Editorial Type:
Article
Article Type:
Research Article

Meteorological Driving Datasets for the U.S. Midwest and Great Lakes Region Incorporating Precipitation Gauge Undercatch Corrections

Gonzalo Huidobro aDepartment of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, Notre Dame, Indiana

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Chun-Mei Chiu aDepartment of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, Notre Dame, Indiana

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Kyuhyun Byun bDepartment of Environmental Engineering, Incheon National University, Incheon, South Korea

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Alan F. Hamlet aDepartment of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, Notre Dame, Indiana

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Online Publication:
18 May 2023
Print Publication:
01 May 2023
DOI:
https://doi.org/10.1175/JHM-D-22-0148.1
Page(s):
873–895
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Abstract

Precipitation (P) gauge undercatch (PUC) is an important source of error when using observed meteorological datasets for hydrologic modeling studies in regions with cold and windy winters. Preliminary simulations using the Variable Infiltration Capacity (VIC) hydrological model forced with different meteorological datasets showed significant underprediction of simulated streamflow throughout the domain. A new hybrid gridded meteorological dataset at 1/16° resolution based on observed station data was assembled over the U.S. Midwest and Great Lakes region from 1915 to 2021 at a daily time step. Correction of primary station data using existing techniques is generally difficult or infeasible in the United States due to missing station metadata and lack of local wind speed (WS) measurements. We developed and tested several different postprocessing adjustment techniques using regridded WS obtained from the NCEP–NCAR reanalysis. The most effective approach corrected rain or mixed P using WS alone, and P as snow using a regressed snow-to-P ratio from a group of wind-shielded reference stations (to account for different and generally unknown snow measurement techniques). The PUC-corrected gridded products were validated against high-quality shielded stations and corrected Global Historical Climatology Network stations with in situ WS, showing good overall agreement. Observed monthly streamflow at 40 river basins was also compared to hydrologic model simulations forced by datasets with and without PUC corrections. The best PUC-corrected dataset produced improvements in streamflow simulations in at least 80% of the streamflow locations for three validation metrics (r2, Nash–Sutcliff efficiency, bias in the mean), demonstrating its value for hydrometeorological studies in the greater Midwest region.

Significance Statement

Many applications in hydrology require in situ precipitation (P) measurements, which are known to have a systematic low bias due to the effects of wind, also known as precipitation undercatch (PUC). Addressing PUC is problematic in the United States due to limited access to detailed station metadata (SMD) and local wind speed (WS) measurements. In this paper we develop a set of procedures to create gridded precipitation datasets for the U.S. Midwest region that incorporate corrections for PUC without needing either (i) detailed SMD or (ii) local WS measurements. Among other tests, results in 40 test basins throughout the Midwest show substantial improvements in simulated streamflow in 32 out of 40 basins when PUC corrections are included in meteorological driving datasets.

© 2023 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: Gonzalo Huidobro, ghuidobr@nd.edu

Abstract

Precipitation (P) gauge undercatch (PUC) is an important source of error when using observed meteorological datasets for hydrologic modeling studies in regions with cold and windy winters. Preliminary simulations using the Variable Infiltration Capacity (VIC) hydrological model forced with different meteorological datasets showed significant underprediction of simulated streamflow throughout the domain. A new hybrid gridded meteorological dataset at 1/16° resolution based on observed station data was assembled over the U.S. Midwest and Great Lakes region from 1915 to 2021 at a daily time step. Correction of primary station data using existing techniques is generally difficult or infeasible in the United States due to missing station metadata and lack of local wind speed (WS) measurements. We developed and tested several different postprocessing adjustment techniques using regridded WS obtained from the NCEP–NCAR reanalysis. The most effective approach corrected rain or mixed P using WS alone, and P as snow using a regressed snow-to-P ratio from a group of wind-shielded reference stations (to account for different and generally unknown snow measurement techniques). The PUC-corrected gridded products were validated against high-quality shielded stations and corrected Global Historical Climatology Network stations with in situ WS, showing good overall agreement. Observed monthly streamflow at 40 river basins was also compared to hydrologic model simulations forced by datasets with and without PUC corrections. The best PUC-corrected dataset produced improvements in streamflow simulations in at least 80% of the streamflow locations for three validation metrics (r2, Nash–Sutcliff efficiency, bias in the mean), demonstrating its value for hydrometeorological studies in the greater Midwest region.

