: Probabilistic and ensemble representations of the uncertainty in an IR/microwave satellite precipitation product. J. Hydrometeor., 6, 10321044, doi:10.1175/JHM454.1.

    • Search Google Scholar
    • Export Citation
  • Bindlish, R., and Barros A. P. , 1996: Aggregation of digital terrain data using a modified fractal interpolation scheme. Comput. Geosci., 22, 907917, doi:10.1016/S0098-3004(96)00049-0.

    • Search Google Scholar
    • Export Citation
  • Bohn, T. J., Livneh B. , Oyler J. W. , Running S. W. , Nijssen B. , and Lettenmaier D. P. , 2013: Global evaluation of MTCLIM and related algorithms for forcing of ecological and hydrological models. Agric. For. Meteor., 176, 3849, doi:10.1016/j.agrformet.2013.03.003.

    • 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 computers. Joint Federal and State River Forecast Center, U.S. National Weather Service, and California Department of Water Resources Tech. Rep., 204 pp.

  • Cifelli, R., Chandrasekar V. , Lim S. , Kennedy P. C. , Wang Y. , and Rutledge S. A. , 2011: A new dual-polarization radar rainfall algorithm: Application in Colorado precipitation events. J. Atmos. Oceanic Technol., 28, 352364, doi:10.1175/2010JTECHA1488.1.

    • Search Google Scholar
    • Export Citation
  • Clark, M. P., and Slater A. G. , 2006: Probabilistic quantitative precipitation estimation in complex terrain. J. Hydrometeor., 7, 322, doi:10.1175/JHM474.1.

    • Search Google Scholar
    • Export Citation
  • Clark, M. P., Slater A. G. , Barrett A. P. , Hay L. E. , McCabe G. J. , Rajagopalan B. , and Leavesley G. H. , 2006: Assimilation of snow-covered area information into hydrologic and land-surface models. Adv. Water Resour., 29, 12091221, doi:10.1016/j.advwatres.2005.10.001.

    • 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
  • Daly, C., Halbleib M. , Smith J. I. , Gibson W. P. , Doggett M. K. , Taylor G. H. , Curtis J. , and Pasteris P. P. , 2008: Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. Int. J. Climatol., 28, 2031–2064, doi:10.1002/joc.1688.

    • Search Google Scholar
    • Export Citation
  • Dinku, T., Anagnostou M. , and Borga M. , 2002: Improving radar-based estimation of rainfall over complex terrain. J. Appl. Meteor., 41, 11631178, doi:10.1175/1520-0450(2002)041<1163:IRBEOR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Duan, Q., Sorooshian S. , and Gupta V. K. , 1992: Effective and efficient global optimization for conceptual rainfall–runoff models. Water Resour. Res., 28, 10151031, doi:10.1029/91WR02985.

    • Search Google Scholar
    • Export Citation
  • Durre, I., Menne M. J. , and Vose R. S. , 2008: Strategies for evaluating quality assurance procedures. J. Appl. Meteor. Climatol., 47, 17851791, doi:10.1175/2007JAMC1706.1.

    • Search Google Scholar
    • Export Citation
  • Durre, I., Menne M. J. , Gleason B. E. , Houston T. G. , and Vose R. S. , 2010: Comprehensive automated quality assurance of daily surface observations. J. Appl. Meteor. Climatol., 49, 16151633, doi:10.1175/2010JAMC2375.1.

    • Search Google Scholar
    • Export Citation
  • Eischeid, J. K., Pasteris P. A. , Dias H. F. , Plantico M. S. , and Lott N. J. , 2000: Creating a serially complete, national daily time series of temperature and precipitation for the western United States. J. Appl. Meteor., 39, 15801591, doi:10.1175/1520-0450(2000)039<1580:CASCND>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Fang, J., and Tacher L. , 2003: An efficient and accurate algorithm for generating spatially-correlated random fields. Commun. Numer. Methods Eng., 19, 801808, doi:10.1002/cnm.621.

