Articles | Volume 12, issue 4
https://doi.org/10.5194/essd-12-2381-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-12-2381-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
02 Oct 2020
Data description paper |
| 02 Oct 2020
| 02 Oct 2020
SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018
University of Saskatchewan Coldwater Lab, Canmore, Alberta, Canada
Centre for Hydrology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
Martyn P. Clark
University of Saskatchewan Coldwater Lab, Canmore, Alberta, Canada
Centre for Hydrology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
Andrew J. Newman
National Center for Atmospheric Research, Boulder, CO, USA
Andrew W. Wood
National Center for Atmospheric Research, Boulder, CO, USA
Simon Michael Papalexiou
Centre for Hydrology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
Department of Civil, Geological and Environmental Engineering, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
Vincent Vionnet
Environmental Numerical Research Prediction, Environment and Climate Change Canada, Dorval, Quebec, Canada
Paul H. Whitfield
University of Saskatchewan Coldwater Lab, Canmore, Alberta, Canada
Centre for Hydrology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
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33 citations as recorded by crossref.
- Probabilistic Evaluation of Drought in CMIP6 Simulations S. Papalexiou et al. 10.1029/2021EF002150
- GPRChinaTemp1km: a high-resolution monthly air temperature data set for China (1951–2020) based on machine learning Q. He et al. 10.5194/essd-14-3273-2022
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- SC-Earth: A Station-Based Serially Complete Earth Dataset from 1950 to 2019 G. Tang et al. 10.1175/JCLI-D-21-0067.1
- Selecting the optimal fine-scale historical climate data for assessing current and future hydrological conditions 10.1175/JHM-D-21-0045.1
- Unravelling the potential of global streamflow reanalysis in characterizing local flow regime T. Zhao et al. 10.1016/j.scitotenv.2022.156125
- Exacerbated heat in large Canadian cities C. Rajulapati et al. 10.1016/j.uclim.2022.101097
- Improved historical reconstruction of daily flows and annual maxima in gauged and ungauged basins J. Martel et al. 10.1016/j.jhydrol.2023.129777
- EMDNA: an Ensemble Meteorological Dataset for North America G. Tang et al. 10.5194/essd-13-3337-2021
- Employing higher density lower reliability weather data from the Global Historical Climatology Network monitors to generate serially complete weather data for watershed modelling R. Garna et al. 10.1002/hyp.15013
- Hydrologic Model Sensitivity to Temporal Aggregation of Meteorological Forcing Data: A Case Study for the Contiguous United States A. Van Beusekom et al. 10.1175/JHM-D-21-0111.1
- HRLT: a high-resolution (1 d, 1 km) and long-term (1961–2019) gridded dataset for surface temperature and precipitation across China R. Qin et al. 10.5194/essd-14-4793-2022
- EASYMORE: A Python package to streamline the remapping of variables for Earth System models S. Gharari et al. 10.1016/j.softx.2023.101547
- Understanding the Information Content in the Hierarchy of Model Development Decisions: Learning From Data S. Gharari et al. 10.1029/2020WR027948
- Can GPM IMERG Capture Extreme Precipitation in North China Plain? D. Zhang et al. 10.3390/rs14040928
- On the Use of Reanalysis Data to Reconstruct Missing Observed Daily Temperatures in Europe over a Lengthy Period of Time K. Varotsos et al. 10.3390/su15097081
- Trends in seasonal precipitation extremes and associated temperatures along continental Chile M. Lagos-Zúñiga et al. 10.1007/s00382-024-07127-z
- An ERA‐5 Derived CONUS‐Wide High‐Resolution Precipitation Dataset Based on a Refined Parametric Statistical Downscaling Framework S. Emmanouil et al. 10.1029/2020WR029548
- Where Can IMERG Provide a Better Precipitation Estimate than Interpolated Gauge Data? S. Hartke & D. Wright 10.3390/rs14215563
- Filling Gaps in Daily Precipitation Series Using Regression and Machine Learning in Inter-Andean Watersheds M. Portuguez-Maurtua et al. 10.3390/w14111799
- The potential and uncertainty of triple collocation in assessing satellite precipitation products in Central Asia X. Lu et al. 10.1016/j.atmosres.2021.105452
- GPEP v1.0: the Geospatial Probabilistic Estimation Package to support Earth science applications G. Tang et al. 10.5194/gmd-17-1153-2024
- Characterizing the responses of local floods to changing climate in three different hydroclimatic regions across the United States M. Shen & T. Chui 10.1016/j.advwatres.2021.103885
- PatagoniaMet: A multi-source hydrometeorological dataset for Western Patagonia R. Aguayo et al. 10.1038/s41597-023-02828-2
- Large‐Domain Multisite Precipitation Generation: Operational Blueprint and Demonstration for 1,000 Sites S. Papalexiou et al. 10.1029/2022WR034094
- Dataset of daily near-surface air temperature in China from 1979 to 2018 S. Fang et al. 10.5194/essd-14-1413-2022
- Canadian Continental-Scale Hydrology under a Changing Climate: A Review T. Stadnyk & S. Déry 10.3390/w13070906
- Evaluation of Seven Gap-Filling Techniques for Daily Station-Based Rainfall Datasets in South Ethiopia A. Chinasho et al. 10.1155/2021/9657460
- Towards a coherent flood forecasting framework for Canada: Local to global implications L. Arnal et al. 10.1111/jfr3.12895
- Completion of the Central Italy daily precipitation instrumental data series from 1951 to 2019 G. Abouzied et al. 10.1002/gdj3.267
- Spatio-Temporal Variation Characteristics of Snow Depth and Snow Cover Days over the Tibetan Plateau C. Zhang et al. 10.3390/w13030307
- Precipitation Bias Correction: A Novel Semi‐parametric Quantile Mapping Method C. Rajulapati & S. Papalexiou 10.1029/2023EA002823
- FROSTBYTE: a reproducible data-driven workflow for probabilistic seasonal streamflow forecasting in snow-fed river basins across North America L. Arnal et al. 10.5194/hess-28-4127-2024
Latest update: 18 Nov 2024
Short summary
Station observations are critical for hydrological and meteorological studies, but they often contain missing values and have short measurement periods. This study developed a serially complete dataset for North America (SCDNA) from 1979 to 2018 for 27 276 precipitation and temperature stations. SCDNA is built on multiple data sources and infilling/reconstruction strategies to achieve high-quality estimates which can be used for a variety of applications.
Station observations are critical for hydrological and meteorological studies, but they often...
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