Significance Statement

Many applications in hydrology require in situ precipitation (P) measurements, which are known to have a systematic low bias due to the effects of wind, also known as precipitation undercatch (PUC). Addressing PUC is problematic in the United States due to limited access to detailed station metadata (SMD) and local wind speed (WS) measurements. In this paper we develop a set of procedures to create gridded precipitation datasets for the U.S. Midwest region that incorporate corrections for PUC without needing either (i) detailed SMD or (ii) local WS measurements. Among other tests, results in 40 test basins throughout the Midwest show substantial improvements in simulated streamflow in 32 out of 40 basins when PUC corrections are included in meteorological driving datasets.

© 2023 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: Gonzalo Huidobro, ghuidobr@nd.edu

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  • Adam, J. C., and D. P. Lettenmaier, 2003: Adjustment of global gridded precipitation for systematic bias. J. Geophys. Res., 108, 4257, https://doi.org/10.1029/2002JD002499.

    • Search Google Scholar
    • Export Citation

  • Adam, J. C., A. F. Hamlet, and D. P. Lettenmaier, 2009: Implications of global climate change for snowmelt hydrology in the twenty-first century. Hydrol. Processes, 23, 962–972, https://doi.org/10.1002/hyp.7201.

    • Search Google Scholar
    • Export Citation

  • Behrangi, A., B. Khakbaz, T. C. Jaw, A. AghaKouchak, K. Hsu, and S. Sorooshian, 2011: Hydrologic evaluation of satellite precipitation products over a mid-size basin. J. Hydrol., 397, 225–237, https://doi.org/10.1016/j.jhydrol.2010.11.043.

    • Search Google Scholar
    • Export Citation

  • Buban, M. S., T. R. Lee, and C. B. Baker, 2020: A comparison of the U.S. Climate Reference Network precipitation data to the Parameter-Elevation Regressions on Independent Slopes Model (PRISM). J. Hydrometeor., 21, 2391–2400, https://doi.org/10.1175/JHM-D-19-0232.1.

    • Search Google Scholar
    • Export Citation

  • Buisán, S. T., and Coauthors, 2020: The potential for uncertainty in numerical weather prediction model verification when using solid precipitation observations. Atmos. Sci. Lett., 21, e976, https://doi.org/10.1002/asl.976.

    • Search Google Scholar
    • Export Citation

  • Byun, K., and A. F. Hamlet, 2018: Projected changes in future climate over the Midwest and Great Lakes region using downscaled CMIP5 ensembles. Int. J. Climatol., 38, e531–e553, https://doi.org/10.1002/joc.5388.

    • Search Google Scholar
    • Export Citation

  • Coustau, M., F. Rousset-Regimbeau, G. Thirel, F. Habets, B. Janet, E. Martin, C. de Saint-Aubin, and J.-M. Soubeyroux, 2015: Impact of improved meteorological forcing, profile of soil hydraulic conductivity and data assimilation on an operational hydrological ensemble forecast system over France. J. Hydrol., 525, 781–792, https://doi.org/10.1016/j.jhydrol.2015.04.022.

    • Search Google Scholar
    • Export Citation

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

    • Search Google Scholar
    • Export Citation

  • Devine, K. A., and É. Mekis, 2008: Field accuracy of Canadian rain measurements. Atmos.–Ocean, 46, 213–227, https://doi.org/10.3137/ao.460202.

    • Search Google Scholar
    • Export Citation

  • Diamond, H. J., and Coauthors, 2013: U.S. Climate Reference Network after one decade of operations: Status and assessment. Bull. Amer. Meteor. Soc., 94, 485–498, https://doi.org/10.1175/BAMS-D-12-00170.1.

