Gridded 1/24-degree daily climate for the western United States, 1951–2025
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This dataset contains daily grids of climate for the 75-year period from January 1 1951 to December 31 2025 at a geographic resolution of 1/24° (~4 km) across the western US domain, defined as the 11 westernmost states in the coterminous US (Arizona, California, Colorado, Idaho, Montana, New Mexico, Nevada, Oregon, Utah, Washington, and Wyoming). Climate variables represented are precipitation total (prec, mm), daily maximum temperature (tmax, °C), daily minimum temperature (tmin, °C), mean vapor pressure (ea, hPa), mean wind velocity two meters above the surface (wind, m s-1), mean downward solar radiation at the surface (solar, W m-2), mean downward longwave radiation at the surface (longwave, W m-2), and surface pressure (psfc, hPa). The dataset was produced in order to make possible long simulations of western US environmental processes such as hydrology, wildfire, and ecosystem dynamics. The dataset was produced by combining data from a variety of publicly available sources, prioritizing station-based observational datasets (as opposed to reanalysis datasets) when possible. This the methods used to produce this dataset will be described in an upcoming publication and a link to that publication will be added to the dataset's metadata at that time. Data sources for each variable: prec, tmax, and tmin: nClimGrid-Daily: https://www.ncei.noaa.gov/products/land-based-station/nclimgrid-daily ea: from PRISM daily 4-km dewpoint from 1980–end: https://prism.oregonstate.edu/ ea: from Rahimi et al. (2024) WUS-D3 9-km dynamically downscaled ERA5 daily specific humidity and vapor pressure and then calibrated using quantile mapping to match monthly climatologies of daily distributions of PRISM-derived ea during 1981–2020. https://wrf-cmip6-noversioning.s3.amazonaws.com/index.html#downscaled_products/reanalysis/era5/postprocess/d02/ wind: WTK-LED 4-km dynamically-downscaled ERA5 wind reanalysis for 2001–2020: https://doi.org/10.25984/2504176 wind: from Rahimi et al. (2024) WUS-D3 9-km dynamically downscaled ERA5 for 1951–2000 and 2021–March 2025, calibrated to WTI-LED winds using quantile mapping to match monthly climatologies of daily distributions during 2001–2020.wind: from GridMET for April 2025–end, calibrated to WTI-LED winds using quantile mapping to match monthly climatologies of daily distributions during 2001–2020: https://www.climatologylab.org/gridmet.html solar: from PRISM daily 4-km downward solar radiation incident on the sloped surface from 1980–end. solar: from Rahimi et al. (2024) WUS-D3 9-km dynamically downscaled ERA5 for 1951–1979, calibrated using quantile mapping to match monthly climatologies of daily distributions of PRISM-derived solar during 1981–2020. longwave: from Rahimi et al. (2024) WUS-D3 9-km dynamically downscaled ERA5 for 1951–March 2025. longwave: extended from April 2025–end with NLDAS2, calibrated using quantile mapping to match monthly climatologies of daily distributions of the dynamically downscaled ERA5 data during 1981–2020: https://hydro1.gesdisc.eosdis.nasa.gov/data/NLDAS/NLDAS_FORA0125_H.2.0/ ps: from Rahimi et al. (2024) WUS-D3 9-km dynamically downscaled ERA5 for 1951–March 2025. 