Constructing retrospective gridded daily precipitation and temperature datasets for the conterminous United States
Open accessAbstract
This paper presents and evaluates a method for the construction of long-range and wide-area temporal spatial datasets of daily precipitation and temperature (maximum and minimum). This method combines the interpolation of daily ratios/fractions derived from ground-based meteorological station records and respective fields of monthly estimates. Data sources for the described implementation over the conterminous United States (CONUS) are two independent and quality-controlled inputs: 1) an enhanced compilation of daily observations derived from the National Climatic Data Center digital archives and 2) the Parameter–Elevation Regressions on Independent Slopes Model (PRISM) maps. The results of this study show that this nonconventional interpolation preserves the spatial and temporal distribution of both the PRISM maps (monthly, topography-sensitive patterns) and the original daily observations. Statistics of a preliminary point comparison with the observed values at high-quality and independent reference sites show a reasonable agreement and a noticeable improvement over the nearest station method in orographically sensitive areas. The implemented datasets provide daily precipitation and temperature values at 2.5-min (around 4 km) resolution for 1960–2001. Combining seamless spatial and temporal coverage and topographic sensitivity characteristics, the datasets offer the potential for supporting current and future regional and historical hydrologic assessments over the CONUS.
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Cites
Very high resolution interpolated climate surfaces for global land areas
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G., & Jarvis, A. (2005) · International Journal of Climatology · doi:10.1002/joc.1276 · Cited by 19,860 · see citing works
LARGE AREA HYDROLOGIC MODELING AND ASSESSMENT PART I: MODEL DEVELOPMENT <sup>1</sup>
Arnold, J. G., Srinivasan, R., Muttiah, R. S., & Williams, J. R. (1998) · JAWRA Journal of the American Water Resources Association · doi:10.1111/j.1752-1688.1998.tb05961.x · Cited by 7,945 · see citing works
Self-citationGenerating surfaces of daily meteorological variables over large regions of complex terrain
THORNTON, P. E., Running, S. W., & White, M. A. (1997) · Journal of Hydrology · doi:10.1016/s0022-1694(96)03128-9 · Cited by 1,388 · see citing works
PRECIPITATION AVERAGES FOR LARGE AREAS
THIESSEN, A. H. (1911) · Monthly Weather Review · doi:10.1175/1520-0493(1911)39 · Cited by 1,204 · see citing works
A knowledge-based approach to the statistical mapping of climate
Daly, C., GIBSON, W. P., Taylor, G. H., Johnson, G., & Pasteris, P. A. (2002) · Climate Research · doi:10.3354/cr022099 · Cited by 1,112 · see citing works
Self-citationGuidelines for assessing the suitability of spatial climate data sets
Daly, C. (2006) · International Journal of Climatology · doi:10.1002/joc.1322 · Cited by 613 · see citing works
Self-citationA refinement of inverse distance weighted interpolation
Watson, D. F., & Philip, G. (1985) · Cited by 606 · see citing works
A COMPARATIVE ANALYSIS OF TECHNIQUES FOR SPATIAL INTERPOLATION OF PRECIPITATION 1
Tabios, G. Q., & Salas, J. D. (1985) · JAWRA Journal of the American Water Resources Association · doi:10.1111/j.1752-1688.1985.tb00147.x · Cited by 551 · see citing works
Determining the Power-Law Wind-Profile Exponent under Near-Neutral Stability Conditions at Sea
Hsu, S. A., Meindl, E. A., & Gilhousen, D. B. (1994) · Journal of applied meteorology · doi:10.1175/1520-0450(1994)033 · Cited by 500 · see citing works
Seaber, P. R., Kapinos, F. P., & Knapp, G. L. (1987) · doi:10.3133/wsp2294 · Cited by 379 · see citing works