    • Search Google Scholar
    • Export Citation
  • Gabriel, K. R., and Neumann J. , 1962: A Markov chain model for daily rainfall occurrences at Tel Aviv. Quart. J. Roy. Meteor. Soc., 88, 9095, doi:10.1002/qj.49708837511.

    • Search Google Scholar
    • Export Citation
  • Gebregiorgis, A. S., and Hossain F. , 2015: How well can we estimate error variance of satellite precipitation data around the world? Atmos. Res., 154, 3959, doi:10.1016/j.atmosres.2014.11.005.

    • Search Google Scholar
    • Export Citation
  • Gervais, M., Tremblay L. B. , Gyakum J. R. , and Atallah E. , 2014: Representing extremes in a daily gridded precipitation analysis over the United States: Impacts of station density, resolution, and gridding methods. J. Climate, 27, 52015218, doi:10.1175/JCLI-D-13-00319.1.

    • Search Google Scholar
    • Export Citation
  • Goodison, B. E., Louie P. Y. T. , and Yang D. , 1998: WMO solid precipitation intercomparison. Instruments and Observing Methods Rep. 67, 212 pp. [Available online at https://www.wmo.int/pages/prog/www/IMOP/publications/IOM-67-solid-precip/WMOtd872.pdf.]

  • Greatrex, H., Grimes D. , and Wheeler T. , 2014: Advances in the stochastic modeling of satellite-derived rainfall estimates using a sparse calibration dataset. J. Hydrometeor., 15, 18101831, doi:10.1175/JHM-D-13-0145.1.

    • Search Google Scholar
    • Export Citation
  • Gutmann, E., Pruitt T. , Clark M. P. , Brekke L. , Arnold J. R. , Raff D. A. , and Rasmussen R. M. , 2014: An intercomparison of statistical downscaling methods used for water resource assessments in the United States. Water Resour. Res., 50, 71677186, doi:10.1002/2014WR015559.

    • Search Google Scholar
    • Export Citation
  • Habib, E., Henschke A. , and Adler R. F. , 2009: Evaluation of TMPA satellite-based research and real-time rainfall estimates during six tropical-related heavy rainfall events over Louisiana, USA. Atmos. Res., 94, 373388, doi:10.1016/j.atmosres.2009.06.015.

    • Search Google Scholar
    • Export Citation
  • Hamlet, A. F., and Lettenmaier D. P. , 2005: Production of temporally consistent gridded precipitation and temperature fields for the continental U.S. J. Hydrometeor., 6, 330336, doi:10.1175/JHM420.1.

    • Search Google Scholar
    • Export Citation
  • Haylock, M. R., Hofstra N. , Klein Tank A. M. G. , Klok E. J. , Jones P. D. , and New M. , 2008: A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006. J. Geophys. Res., 113, D20119, doi:10.1029/2008JD010201.

    • Search Google Scholar
    • Export Citation
  • Hossain, F., and Anagnostou E. N. , 2006: A two-dimensional satellite rainfall error model. IEEE Trans. Geosci. Remote Sens., 44, 15111521, doi:10.1109/TGRS.2005.863866.

    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., Adler R. F. , Morrissey M. , Bolvin D. , Curtis S. , Joyce R. , McGavock B. , and Susskind J. , 2001: Global precipitation at one-degree daily resolution from multi-satellite observations. J. Hydrometeor., 2, 3650, doi:10.1175/1525-7541(2001)002<0036:GPAODD>2.0.CO;2.

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

    • Search Google Scholar
    • Export Citation
  • Hungerford, R. D., Nemani R. , Running S. W. , and Coughlan J. C. , 1989: MT-CLIM: A mountain microclimate simulation model. U.S. Forest Service Research Paper INT-414, 52 pp. [Available online at http://www.fs.fed.us/rm/pubs_int/int_rp414.pdf.]

  • Johnson, M. E., 1987: Multivariate Statistical Simulation. Wiley, 230 pp.