    • Search Google Scholar
    • Export Citation

  • Ehsani, M. R., and A. Behrangi, 2022: A comparison of correction factors for the systematic gauge-measurement errors to improve the global land precipitation estimate. J. Hydrol., 610, 127884, https://doi.org/10.1016/j.jhydrol.2022.127884.

    • Search Google Scholar
    • Export Citation

  • Ferguson, H. L., and D. Storr, 1969: Some current studies of local precipitation variability over western Canada. Water Balance in North America, A. H. Laycock, M. Francisco, and T. Fisher, Eds., American Water Resources Association, 80–100.

  • Goodison, B. E., P. Y. T. Louie, and D. Yang, 1998: WMO solid precipitation measurement intercomparison. Instruments and Observing Methods Rep. 67, WMO/TD-872, 212 pp., https://library.wmo.int/index.php?lvl=notice_display&id=6441#.ZAdjT3ZByUk.

  • Groisman, P. Ya., and D. R. Legates, 1994: The accuracy of United States precipitation data. Bull. Amer. Meteor. Soc., 75, 215–228, https://doi.org/10.1175/1520-0477(1994)075<0215:TAOUSP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Hamlet, A. F., and D. F. Lettenmaier, 2005: Production of temporally consistent gridded precipitation and temperature fields for the continental United States. J. Hydrometeor., 6, 330–336, https://doi.org/10.1175/JHM420.1.

    • Search Google Scholar
    • Export Citation

  • Hamlet, A. F., P. W. Mote, M. P. Clark, and D. P. Lettenmaier, 2005: Effects of temperature and precipitation variability on snowpack trends in the western United States. J. Climate, 18, 4545–4561, https://doi.org/10.1175/JCLI3538.1.

    • Search Google Scholar
    • Export Citation

  • Hamlet, A. F., M. M. Elsner, G. S. Mauger, S.-Y. Lee, I. Tohver, and R. A. Norheim, 2013: An overview of the Columbia Basin Climate Change Scenarios Project: Approach, methods, and summary of key results. Atmos.–Ocean, 51, 392–415, https://doi.org/10.1080/07055900.2013.819555.

    • Search Google Scholar
    • Export Citation

  • Holman, K. D., A. Gronewold, M. Notaro, and A. Zarrin, 2012: Improving historical precipitation estimates over the Lake Superior basin. Geophys. Res. Lett., 39, L03405, https://doi.org/10.1029/2011GL050468.

    • Search Google Scholar
    • Export Citation

  • Hoover, J., M. E. Earle, P. I. Joe, and P. E. Sullivan, 2021: Unshielded precipitation gauge collection efficiency with wind speed and hydrometeor fall velocity. Hydrol. Earth Syst. Sci., 25, 5473–5491, https://doi.org/10.5194/hess-25-5473-2021.

    • Search Google Scholar
    • Export Citation

  • Hunter, T. S., A. H. Clites, K. B. Campbell, and A. D. Gronewold, 2015: Development and application of a North American Great Lakes hydrometeorological database—Part I: Precipitation, evaporation, runoff, and air temperature. J. Great Lakes Res., 41, 65–77, https://doi.org/10.1016/j.jglr.2014.12.006.

    • Search Google Scholar
    • Export Citation

  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437–471, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Kochendorfer, J., and Coauthors, 2017: Analysis of single-Alter-shielded and unshielded measurements of mixed and solid precipitation from WMO-SPICE. Hydrol. Earth Syst. Sci., 21, 3525–3542, https://doi.org/10.5194/hess-21-3525-2017.

    • Search Google Scholar
    • Export Citation

  • Kochendorfer, J., and Coauthors, 2020: Undercatch adjustments for tipping-bucket gauge measurements of solid precipitation. J. Hydrometeor., 21, 1193–1205, https://doi.org/10.1175/JHM-D-19-0256.1.

    • Search Google Scholar
    • Export Citation

  • Kochendorfer, J., and Coauthors, 2022: How well are we measuring snow post-SPICE? Bull. Amer. Meteor. Soc., 103, E370–E388, https://doi.org/10.1175/BAMS-D-20-0228.1.

