Editorial Type:
Article
Article Type:
Research Article

Daily High-Resolution Temperature and Precipitation Fields for the Contiguous United States from 1951 to Present

Imke Durre aNOAA/National Centers for Environmental Information, Asheville, North Carolina

Search for other papers by Imke Durre in
Current site
Google Scholar
PubMed Close
,
Anthony Arguez aNOAA/National Centers for Environmental Information, Asheville, North Carolina

Search for other papers by Anthony Arguez in
Current site
Google Scholar
PubMed Close
,
Carl J. Schreck III bCooperative Institute for Satellite Earth System Studies, North Carolina State University, Asheville, North Carolina

Search for other papers by Carl J. Schreck III in
Current site
Google Scholar
PubMed Close
,
Michael F. Squires cSaint Louis, Missouri

Search for other papers by Michael F. Squires in
Current site
Google Scholar
PubMed Close
, and
Russell S. Vose aNOAA/National Centers for Environmental Information, Asheville, North Carolina

Search for other papers by Russell S. Vose in
Current site
Google Scholar
PubMed Close
Online Publication:
18 Nov 2022
Print Publication:
01 Dec 2022
DOI:
https://doi.org/10.1175/JTECH-D-22-0024.1
Page(s):
1837–1855
Restricted access

Abstract

In this paper, a new set of daily gridded fields and area averages of temperature and precipitation is introduced that covers the contiguous United States (CONUS) from 1951 to present. With daily updates and a grid resolution of approximately 0.0417° (nominally 5 km), the product, named nClimGrid-Daily, is designed to be used particularly in climate monitoring and other applications that rely on placing event-specific meteorological patterns into a long-term historical context. The gridded fields were generated by interpolating morning and midnight observations from the Global Historical Climatology Network–Daily dataset using thin-plate smoothing splines. Additional processing steps limit the adverse effects of spatial and temporal variations in station density, observation time, and other factors on the quality and homogeneity of the fields. The resulting gridded data provide smoothed representations of the point observations, although the accuracy of estimates for individual grid points and days can be sensitive to local spatial variability and the ability of the available observations and interpolation technique to capture that variability. The nClimGrid-Daily dataset is therefore recommended for applications that require the aggregation of estimates in space and/or time, such as climate monitoring analyses at regional to national scales.

Significance Statement

Many applications that use historical weather observations require data on a high-resolution grid that are updated daily. Here, a new dataset of daily temperature and precipitation for 1951–present is introduced that was created by interpolating irregularly spaced observations to a regular grid with a spacing of 0.0417° across the contiguous United States. Compared to other such datasets, this product is particularly suitable for monitoring climate and drought on a daily basis because it was processed so as to limit artificial variations in space and time that may result from changes in the types and distribution of observations used.

© 2022 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: Imke Durre, imke.durre@noaa.gov

Abstract

In this paper, a new set of daily gridded fields and area averages of temperature and precipitation is introduced that covers the contiguous United States (CONUS) from 1951 to present. With daily updates and a grid resolution of approximately 0.0417° (nominally 5 km), the product, named nClimGrid-Daily, is designed to be used particularly in climate monitoring and other applications that rely on placing event-specific meteorological patterns into a long-term historical context. The gridded fields were generated by interpolating morning and midnight observations from the Global Historical Climatology Network–Daily dataset using thin-plate smoothing splines. Additional processing steps limit the adverse effects of spatial and temporal variations in station density, observation time, and other factors on the quality and homogeneity of the fields. The resulting gridded data provide smoothed representations of the point observations, although the accuracy of estimates for individual grid points and days can be sensitive to local spatial variability and the ability of the available observations and interpolation technique to capture that variability. The nClimGrid-Daily dataset is therefore recommended for applications that require the aggregation of estimates in space and/or time, such as climate monitoring analyses at regional to national scales.

Significance Statement

Many applications that use historical weather observations require data on a high-resolution grid that are updated daily. Here, a new dataset of daily temperature and precipitation for 1951–present is introduced that was created by interpolating irregularly spaced observations to a regular grid with a spacing of 0.0417° across the contiguous United States. Compared to other such datasets, this product is particularly suitable for monitoring climate and drought on a daily basis because it was processed so as to limit artificial variations in space and time that may result from changes in the types and distribution of observations used.

© 2022 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: Imke Durre, imke.durre@noaa.gov

Supplementary Materials

    • Supplemental Materials (PDF 25.9 MB)
Save
Cover Journal of Atmospheric and Oceanic Technology
Journal of Atmospheric and Oceanic Technology

  • Behnke, R., S. Vavrus, A. Allstadt, T. Albright, W. Thogmartin, and V. Radeloff, 2016: Evaluation of downscaled, gridded climate data for the conterminous United States. Ecol. Appl., 26, 1338–1351, https://doi.org/10.1002/15-1061.