  • Kavetski, D. N., Franks S. W. , and Kuczera G. , 2003: Confronting input uncertainty in environmental modelling. Calibration of Watershed Models, Q. Duan et al., Eds, Water Science and Application Series, Vol. 6, Amer. Geophys. Union, 49–68.

  • Kirstetter, P.-E., Hong Y. , Gourley J. J. , Schwaller M. , Petersen W. , and Zhang J. , 2013a: Comparison of TRMM 2a25 products, version 6 and version 7, with NOAA/NSSL ground radar–based national mosaic QPE. J. Hydrometeor., 14, 661669, doi:10.1175/JHM-D-12-030.1.

    • Search Google Scholar
    • Export Citation
  • Kirstetter, P.-E., Viltard N. , and Gosset M. , 2013b: An error model for instantaneous satellite rainfall estimates: Evaluation of BRAIN-TMI over West Africa. Quart. J. Roy. Meteor. Soc., 139, 894911, doi:10.1002/qj.1964.

    • Search Google Scholar
    • Export Citation
  • Kirstetter, P.-E., Gourley J. J. , Hong Y. , Zhang J. , Moazamigoodarzi S. , Langston C. , and Arthur A. , 2015: Probabilistic precipitation rate estimates with ground-based radar networks. Water Resour. Res., 51, 14221442, doi:10.1002/2014WR015672.

    • Search Google Scholar
    • Export Citation
  • Krajewski, W. F., Ciach G. J. , McCollum J. R. , and Bacotiu C. , 2000: Initial validation of the global precipitation climatology project monthly rainfall over the United States. J. Appl. Meteor., 39, 10711086, doi:10.1175/1520-0450(2000)039<1071:IVOTGP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Lehmann, E. L., and D’Abrera H. J. M. , 1998: Nonparametrics: Statistical Methods Based on Ranks. Rev. Ed. Prentice-Hall, 463 pp.

  • Lin, Y., and Mitchell K. E. , 2005: The NCEP stage II/IV hourly precipitation analyses: Development and applications. 19th Conf. on Hydrology, San Diego, CA, Amer. Meteor. Soc., 1.2. [Available online at https://ams.confex.com/ams/Annual2005/webprogram/Paper83847.html.]

  • Livneh, B., Rosenberg E. A. , Lin C. , Nijssen B. , Mishra V. , Andreadis K. M. , Maurer E. P. , and Lettenmaier D. P. , 2013: A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States: Update and extensions. J. Climate, 26, 93849392, doi:10.1175/JCLI-D-12-00508.1.

    • Search Google Scholar
    • Export Citation
  • Loader, C., 1999: Local Regression and Likelihood. Springer, 308 pp.

  • Maggioni, V., Sapiano M. R. P. , Adler R. F. , Tian Y. , and Huffman G. J. , 2014: An error model for uncertainty quantification in high-time-resolution precipitation products. J. Hydrometeor., 15, 12741292, doi:10.1175/JHM-D-13-0112.1.

    • Search Google Scholar
    • Export Citation
  • Maurer, E. P., Wood A. W. , Adam J. C. , Lettenmaier D. P. , and Nijssen B. , 2002: A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States. J. Climate, 15, 32373251, doi:10.1175/1520-0442(2002)015<3237:ALTHBD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Maurer, E. P., Wood A. W. , Adam J. C. , Lettenmaier D. P. , and Nijssen B. , 2013: Gridded meteorological data: 1949-2010. Accessed 12 September 2013. [Available online at http://www.engr.scu.edu/~emaurer/gridded_obs/index_gridded_obs.html.]

  • Menne, M. J., and Williams C. N. Jr., 2005: Detection of undocumented changepoints using multiple test statistics and composite reference series. J. Climate, 18, 42714286, doi:10.1175/JCLI3524.1.