    • Search Google Scholar
    • Export Citation

  • Køltzow, M., B. Casati, E. Bazile, T. Haiden, and T. Valkonen, 2019: An NWP model intercomparison of surface weather parameters in the European Arctic during the year of polar prediction special observing period Northern Hemisphere 1. Wea. Forecasting, 34, 959–983, https://doi.org/10.1175/WAF-D-19-0003.1.

    • Search Google Scholar
    • Export Citation

  • Køltzow, M., B. Casati, T. Haiden, and T. Valkonen, 2020: Verification of solid precipitation forecasts from numerical weather prediction models in Norway. Wea. Forecasting, 35, 2279–2292, https://doi.org/10.1175/WAF-D-20-0060.1.

    • Search Google Scholar
    • Export Citation

  • Larson, L. W., and E. L. Peck, 1974: Accuracy of precipitation measurements for hydrologic modeling. Water Resour. Res., 10, 857–863, https://doi.org/10.1029/WR010i004p00857.

    • Search Google Scholar
    • Export Citation

  • Leeper, R. D., J. Rennie, and M. A. Palecki, 2015: Observational Perspectives from U.S. Climate Reference Network (USCRN) and Cooperative Observer Program (COOP) Network: Temperature and precipitation comparison. J. Atmos. Oceanic Technol., 32, 703–721, https://doi.org/10.1175/JTECH-D-14-00172.1.

    • Search Google Scholar
    • Export Citation

  • Legates, D. R., 1987: A climatology of global precipitation. Publ. Climatol., 40(1), 85 pp.

  • Lespinas, F., V. Fortin, G. Roy, P. Rasmussen, and T. Stadnyk, 2015: Performance evaluation of the Canadian Precipitation Analysis (CaPA). J. Hydrometeor., 16, 2045–2064, https://doi.org/10.1175/JHM-D-14-0191.1.

    • Search Google Scholar
    • Export Citation

  • Liang, X., D. P. Lettenmaier, E. F. Wood, and S. J. Burges, 1994: A simple hydrologically based model of land surface water and energy fluxes for general circulation models. J. Geophys. Res., 99, 14 415–14 428, https://doi.org/10.1029/94JD00483.

    • Search Google Scholar
    • Export Citation

  • Liang, X., E. F. Wood, and D. P. Lettenmaier, 1996: Surface soil moisture parameterization of the VIC-2L model: Evaluation and modification. Global Planet. Change, 13, 195–206, https://doi.org/10.1016/0921-8181(95)00046-1.

    • Search Google Scholar
    • Export Citation

  • Livneh, B., E. A. Rosenberg, C. Lin, B. Nijssen, V. Mishra, K. M. Andreadis, E. P. Maurer, and D. P. Lettenmaier, 2013: A long-term hydrologically based data set of land surface fluxes and states for the conterminous United States: Updates and extensions. J. Climate, 26, 9384–9392, https://doi.org/10.1175/JCLI-D-12-00508.1.

    • Search Google Scholar
    • Export Citation

  • Livneh, B., T. J. Bohn, D. W. Pierce, F. Munoz-Arriola, B. Nijssen, R. Vose, D. R. Cayan, and L. Brekke, 2015: A spatially comprehensive, hydrometeorological data set for Mexico, the U.S., and Southern Canada 1950–2013. Sci. Data, 2, 150042, https://doi.org/10.1038/sdata.2015.42.

    • Search Google Scholar
    • Export Citation

  • Lohmann, D., E. Raschke, B. Nijssen, and D. P. Lettenmaier, 1998: Regional scale hydrology: I. Formulation of the VIC-2L model coupled to a routing model. Hydrol. Sci. J., 43, 131–141, https://doi.org/10.1080/02626669809492107.