Climatologically aided interpolation (CAI) of terrestrial air temperature
Willmott, C. J., & Robeson, S. M. (1995) · International Journal of Climatology · doi:10.1002/joc.3370150207 · Cited by 355 · see citing works
The Prism Approach to Mapping Precipitation and Temperature
Daly, C., & Taylor, G. H. (1998) · Cited by 320 · see citing works
Self-citationHIGH-QUALITY SPATIAL CLIMATE DATA SETS FOR THE UNITED STATES AND BEYOND
Daly, C., Taylor, G. H., Gibson, W. P., Parzybok, T. W., Johnson, G. L., & Pasteris, P. A. (2000) · Transactions of the ASAE · doi:10.13031/2013.3101 · Cited by 290 · see citing works
Self-citationMapping the climate of Puerto Rico, Vieques and Culebra
Daly, C., Helmer, E. H., & Quiñones, M. (2003) · International Journal of Climatology · doi:10.1002/joc.937 · Cited by 290 · see citing works
Self-citationEffect of bias adjustment and rain gauge data quality control on radar rainfall estimation
Steiner, M., Smith, J. A., Burges, S. J., et al. (1999) · Water Resources Research · doi:10.1029/1999wr900142 · Cited by 259 · see citing works
Hamlet, A. F., & Lettenmaier, D. P. (2005) · Journal of Hydrometeorology · doi:10.1175/jhm420.1 · Cited by 255 · see citing works
Canadian historical and homogeneous temperature datasets for climate change analyses
Vincent, L. A., & Gullett, D. W. (1999) · International Journal of Climatology · doi:10.1002/(sici)1097-0088(199910)19:12 · Cited by 228 · see citing works
The length we go: Measuring environmental benefits of conservation practices
Mausbach, M. J., & Dedrick, A. R. (2004) · Cited by 170 · see citing works
Kuligowski, R. J., & Barros, A. P. (1998) · Weather and Forecasting · doi:10.1175/1520-0434(1998)013 · Cited by 164 · see citing works
Mapping of temperature variables in Israel: a comparison of different interpolation methods
Kurtzman, D., & Kadmon, R. (1999) · Climate Research · doi:10.3354/cr013033 · Cited by 147 · see citing works
Spatial variability of daily weather variables in the high plains of the USA
Hubbard, K. G. (1994) · Agricultural and Forest Meteorology · doi:10.1016/0168-1923(94)90067-1 · Cited by 134 · see citing works
Seo, D., Krajewski, W. F., & Bowles, D. S. (1990) · Water Resources Research · doi:10.1029/wr026i003p00469 · Cited by 112 · see citing works
Gridded Area-Averaged Daily Precipitation via Conditional Interpolation
Hewitson, B. C., & Crane, R. G. (2005) · Journal of Climate · doi:10.1175/jcli3246.1 · Cited by 78 · see citing works
MEAN AREAL PRECIPITATION FOR DAILY HYDROLOGIC MODELING IN MOUNTAINOUS REGIONS1
Garen, D. C., Johnson, G. L., & Hanson, C. L. (1994) · JAWRA Journal of the American Water Resources Association · doi:10.1111/j.1752-1688.1994.tb03307.x · Cited by 76 · see citing works
Self-citationRAINFALL CHARACTERISTICS AND SPATIAL CORRELATION FOR THE GEORGIA COASTAL PLAIN
Bosch, D. D., Sheridan, J. M., & Davis, F. M. (1999) · Transactions of the ASAE · doi:10.13031/2013.13330 · Cited by 63 · see citing works
Development of a 103-year high-resolution climate data set for the conterminous United States
Daly, C. (2000) · Asheville Meeting · Cited by 63 · see citing works
Self-citationSimpson, J. J., Hufford, G. L., Daly, C., et al. (2010) · ARCTIC · doi:10.14430/arctic407 · Cited by 55 · see citing works
Self-citationA gridded global data set of daily temperature and precipitation for terrestrial biospheric modeling
Piper, S. C., & Stewart, E. F. (1996) · Global Biogeochemical Cycles · doi:10.1029/96gb01894 · Cited by 54 · see citing works
Infilling Sparse Records of Spatial Fields
Johns, C. J., Nychka, D. W., Kittel, T. G. F., et al. (2003) · Journal of the American Statistical Association · doi:10.1198/016214503000000729 · Cited by 54 · see citing works
Kittel, T. G. F., Rosenbloom, N. A., Royle, J. A., Daly, C., GIBSON, W. P., Fisher, H., THORNTON, P. E., Yates, D., Aulenbach, S. M., Kaufman, C., et al. (2004) · Climate Research · doi:10.3354/cr027151 · Cited by 52 · see citing works