    • Search Google Scholar
    • Export Citation
  • Menne, M. J., Williams C. N. Jr., and Vose R. S. , 2009: The U.S. Historical Climatology Network monthly temperature data, version 2. Bull. Amer. Meteor. Soc., 90, 9931007, doi:10.1175/2008BAMS2613.1.

    • Search Google Scholar
    • Export Citation
  • Menne, M. J., Williams C. N. Jr., and Palecki M. A. , 2010: On the reliability of the U.S. surface temperature record. J. Geophys. Res., 115, D11108, doi:10.1029/2009JD013094.

    • Search Google Scholar
    • Export Citation
  • Menne, M. J., Durre I. , Vose R. S. , Gleason B. E. , and Houston T. G. , 2012a: An overview of the Global Historical Climatology Network-Daily database. J. Atmos. Oceanic Technol., 29, 897910, doi:10.1175/JTECH-D-11-00103.1.

    • Search Google Scholar
    • Export Citation
  • Menne, M. J., and Coauthors, 2012b: Global Historical Climatology Network - Daily (GHCN-Daily), version 2.92. NOAA National Climatic Data Center, accessed 17 September 2013, doi:10.7289/V5D21VHZ.

  • Mesinger, F., and Coauthors, 2006: North American Regional Reanalysis. Bull. Amer. Meteor. Soc., 87, 343360, doi:10.1175/BAMS-87-3-343.

    • Search Google Scholar
    • Export Citation
  • Mizukami, N., Clark M. P. , Slater A. G. , Brekke L. D. , Elsner M. M. , Arnold J. R. , and Gangopadhyay S. , 2014: Hydrological implications of difference large-scale meteorological model forcing datasets in mountainous regions. J. Hydrometeor., 15, 474488, doi:10.1175/JHM-D-13-036.1.

    • Search Google Scholar
    • Export Citation
  • Mizukami, N., and Coauthors, 2015: Implications of the methodological choices for hydrologic portrayals of climate change over the contiguous United States: Statistically downscaled forcing data and hydrologic models. J. Hydrometeor., doi:10.1175/JHM-D-14-0187.1, in press.

    • Search Google Scholar
    • Export Citation
  • Moradkhani, H., Hsu K. , Gupta H. , and Sorooshian S. , 2005: Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter. Water Resour. Res., 41, W05012, doi:10.1029/2004WR003604.

    • Search Google Scholar
    • Export Citation
  • Morrissey, M. L., and Green J. S. , 1998: Uncertainty analysis of satellite rainfall algorithms over the tropical Pacific. J. Geol. Res., 103, 19 56919 576, doi:10.1029/98JD00309.

    • Search Google Scholar
    • Export Citation
  • Nash, J. E., and Sutcliffe J. V. , 1970: River flow forecasting through conceptual models. Part I: A discussion of principles. J. Hydrol., 10, 282290, doi:10.1016/0022-1694(70)90255-6.

    • Search Google Scholar
    • Export Citation
  • Newman, A. J., Sampson K. , Clark M. P. , Bock A. , Viger R. J. , and Blodgett D. , 2014: A large-sample watershed-scale hydrometeorological dataset for the contiguous USA. UCAR/NCAR, doi:10.5065/D6MW2F4D.

  • Newman, A. J., and Coauthors, 2015a: Development of a large-sample watershed-scale hydrometeorological dataset for the contiguous USA: Dataset characteristics and assessment of regional variability in hydrologic model performance. Hydrol. Earth Syst. Sci., 19, 209223, doi:10.5194/hess-19-209-2015.

    • Search Google Scholar
    • Export Citation
  • Newman, A. J., Clark M. P. , Craig J. , Nijssen B. , Wood A. , and Gutmann E. , 2015b: Gridded ensemble precipitation and temperature estimates over the contiguous United States. UCAR/NCAR, doi:10.5065/D6TH8JR2.

  • Nijssen, B., and Lettenmaier D. P. , 2004: Effect of precipitation sampling error on simulated hydrological fluxes and states: Anticipating the Global Precipitation Measurement satellites. J. Geophys. Res., 109, D02103, doi:10.1029/2003JD003497.