    • Search Google Scholar
    • Export Citation

  • Lundquist, J., M. Hughes, E. Gutmann, and S. Kapnick, 2019: Our skill in modeling mountain rain and snow is bypassing the skill of our observational networks. Bull. Amer. Meteor. Soc., 100, 2473–2490, https://doi.org/10.1175/BAMS-D-19-0001.1.

    • Search Google Scholar
    • Export Citation

  • Lussana, C., T. Saloranta, T. Skaugen, J. Magnusson, O. E. Tveito, and J. Andersen, 2018: seNorge2 daily precipitation, an observational gridded dataset over Norway from 1957 to the present day. Earth Syst. Sci. Data, 10, 235–249, https://doi.org/10.5194/essd-10-235-2018.

    • Search Google Scholar
    • Export Citation

  • Maurer, E. P., A. W. Wood, J. C. Adam, D. P. Lettenmaier, and B. Nijessen, 2002: A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States. J. Climate, 15, 3237–3251, https://doi.org/10.1175/1520-0442(2002)015<3237:ALTHBD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Mekis, É., and L. A. Vincent, 2011: An overview of the second generation adjusted daily precipitation dataset for trend analysis in Canada. Atmos.–Ocean, 49, 163–177, https://doi.org/10.1080/07055900.2011.583910.

    • Search Google Scholar
    • Export Citation

  • Metcalfe, J. R., S. Ishida, and B. E. Goodison, 1994: A corrected precipitation archive for the Northwest Territories. Mackenzie Basin Impact Study, Interim Rep. No 2, Environment Canada, 110–117.

  • Mote, P. W., A. F. Hamlet, M. P. Clark, and D. P. Lettenmaier, 2005: Declining mountain snowpack in western North America. Bull. Amer. Meteor. Soc., 86, 39–50, https://doi.org/10.1175/BAMS-86-1-39.

    • Search Google Scholar
    • Export Citation

  • Nash, J. E., and J. V. Sutcliffe, 1970: River flow forecasting through conceptual models part I—A discussion of principles. J. Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6.

    • Search Google Scholar
    • Export Citation

  • Nitu, R., and Coauthors, 2019: WMO Solid Precipitation Intercomparison Experiment (SPICE) (2012–2015). WMO/IOM-131, 1445 pp., https://hdl.handle.net/20.500.11765/10839.

  • Panahi, M., and A. Behrangi, 2020: Comparative analysis of snowfall accumulation and gauge undercatch correction factors from diverse data sets: In situ, satellite, and reanalysis. Asia-Pac. J. Atmos. Sci., 56, 615–628, https://doi.org/10.1007/s13143-019-00161-6.

    • Search Google Scholar
    • Export Citation

  • Rasmussen, R., and Coauthors, 2012: How well are we measuring snow: The NOAA/FAA/NCAR Winter Precipitation Test Bed. Bull. Amer. Meteor. Soc., 93, 811–829, https://doi.org/10.1175/BAMS-D-11-00052.1.

    • Search Google Scholar
    • Export Citation

  • Robinson, E. L., and D. B. Clark, 2020: Using gravity recovery and climate experiment data to derive corrections to precipitation data sets and improve modelled snow mass at high latitudes. Hydrol. Earth Syst. Sci., 24, 1763–1779, https://doi.org/10.5194/hess-24-1763-2020.

    • Search Google Scholar
    • Export Citation

  • Rodda, J. C., 1971: The precipitation measurement paradox—The instrument accuracy problem. WMO-316, 42 pp.

  • Sapiano, M. R. P., and P. A. Arkin, 2009: An intercomparison and validation of high-resolution satellite precipitation estimates with 3-hourly gauge data. J. Hydrometeor., 10, 149–166, https://doi.org/10.1175/2008JHM1052.1.

    • Search Google Scholar
    • Export Citation

  • Sevruk, B., 1982: Methods of correction for systematic error in point precipitation measurement for operational use. WMO Operational Hydrology Rep. 21, 91 pp.

  • Slack, J. R., A. M. Lumb, and J. M. Landwehr, 1994: Hydro-Climatic Data Network (HCDN)–A USGS streamflow data set for the U.S. for the study of climate fluctuations. USGS Water Resources Investigations Rep. 93-4076, https://doi.org/10.3133/wri934076.