9-km gridded data downscaled to 4-km with bilinear interpolation and then each 4-km grid’s dataset was multiplicatively adjusted so that 1981–2020 means matched the long-term mean surface pressure (pmean; hPa) expected from 4-km gridded mean elevation (in m above sea level pmean = 1013.25 * (1 - (2.2557*10^-5) * elev).^5.25588. ps: extended from April 2025–end with NLDAS2, calibrated using quantile mapping to match monthly climatologies of daily distributions of the dynamically downscaled ERA5 data during 1981–2020. References nClimGrid-Daily: Durre, I., Arguez, A., Schreck III, C. J., Squires, M. F., and Vose, R. S.: Daily high-resolution temperature and precipitation fields for the contiguous United States from 1951 to present, Journal of Atmospheric and Oceanic Technology, 39, 1837–1855, https://doi.org/10.1175/JTECH-D-22-0024.1, 2022. GridMET Abatzoglou, J. T.: Development of gridded surface meteorological data for ecological applications and modelling, Int J Climatol, 33, 121–131, https://doi.org/10.1002/joc.3413, 2013. PRISM Daly, C., Doggett, M. K., Smith, J. I., Olson, K. V., Halbleib, M. D., Dimcovic, Z., Keon, D., Loiselle, R. A., Steinberg, B., and Ryan, A. D.: Challenges in observation-based mapping of daily precipitation across the conterminous United States, Journal of Atmospheric and Oceanic Technology, 38, 1979–1992, https://doi.org/10.1175/JTECH-D-21-0054.1, 2021. Rupp, D. E., Daly, C., Doggett, M. K., Smith, J. I., and Steinberg, B.: Mapping an observation-based global solar irradiance climatology across the conterminous United States, Journal of Applied Meteorology and Climatology, 61, 857–876, https://doi.org/10.1175/JAMC-D-21-0236.1, 2022. WUS-D3 9-km dynamically downscaled ERA5 reanalysis Rahimi, S., Huang, L., Norris, J., Hall, A., Goldenson, N., Krantz, W., Bass, B., Thackeray, C., Lin, H., and Chen, D.: An overview of the Western United States Dynamically Downscaled Dataset (WUS-D3), Geoscientific Model Development, 17, 2265–2286, https://doi.org/10.5194/gmd-17-2265-2024, 2024. WTK-LED wind reanalysis Draxl, C., Wang, J., Sheridan, L., Jung, C., Bodini, N., Buckhold, S., Aghili, C. T., Peco, K., Kotamarthi, R., Kumler, A., Phillips, C., Purkayastha, A., Young, E., Rosenlieb, E., and Heidi, T.: WTK-LED: The wind toolkit long-term ensemble dataset, National Renewable Energy Laboratory (NREL), Golden, CO, https://doi.org/10.2172/2473210, 2024. NLDAS2 Xia, Y., Mitchell, K., Ek, M., Sheffield, J., Cosgrove, B., Wood, E., Luo, L., Alonge, C., Wei, H., Meng, J., Linvneh, B., Lettenmaier, D., Koren, V., Duan, Q., Mo, K., Fan, Y., and Mocko, D.: 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-Atmos, 117, D03109, https://doi.org/10.1029/2011JD016051, 2012.
本数据集包含1951年1月1日至2025年12月31日共计75年的逐日气候格点数据，地理分辨率为1/24°（约4公里），覆盖美国西部区域——即美国本土48州最西部的11个州：亚利桑那州、加利福尼亚州、科罗拉多州、爱达荷州、蒙大拿州、新墨西哥州、内华达州、俄勒冈州、犹他州、华盛顿州与怀俄明州。所涵盖的气候变量包括：降水量（prec，单位：毫米）、日最高气温（tmax，单位：摄氏度）、日最低气温（tmin，单位：摄氏度）、平均水汽压（ea，单位：百帕）、地表以上2米处平均风速（wind，单位：米每秒）、地表向下平均太阳短波辐射（solar，单位：瓦每平方米）、地表向下平均长波辐射（longwave，单位：瓦每平方米），以及地表气压（psfc，单位：百帕）。 本数据集的制作旨在支持美国西部水文、野火、生态系统动态等环境过程的长期模拟。数据集通过整合多种公开可用数据源构建而成，在可行条件下优先选用基于台站的观测数据集（而非再分析数据集）。 本数据集的制作方法将在即将发表的论文中详细阐述，届时会将该论文的链接添加至数据集元数据中。 ### 各变量数据源 降水量（prec）、日最高气温（tmax）与日最低气温（tmin）：数据来源于nClimGrid-Daily，访问链接：https://www.ncei.noaa.gov/products/land-based-station/nclimgrid-daily 平均水汽压（ea）：1980年至今的数据来源于PRISM每日4公里露点数据集，访问链接：https://prism.oregonstate.edu/；1951年至1979年的数据来源于Rahimi等人（2024）发布的WUS-D3 9公里动力降尺度ERA5逐日比湿度与水汽压数据集，并通过分位数映射校准，以匹配1981-2020年PRISM衍生ea的日分布月气候态，访问链接：https://wrf-cmip6-noversioning.s3.amazonaws.com/index.html#downscaled_products/reanalysis/era5/postprocess/d02/ 平均风速（wind）：2001-2020年的数据来源于WTK-LED 4公里动力降尺度ERA5风速再分析数据集，访问链接：https://doi.org/10.25984/2504176；1951-2000年与2021年至2025年3月的数据来源于Rahimi等人（2024）发布的WUS-D3 9公里动力降尺度ERA5数据集，并通过分位数映射校准，以匹配2001-2020年WTK-LED风速的日分布月气候态；2025年4月至今的数据来源于GridMET，并通过分位数映射校准至WTK-LED风速的同期月气候态，访问链接：https://www.climatologylab.org/gridmet.html 地表向下太阳短波辐射（solar）：1980年至今的数据来源于PRISM每日4公里倾斜面入射向下太阳辐射数据集；1951-1979年的数据来源于Rahimi等人（2024）发布的WUS-D3 9公里动力降尺度ERA5数据集，并通过分位数映射校准，以匹配1981-2020年PRISM衍生太阳辐射的日分布月气候态。 地表向下长波辐射（longwave）：1951年至2025年3月的数据来源于Rahimi等人（2024）发布的WUS-D3 9公里动力降尺度ERA5数据集；2025年4月至今的数据通过NLDAS2扩展得到，并通过分位数映射校准，以匹配1981-2020年动力降尺度ERA5数据的日分布月气候态，访问链接：https://hydro1.gesdisc.eosdis.nasa.gov/data/NLDAS/NLDAS_FORA0125_H.2.0/ 地表气压（psfc）：1951年至2025年3月的数据来源于Rahimi等人（2024）发布的WUS-D3 9公里动力降尺度ERA5数据集，先通过双线性插值降尺度至4公里分辨率，随后对每个4公里格点数据进行乘法调整，使得1981-2020年的平均值与基于4公里格点平均海拔（单位：米）计算的长期地表气压均值（pmean，单位：百帕）一致，计算公式为：$pmean = 1013.25 imes (1 - 2.2557 imes 10^{-5} imes 海拔)^{5.25588}$；2025年4月至今的数据通过NLDAS2扩展得到，并通过分位数映射校准，以匹配1981-2020年动力降尺度ERA5数据的日分布月气候态。 ### 参考文献 1. nClimGrid-Daily： Durre, I., Arguez, A., Schreck III, C. J., Squires, M. F., and Vose, R. S.: Daily high-resolution temperature and precipitation fields for the contiguous United States from 1951 to present, Journal of Atmospheric and Oceanic Technology, 39, 1837–1855, https://doi.org/10.1175/JTECH-D-22-0024.1, 2022. 2. GridMET： Abatzoglou, J. T.: Development of gridded surface meteorological data for ecological applications and modelling, Int J Climatol, 33, 121–131, https://doi.org/10.1002/joc.3413, 2013. 3. PRISM： Daly, C., Doggett, M. K., Smith, J. I., Olson, K. V., Halbleib, M. D., Dimcovic, Z., Keon, D., Loiselle, R. A., Steinberg, B., and Ryan, A. D.: Challenges in observation-based mapping of daily precipitation across the conterminous United States, Journal of Atmospheric and Oceanic Technology, 38, 1979–1992, https://doi.org/10.1175/JTECH-D-21-0054.1, 2021. 4. Rupp, D. E., Daly, C., Doggett, M. K., Smith, J. I., and Steinberg, B.: Mapping an observation-based global solar irradiance climatology across the conterminous United States, Journal of Applied Meteorology and Climatology, 61, 857–876, https://doi.org/10.1175/JAMC-D-21-0236.1, 2022. 5. WUS-D3 9-km dynamically downscaled ERA5 reanalysis： Rahimi, S., Huang, L., Norris, J., Hall, A., Goldenson, N., Krantz, W., Bass, B., Thackeray, C., Lin, H., and Chen, D.: An overview of the Western United States Dynamically Downscaled Dataset (WUS-D3), Geoscientific Model Development, 17, 2265–2286, https://doi.org/10.5194/gmd-17-2265-2024, 2024. 6. WTK-LED wind reanalysis： Draxl, C., Wang, J., Sheridan, L., Jung, C., Bodini, N., Buckhold, S., Aghili, C. T., Peco, K., Kotamarthi, R., Kumler, A., Phillips, C., Purkayastha, A., Young, E., Rosenlieb, E., and Heidi, T.: WTK-LED: The wind toolkit long-term ensemble dataset, National Renewable Energy Laboratory (NREL), Golden, CO, https://doi.org/10.2172/2473210, 2024. 7. NLDAS2： Xia, Y., Mitchell, K., Ek, M., Sheffield, J., Cosgrove, B., Wood, E., Luo, L., Alonge, C., Wei, H., Meng, J., Linvneh, B., Lettenmaier, D., Koren, V., Duan, Q., Mo, K., Fan, Y., and Mocko, D.: 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-Atmos, 117, D03109, https://doi.org/10.1029/2011JD016051, 2012.