    • Search Google Scholar
    • Export Citation
  • Nogueira, M., and Barros A. P. , 2013: The Integrated Precipitation and Hydrology Experiment—Hydrologic Applications for the Southeast US (IPHEx-H4SE): Part III: High-resolution ensemble rainfall products. Rep. EPL-2013-IPHEX-H4SE-3, EPL/Duke University, 38 pp. [Available online at http://hdl.handle.net/10161/8969.]

  • Oyler, J. W., Dobrowski S. Z. , Ballantyne A. P. , Klene A. E. , and Running S. W. , 2015: Artificial amplification of warming trends across the mountains of the western United States. Geophys. Res. Lett., 42, 153–161, doi:10.1002/2014GL062803.

    • Search Google Scholar
    • Export Citation
  • Panofsky, H. A., and Brier G. W. , 1968: Some Applications of Statistics to Meteorology. The Pennsylvania State University, 224 pp.

  • Renard, B., Kavetski D. , Kuczera G. , Thyer M. , and Franks S. W. , 2010: Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors. Water Resour. Res., 46, W05521, doi:10.1029/2009WR008328.

  • Ryzhkov, A. V., Giangrande S. , and Schuur T. , 2003: Rainfall measurements with the polarimetric WSR-88D radar. National Severe Storms Laboratory, 98 pp. [Available online at http://docs.lib.noaa.gov/noaa_documents/OAR/National_Severe_Storms_Laboratory/Rainfall_measurements_Polarimetric_WSR88D_Radar.pdf.]

  • Saha, S., and Coauthors, 2010: The NCEP Climate Forecast System Reanalysis. Bull. Amer. Meteor. Soc., 91, 10151057, doi:10.1175/2010BAMS3001.1.

    • Search Google Scholar
    • Export Citation
  • Sapiano, M. R. P., and Arkin P. A. , 2009: An intercomparison and validation of high-resolution satellite precipitation estimates with 3-hourly gauge data. J. Hydrometeor., 10, 149166, doi:10.1175/2008JHM1052.1.

    • Search Google Scholar
    • Export Citation
  • Schiemann, R., Erdin R. , Willi M. , Frei C. , Berenguer M. , and Sempere-Torres D. , 2011: Geostatistical radar–raingauge combination with nonparametric correlograms: Methodological considerations and application in Switzerland. Hydrol. Earth Syst. Sci., 15, 15151526, doi:10.5194/hess-15-1515-2011.

    • Search Google Scholar
    • Export Citation
  • Serreze, M. C., Clark M. P. , Armstrong R. L. , McGinnis D. A. , and Pulwarty R. L. , 1999: Characteristics of the western U.S. snowpack from snowpack telemetry (SNOTEL) data. Water Resour. Res., 35, 21452160, doi:10.1029/1999WR900090.

    • Search Google Scholar
    • Export Citation
  • Shen, Y., Xiong A. , Wang Y. , and Xie P. , 2010: Performance of high-resolution satellite precipitation products over China. J. Geophys. Res., 115, D02114, doi:10.1029/2009JD012097.

    • Search Google Scholar
    • Export Citation
  • Shepard, D. S., 1968: A two-dimensional interpolation function for irregularly spaced data. Proceedings of the 23rd Association for Computing Machinery Conference, ACM, 517–524, doi:10.1145/800186.810616.

  • Shepard, D. S., 1984: Computer mapping: The SYMAP interpolation algorithm. Spatial Statistics and Models, G. L. Gaile and C. J. Willmott, Eds., D. Reidel, 133–145.

  • Skinner, C. J., Bellerby T. J. , Greatrex H. , and Grimes D. I. F. , 2015: Hydrological modeling using ensemble satellite rainfall estimates in a sparsely gauged river basin: The need for whole ensemble calibration. J. Hydrol., 522, 110122, doi:10.1016/j.jhydrol.2014.12.052.

