edu.ucar.opensky::articles:23703 UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact 2025-12-25T06:44:07.583624 ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) https://n2t.org/ark:/85065/d7ks6vtq https://doi.org/10.5281/zenodo.3735533 https Related Dataset #1 SCDNA: a serially complete precipitation and temperature dataset in North America from 1979 to 2018 (Version 1.1) SCDNA: A serially complete precipitation and temperature dataset for North America from 1979 to 2018 2020-10-02T00:00:00Z publication https://n2t.org/ark:/85065/d7ks6vtq https://doi.org/10.5194/essd-12-2381-2020 Tang, G. author Clark, M. P. author Newman, Andrew J. author Wood, Andrew author Papalexiou, S. M. author Vionnet, V. author Whitfield, P. H. author UCAR/NCAR - Library publisher Station-based serially complete datasets (SCDs) of precipitation and temperature observations are important for hydrometeorological studies. Motivated by the lack of serially complete station observations for North America, this study seeks to develop an SCD from 1979 to 2018 from station data. The new SCD for North America (SCDNA) includes daily precipitation, minimum temperature (T-min), and maximum temperature (T-max) data for 27 276 stations. Raw meteorological station data were obtained from the Global Historical Climate Network Daily (GHCN-D), the Global Surface Summary of the Day (GSOD), Environment and Climate Change Canada (ECCC), and a compiled station database in Mexico. Stations with at least 8-year-long records were selected, which underwent location correction and were subjected to strict quality control. Outputs from three reanalysis products (ERA5, JRA-55, and MERRA-2) provided auxiliary information to estimate station records. Infilling during the observation period and reconstruction beyond the observation period were accomplished by combining estimates from 16 strategies (variants of quantile mapping, spatial interpolation, and machine learning). A sensitivity experiment was conducted by assuming that 30% of observations from stations were missing - this enabled independent validation and provided a reference for reconstruction. Quantile mapping and mean value corrections were applied to the final estimates. The median Kling-Gupta efficiency (KGE') values of the final SCDNA for all stations are 0.90, 0.98, and 0.99 for precipitation, T-min, and T-max, respectively. The SCDNA is closer to station observations than the four benchmark gridded products and can be used in applications that require either quality-controlled meteorological station observations or reconstructed long-term estimates for analysis and modeling. The dataset is available at https://doi.org/10.5281/zenodo.3735533 (Tang et al., 2020). UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact PDF Text theme Resource Type 2016-01-01T00:00:00Z publication Copyright author(s). This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. None eng geoscientificInformation