    • Search Google Scholar
    • Export Citation

  • Caesar, J., L. Alexander, and R. Vose, 2006: Large-scale changes in observed daily maximum and minimum temperatures: Creation and analysis of a new gridded data set. J. Geophys. Res., 111, D05101, https://doi.org/10.1029/2005JD006280.

    • Search Google Scholar
    • Export Citation

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

    • Search Google Scholar
    • Export Citation

  • Contractor, S., and Coauthors, 2020: Rainfall Estimates on a Gridded Network (REGEN)—A global land-based gridded dataset of daily precipitation from 1950 to 2016. Hydrol. Earth Syst. Sci., 24, 919–943, https://doi.org/10.5194/hess-24-919-2020.

    • Search Google Scholar
    • Export Citation

  • Craven, P., and G. Wahba, 1979: Smoothing noisy data with spline functions. Numer. Math., 31, 377–403, https://doi.org/10.1007/BF01404567.

    • Search Google Scholar
    • Export Citation

  • Daly, C., W. P. Gibson, G. H. Taylor, M. K. Doggett, and J. I. Smith, 2007: Observer bias in daily precipitation measurements at United States Cooperative network stations. Bull. Amer. Meteor. Soc., 88, 899–912, https://doi.org/10.1175/BAMS-88-6-899.

    • Search Google Scholar
    • Export Citation

  • Daly, C., and Coauthors, 2021: Challenges in observation-based mapping of daily precipitation across the conterminous United States. J. Atmos. Oceanic Technol., 38, 1979–1992, https://doi.org/10.1175/JTECH-D-21-0054.1.

    • Search Google Scholar
    • Export Citation

  • DeGaetano, A. T., 1999: A method to infer observation time based on day-to-day temperature variations. J. Climate, 12, 3443–3456, https://doi.org/10.1175/1520-0442(1999)012<3443:AMTIOT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • DeGaetano, A. T., and B. N. Belcher, 2007: Spatial interpolation of daily maximum and minimum air temperature based on meteorological model analyses and independent observations. J. Appl. Meteor. Climatol., 46, 1981–1992, https://doi.org/10.1175/2007JAMC1536.1.

    • Search Google Scholar
    • Export Citation

  • Diluzio, M., G. L. Johnson, C. Daly, J. K. Eischeid, and J. G. Arnold, 2008: Constructing retrospective gridded daily precipitation and temperature datasets for the conterminous United States. J. Appl. Meteor. Climatol., 47, 475–497, https://doi.org/10.1175/2007JAMC1356.1.

    • Search Google Scholar
    • Export Citation

  • Donat, M. G., L. V. Alexander, H. Yang, I. Durre, R. S. Vose, and J. Caesar, 2013: Global land-based datasets for monitoring climatic extremes. Bull. Amer. Meteor. Soc., 94, 997–1006, https://doi.org/10.1175/BAMS-D-12-00109.1.

    • Search Google Scholar
    • Export Citation

  • Dunn, R. J. H., K. M. Willett, P. W. Thorne, E. Woolley, I. Durre, A. Dai, D. E. Parker, and R. S. Vose, 2012: HadISD: A quality controlled global synoptic report database for selected variables at long-term stations from 1973–2010. Climate Past, 8, 1649–1679, https://doi.org/10.5194/cp-8-1649-2012.

    • Search Google Scholar
    • Export Citation

  • Durre, I., M. J. Menne, B. E. Gleason, T. G. Houston, and R. S. Vose, 2010: Comprehensive automated quality assurance of daily surface observations. J. Appl. Meteor. Climatol., 49, 1615–1633, https://doi.org/10.1175/2010JAMC2375.1.

    • Search Google Scholar
    • Export Citation

  • Durre, I., M. F. Squires, R. S. Vose, A. Arguez, W. S. Gross, J. R. Rennie, and C. J. Schreck, 2022: NOAA’s nClimGrid-Daily version 1—Daily gridded temperature and precipitation for the contiguous United States since 1951. NOAA National Centers for Environmental Information, 6 May 2022, https://doi.org/10.25921/c4gt-r169.

  • Ensor, L. A., and S. M. Robeson, 2008: Statistical characteristics of daily precipitation: Comparisons of gridded and point datasets. J. Appl. Meteor. Climatol., 47, 2468–2476, https://doi.org/10.1175/2008JAMC1757.1.