    • Search Google Scholar
    • Export Citation
  • Stahl, K., Moore R. D. , Floyer J. A. , Asplin M. G. , and McKendry I. G. , 2006: Comparison of approaches for spatial interpolation of daily air temperature in a large region with complex topography and highly variable station density. Agric. For. Meteor., 139, 224236, doi:10.1016/j.agrformet.2006.07.004.

    • Search Google Scholar
    • Export Citation
  • Teegavarapu, R. S. V., 2014: Statistical corrections of spatially interpolated missing precipitation data estimates. Hydrol. Processes, 28, 3789–3808, doi:10.1002/hyp.9906.

    • Search Google Scholar
    • Export Citation
  • Teo, C. K., and Grimes D. I. F. , 2007: Stochastic modelling of rainfall from satellite data. J. Hydrol., 346, 3350, doi:10.1016/j.jhydrol.2007.08.014.

    • Search Google Scholar
    • Export Citation
  • Thornton, P. E., Running S. W. , and White M. A. , 1997: Generating surfaces of daily meteorological variables over large regions of complex terrain. J. Hydrol., 190, 214251, doi:10.1016/S0022-1694(96)03128-9.

    • Search Google Scholar
    • Export Citation
  • Thornton, P. E., Thornton M. M. , Mayer B. W. , Wilhelmi N. , Wei Y. , Devarakonda R. , and Cook R. B. , 2014: Daymet: Daily surface weather data on a 1-km grid for North America, version 2. Oak Ridge National Laboratory Distributed Active Archive Center, accessed 15 November 2014, doi:10.3334/ORNLDAAC/1219.

  • Tian, Y., and Peters-Lidard C. D. , 2007: Systematic anomalies over inland water bodies in satellite-based precipitation estimates. Geophys. Res. Lett., 34, L14403, doi:10.1029/2007GL030787.

    • Search Google Scholar
    • Export Citation
  • Tian, Y., and Coauthors, 2009: Component analysis of errors in satellite-based precipitation estimates. J. Geophys. Res., 114, D24101, doi:10.1029/2009JD011949.

    • Search Google Scholar
    • Export Citation
  • Tian, Y., Huffman G. J. , Adler R. F. , Tang L. , Sapiano M. , Maggioni V. , and Wu H. , 2013: Modeling errors in daily precipitation measurements: Additive or multiplicative? Geophys. Res. Lett., 40, 20602065, doi:10.1002/grl.50320.

    • Search Google Scholar
    • Export Citation
  • Westrick, K. J., Mass C. F. , and Colle B. A. , 1999: The limitations of the WSR-88D radar network for quantitative precipitation measurement over the coastal western United States. Bull. Amer. Meteor. Soc., 80, 22892298, doi:10.1175/1520-0477(1999)080<2289:TLOTWR>2.0.CO;2.

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

  • Wood, A. W., Maurer E. P. , Kumar A. , and Lettenmaier D. P. , 2002: Long-range experimental hydrologic forecasting for the eastern United States. J. Geophys. Res., 107, 4429, doi:10.1029/2001JD000659.

    • Search Google Scholar
    • Export Citation
  • Xia, Y., and Coauthors, 2009: NLDAS_FORA0125_H version 002: NLDAS primary forcing data L4 hourly 0.125 x 0.125 degree. Goddard Earth Sciences Data and Information Services Center, accessed 15 September 2013, doi:10.5067/6J5LHHOHZHN4.

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

    • Search Google Scholar
    • Export Citation
  • Yang, D., Goodison B. E. , Metcalfe J. R. , Golubev V. S. , Bates R. , Pangburn T. , and Hanson C. L. , 1998: Accuracy of NWS 8” standard nonrecording precipitation gauge: Results and application of WMO intercomparison. J. Atmos. Oceanic Technol., 15, 5468, doi:10.1175/1520-0426(1998)015<0054:AONSNP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Young, C. B., Nelson B. R. , Bradley A. A. , Smith J. A. , Peters-Lidard C. D. , Kruger A. , and Baeck M. L. , 1999: An evaluation of NEXRAD precipitation estimates in complex terrain. J. Geophys. Res., 104, 19 69119 703, doi:10.1029/1999JD900123.