Self-citationKane, R. P. (1999) · International Journal of Climatology · doi:10.1002/(sici)1097-0088(19990330)19:4 · Cited by 51 · see citing works
Forest climatology: estimation and use of daily climatological data for Bavaria, Germany
Xia, Y., Fabian, P., Winterhalter, M., et al. (2001) · Agricultural and Forest Meteorology · doi:10.1016/s0168-1923(00)00210-0 · Cited by 44 · see citing works
1:250,000-scale Hydrologic Units of the United States
Steeves, P. A., & Nebert, D. D. (1994) · Antarctica A Keystone in a Changing World · doi:10.3133/ofr19940236 · Cited by 26 · see citing works
Seo, D. (1990) · Water Resources Research · doi:10.1029/89wr02984 · Cited by 25 · see citing works
Li, X., Christopher, S. A., Chou, J., & Welch, R. M. (2000) · Journal of applied meteorology · doi:10.1175/1520-0450(2001)040 · Cited by 22 · see citing works
Hua, Z. (2003) · Geographical Research · Cited by 13 · see citing works
Plantico, M. S. (2000) · Asheville Meeting · Cited by 9 · see citing works
Carlin (2002) · doi:10.1175/1520-0442(2002)015
Mutai, C. (2000) · doi:10.1175/1520-0442(2000)013
Mutai, C. (1998) · doi:10.1002/(sici)1097-0088(199807)18:9
Latif, M. (1999) · doi:10.1175/1520-0442(1999)012
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Research works printed but not linked: 1
- [13]13. --, W. P. Gibson, M. Doggett, J. Smith, and G. Taylor, 2004: Up-to-date monthly climate maps for the conterminous United States. Preprints, 14th Conf. on Applied Climatology, Seattle, WA, Amer. Meteor. Soc., CD-ROM, P5.
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Obradovich, N., Migliorini, R., Paulus, M. P., & Rahwan, I. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1801528115 · Cited by 492 · see citing works
Climate change may alter human physical activity patterns
Obradovich, N., & Fowler, J. H. (2017) · Nature Human Behaviour · doi:10.1038/s41562-017-0097 · Cited by 221 · see citing works
SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018
Tang, G., Clark, M. P., Newman, A. J., Wood, A. W., Papalexiou, S. M., Vionnet, V., & Whitfield, P. H. (2020) · Earth System Science Data · doi:10.5194/essd-12-2381-2020 · Cited by 82 · see citing works
- Authors
- Di Luzio, M., Johnson, G. L., Daly, C., Eischeid, J. K., & Arnold, J. G.
- Affiliations
- Blackland Research Center, Texas Agricultural Experiment Station, Texas A&M University System, Temple, Texas
- Natural Resources Conservation Service (US)
- Oregon State University (US)
- Cooperative Institute for Research in Environmental Sciences (US)
- Agricultural Research Service (US)
- Venue
- Journal of Applied Meteorology and Climatology
- Publisher
- American Meteorological Society
- Published
- 2008-02-01
- Volume
- 47
- Issue
- 2
- Pages
- 475-497
- ISSN
- 1558-8424 (ISSN-L)
- 1558-8432
- OpenAlex
- W2125772301
- Cited by
- 271
- References
- 44
- Citations by year
- 2026: 7 · 2025: 7 · 2024: 9 · 2023: 12 · 2022: 17 · 2021: 14 · 2020: 18 · 2019: 22 · 2018: 17 · 2017: 23 · 2016: 22 · 2015: 11 · 2014: 20 · 2013: 15 · 2012: 15
- FWCI
- 8.45
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Applied Meteorology and Climatology (publishedVersion)
- Funders
- U.S. Department of Agriculture
- Agricultural Research Service
- Natural Resources Conservation Service
- Topics
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Precipitation Measurement and Analysis
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
- Physical Sciences › Environmental Science › Environmental Engineering › Soil Geostatistics and Mapping
- Keywords
- daily precipitation, daily temperature, gridded climate data, PRISM, orographic effects, contiguous United States, historical climate data
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