    • Search Google Scholar
    • Export Citation

  • Gervais, M., L. B. Tremblay, J. R. Gyakum, and E. Atallah, 2014: Representing extremes in a daily gridded precipitation analysis over the United States: Impacts of station density, resolution, and gridding methods. J. Climate, 27, 5201–5218, https://doi.org/10.1175/JCLI-D-13-00319.1.

    • Search Google Scholar
    • Export Citation

  • Guttman, N. B., and C. B. Baker, 1996: Exploratory analysis of the difference between temperature observations recorded by ASOS and conventional methods. Bull. Amer. Meteor. Soc., 77, 2865–2874, https://doi.org/10.1175/1520-0477(1996)077<2865:EAOTDB>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

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

    • Search Google Scholar
    • Export Citation

  • Herrera, S., J. Fernández, and J. M. Gutiérrez, 2016: Update of the Spain02 gridded observational dataset for EURO-CORDEX evaluation: Assessing the effect of the interpolation methodology. Int. J. Climatol., 36, 900–908, https://doi.org/10.1002/joc.4391.

    • Search Google Scholar
    • Export Citation

  • Herrera, S., S. Kotlarski, P. M. Soares, R. M. Cardoso, A. Jaczewski, J. M. Gutiérrez, and D. Maraun, 2019: Uncertainty in gridded precipitation products: Influence of station density, interpolation method and grid resolution. Int. J. Climatol., 39, 3717–3729, https://doi.org/10.1002/joc.5878.

    • Search Google Scholar
    • Export Citation

  • Hofstra, N., M. New, and C. McSweeney, 2009: The influence of interpolation and station network density on the distributions and trends of climate variables in gridded daily data. Climate Dyn., 35, 841–858, https://doi.org/10.1007/s00382-009-0698-1.

    • Search Google Scholar
    • Export Citation

  • Huffman, G. J., R. F. Adler, M. M. Morrissey, S. Curtis, R. Joyce, B. McGavock, and J. Susskind, 2001: Global precipitation at one-degree daily resolution from multisatellite observations. J. Hydrometeor., 2, 36–50, https://doi.org/10.1175/1525-7541(2001)002<0036:GPAODD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Hutchinson, M. F., 1998a: Interpolation of rainfall data with thin plate smoothing splines: I. Two dimensional smoothing of data with short range correlation. J. Geogr. Inf. Decis. Anal., 2, 152–167.

    • Search Google Scholar
    • Export Citation

  • Hutchinson, M. F., 1998b: Interpolation of rainfall data with thin plate smoothing splines: II. Analysis of topographic dependence. J. Geogr. Inf. Decis. Anal., 2, 168–185.

    • Search Google Scholar
    • Export Citation

  • Hutchinson, M. F., 2007: ANUSPLIN version 4.37 user guide. Australian National University Centre for Resource and Environmental Studies Doc., 54 pp., https://vdocuments.mx/anusplin-version-437-user-guide.html.

  • Hutchinson, M. F., D. W. McKenney, K. Lawrence, J. H. Pedlar, R. F. Hopkinson, E. Milewska, and P. Papadopol, 2009: Development and testing of Canada-wide interpolated spatial models of daily minimum–maximum temperature and precipitation for 1961–2003. J. Appl. Meteor. Climatol., 48, 725–741, https://doi.org/10.1175/2008JAMC1979.1.

    • Search Google Scholar
    • Export Citation

  • Janis, M. J., 2002: Observation-time-dependent biases and departures for daily minimum and maximum air temperatures. J. Appl. Meteor., 41, 588–603, https://doi.org/10.1175/1520-0450(2002)041<0588:OTDBAD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Jarvis, C. H., and N. Stuart, 2001a: A comparison among strategies for interpolating maximum and minimum daily air temperatures. Part I: The selection of “guiding” topographic and land cover variables. J. Appl. Meteor., 40, 1060–1074, https://doi.org/10.1175/1520-0450(2001)040<1060:ACASFI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Jarvis, C. H., and N. Stuart, 2001b: A comparison among strategies for interpolating maximum and minimum daily air temperatures. Part II: The interaction between number of guiding variables and the type of interpolation method. J. Appl. Meteor., 40, 1075–1084, https://doi.org/10.1175/1520-0450(2001)040<1075:ACASFI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Karl, T. R., C. N. Williams Jr., W. M. Young, and P. M. Wendland, 1986: A model to estimate the time of observation bias associated with monthly mean maximum, minimum and mean temperatures for the United States. J. Climate Appl. Meteor., 25, 145–160, https://doi.org/10.1175/1520-0450(1986)025<0145:AMTETT>2.0.CO;2.