    • Search Google Scholar
    • Export Citation
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Gridded Ensemble Precipitation and Temperature Estimates for the Contiguous United States

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  • 1 National Center for Atmospheric Research,* Boulder, Colorado
  • | 2 Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington
  • | 3 National Center for Atmospheric Research,* Boulder, Colorado
  • | 4 Bureau of Reclamation, U.S. Department of Interior, Denver, Colorado
  • | 5 Institute for Water Resources, U.S. Army Corps of Engineers, Seattle, Washington
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Abstract

Gridded precipitation and temperature products are inherently uncertain because of myriad factors, including interpolation from a sparse observation network, measurement representativeness, and measurement errors. Generally uncertainty is not explicitly accounted for in gridded products of precipitation or temperature; if it is represented, it is often included in an ad hoc manner. A lack of quantitative uncertainty estimates for hydrometeorological forcing fields limits the application of advanced data assimilation systems and other tools in land surface and hydrologic modeling. This study develops a gridded, observation-based ensemble of precipitation and temperature at a daily increment for the period 1980–2012 for the conterminous United States, northern Mexico, and southern Canada. This allows for the estimation of precipitation and temperature uncertainty in hydrologic modeling and data assimilation through the use of the ensemble variance. Statistical verification of the ensemble indicates that it has generally good reliability and discrimination of events of various magnitudes but has a slight wet bias for high threshold events (>50 mm). The ensemble mean is similar to other widely used hydrometeorological datasets but with some important differences. The ensemble product produces a more realistic occurrence of precipitation statistics (wet day fraction), which impacts the empirical derivation of other fields used in land surface and hydrologic modeling. In terms of applications, skill in simulations of streamflow in 671 headwater basins is similar to other coarse-resolution datasets. This is the first version, and future work will address temporal correlation of precipitation anomalies, inclusion of other data streams, and examination of topographic lapse rate choices.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Corresponding author address: Andrew J. Newman, NCAR, P.O. Box 3000, Boulder, CO 80307-3000. E-mail: anewman@ucar.edu

Abstract

Gridded precipitation and temperature products are inherently uncertain because of myriad factors, including interpolation from a sparse observation network, measurement representativeness, and measurement errors. Generally uncertainty is not explicitly accounted for in gridded products of precipitation or temperature; if it is represented, it is often included in an ad hoc manner. A lack of quantitative uncertainty estimates for hydrometeorological forcing fields limits the application of advanced data assimilation systems and other tools in land surface and hydrologic modeling. This study develops a gridded, observation-based ensemble of precipitation and temperature at a daily increment for the period 1980–2012 for the conterminous United States, northern Mexico, and southern Canada. This allows for the estimation of precipitation and temperature uncertainty in hydrologic modeling and data assimilation through the use of the ensemble variance. Statistical verification of the ensemble indicates that it has generally good reliability and discrimination of events of various magnitudes but has a slight wet bias for high threshold events (>50 mm). The ensemble mean is similar to other widely used hydrometeorological datasets but with some important differences. The ensemble product produces a more realistic occurrence of precipitation statistics (wet day fraction), which impacts the empirical derivation of other fields used in land surface and hydrologic modeling. In terms of applications, skill in simulations of streamflow in 671 headwater basins is similar to other coarse-resolution datasets. This is the first version, and future work will address temporal correlation of precipitation anomalies, inclusion of other data streams, and examination of topographic lapse rate choices.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Corresponding author address: Andrew J. Newman, NCAR, P.O. Box 3000, Boulder, CO 80307-3000. E-mail: anewman@ucar.edu
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