  • Smith, C. D., E. Mekis, M. Hartwell, and A. Ross, 2022: The hourly wind-bias-adjusted precipitation data set from the Environment and Climate Change Canada automated surface observation network (2001–2019). Earth Syst. Sci. Data, 14, 5253–5265, https://doi.org/10.5194/essd-14-5253-2022.

    • Search Google Scholar
    • Export Citation

  • Tong, K., F. Su, D. Yang, L. Zhang, and Z. Hao, 2014: Tibetan Plateau precipitation as depicted by gauge observations, reanalyses and satellite retrievals. Int. J. Climatol., 34, 265–285, https://doi.org/10.1002/joc.3682.

    • Search Google Scholar
    • Export Citation

  • U.S. Army Corps of Engineers, 1956: Snow hydrology: Summary report of the snow investigations. North Pacific Division, USACE, 437 pp.

  • Wagner, D. N., and Coauthors, 2021: Snowfall and snow accumulation processes during MOSAiC. EGU General Assembly 2021, Online, EGU, EGU21-12692, https://doi.org/10.5194/egusphere-egu21-12692.

  • Wang, X. L., H. Xu, B. Qian, Y. Feng, and E. Mekis, 2017: Adjusted daily rainfall and snowfall data for Canada. Atmos.–Ocean, 55, 155–168, https://doi.org/10.1080/07055900.2017.1342163.

    • Search Google Scholar
    • Export Citation

  • WMO, 1983: Guide to meteorological instruments and methods of observation. WMO-8, 681 pp., https://www.weather.gov/media/epz/mesonet/CWOP-WMO8.pdf.

  • Wolff, M. A., K. Isaksen, A. Petersen-Øverleir, K. Ødemark, T. Reitan, and R. Brækkanand, 2015: Derivation of a new continuous adjustment function for correcting wind-induced loss of solid precipitation: Results of a Norwegian field study. Hydrol. Earth Syst. Sci., 19, 951–967, https://doi.org/10.5194/hess-19-951-2015.

    • Search Google Scholar
    • Export Citation

  • Yang, D., 1999: An improved precipitation climatology for the Arctic Ocean. Geophys. Res. Lett., 26, 1625–1628, https://doi.org/10.1029/1999GL900311.

    • Search Google Scholar
    • Export Citation

  • Yang, D., and T. Ohata, 2001: A bias-corrected Siberian regional precipitation climatology. J. Hydrometeor., 2, 122–139, https://doi.org/10.1175/1525-7541(2001)002<0122:ABCSRP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Yang, D., B. E. Goodison, J. R. Metcalfe, V. S. Golubev, R. Bates, T. Pangburn, and C. L. Hanson, 1998a: Accuracy of NWS 8” standard nonrecording precipitation gauge: Results and application of WMO intercomparison. J. Atmos. Oceanic Technol., 15, 54–68, https://doi.org/10.1175/1520-0426(1998)015<0054:AONSNP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Yang, D., B. E. Goodison, S. Ishida, and C. S. Benson, 1998b: Adjustment of daily precipitation data at 10 climate stations in Alaska: Application of World Meteorological Organization intercomparison results. Water Resour. Res., 34, 241–256, https://doi.org/10.1029/97WR02681.

    • Search Google Scholar
    • Export Citation

  • Yang, D., D. Kane, Z. Zhang, D. Legate, and B. Goodison, 2005: Bias correction of long-term (1973-2004) daily precipitation data over the northern regions. Geophys. Res. Lett., 32, L19501, https://doi.org/10.1029/2005GL024057.

    • Search Google Scholar
    • Export Citation

  • Ye, B., D. Yang, Y. Ding, T. Han, and T. Koike, 2004: A bias-corrected precipitation climatology for China. J. Hydrometeor., 5, 1147–1160, https://doi.org/10.1175/JHM-366.1.

    • Search Google Scholar
    • Export Citation
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