    • 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

  • Menne, M. J., and C. N. Williams Jr., 2009: Homogenization of temperature series via pairwise comparisons. J. Climate, 22, 1700–1717, https://doi.org/10.1175/2008JCLI2263.1.

    • Search Google Scholar
    • Export Citation

  • Menne, M. J., and Coauthors, 2012a: Global Historical Climatology Network–Daily (GHCN-Daily), version 3. NOAA National Centers for Environmental Information, 1 April 2017, https://doi.org/10.7289/V5D21VHZ.

  • Menne, M. J., I. Durre, R. S. Vose, B. E. Gleason, and T. G. Houston, 2012b: An overview of the Global Historical Climatology Network–Daily database. J. Atmos. Oceanic Technol., 29, 897–910, https://doi.org/10.1175/JTECH-D-11-00103.1.

    • Search Google Scholar
    • Export Citation

  • Myrick, D. T., and J. D. Horel, 2008: Sensitivity of surface analyses over the western United States to RAWS observations. Wea. Forecasting, 23, 145–158, https://doi.org/10.1175/2007WAF2006074.1.

    • Search Google Scholar
    • Export Citation

  • Newman, A. J., and Coauthors, 2015: Gridded ensemble precipitation and temperature estimates for the contiguous United States. J. Hydrol., 16, 2481–2500, https://doi.org/10.1175/JHM-D-15-0026.1.

    • Search Google Scholar
    • Export Citation

  • Palmer, W. C., 1965: Meteorological drought. U.S. Weather Bureau Research Paper 45, 58 pp.

  • Serreze, M. C., M. P. Clark, and R. L. Armstrong, 1999: Characteristics of the western United States snowpack from snowpack telemetry (SNOTEL) data. Water Resour. Res., 35, 2145–2160, https://doi.org/10.1029/1999WR900090.

    • Search Google Scholar
    • Export Citation

  • Thorne, P. W., and Coauthors, 2016: Reassessing changes in diurnal temperature range: Intercomparison and evaluation of existing global dataset estimates. J. Geophys. Res. Atmos., 121, 5138–5158, https://doi.org/10.1002/2015JD024584.

    • Search Google Scholar
    • Export Citation

  • Thornton, P. E., S. W. Running, and M. A. White, 1997: Generating surfaces of daily meteorological variables over large regions of complex terrain. J. Hydrol., 190, 214–251, https://doi.org/10.1016/S0022-1694(96)03128-9.

    • Search Google Scholar
    • Export Citation

  • Thornton, P. E., R. Shrestha, M. Thornton, S. Kao, Y. Wei, and B. E. Wilson, 2021: Gridded daily weather data for North America with comprehensive uncertainty quantification. Sci. Data, 8, 190, https://doi.org/10.1038/s41597-021-00973-0.

    • Search Google Scholar
    • Export Citation

  • Vose, R. S., and Coauthors, 2014: Improved historical temperature and precipitation time series for U.S. climate divisions. J. Appl. Meteor. Climatol., 53, 1232–1251, https://doi.org/10.1175/JAMC-D-13-0248.1.

    • Search Google Scholar
    • Export Citation

  • Wagner, P. D., P. Fiener, F. Wilken, S. Kumar, and K. Schneider, 2012: Comparison and evaluation of spatial interpolation schemes for daily rainfall in data scarce regions. J. Hydrol., 464–465, 388–400, https://doi.org/10.1016/j.jhydrol.2012.07.026.

    • Search Google Scholar
    • Export Citation

  • Walton, D., and A. Hall, 2018: An assessment of high-resolution gridded temperature datasets over California. J. Climate, 31, 3789–3810, https://doi.org/10.1175/JCLI-D-17-0410.1.

    • Search Google Scholar
    • Export Citation

  • Willmott, C. J., C. M. Rowe, and W. D. Philpot, 1985: Small-scale climate maps: A sensitivity analysis of some common assumptions associated with grid-point interpolation and contouring. Amer. Cartographer, 12, 5–16, https://doi.org/10.1559/152304085783914686.

    • Search Google Scholar
    • Export Citation

  • Zachariassen, J., K. F. Zeller, N. Nikolov, and T. McClelland, 2003: A review of the Forest Service Remote Automated Weather Station (RAWS) network. U.S. Forest Service Rocky Mountain Research Station Tech. Rep. RMRSGTR-119, 153 pp., http://www.fs.fed.us/rm/pubs/rmrs_gtr119.pdf.

All Time Past Year Past 30 Days
Abstract Views 1214 608 45
Full Text Views 646 430 43
PDF Downloads 400 197 9