Description

Historical records of snow observations are critical for civil engineering, climate modeling, and risk assessment. While a robust dataset of annual maximum snow loads has been developed for the conterminous United States, a similar dataset does not exist for areas of interest in areas outside of the conterminous United States (OCONUS). This dataset contains over 280,000 annual maximum SWE values across over 7,400 OCONUS stations, both from the Global Historical Climatology-Daily (GHCND) network provided by the National Oceanic and Atmospheric Administration (NOAA) and from country-specific networks of weather stations from international meteorological agencies. This dataset will be valuable for civil engineers, climate scientists, and data scientists for applications including developing engineering snow design loads for international sites and supplementing and validating remote sensing observations of snow depth. By substantially expanding access to global coverage of extreme snow events, this work provides a foundation for future research and engineering standards in snow-prone regions worldwide.

Author ORCID Identifier

https://orcid.org/0000-0002-2853-0455

Contributors

Nicholas Brimhall Utah State University 3900 Old Main Hill Logan, UT 84322 a02386758@usu.edu ORCid: 0009-0008-7410-0166 Marc Maguire University of Nebraska - Lincoln 1110 S. 67th Street Omaha, NE 68182 marc.maguire@unl.edu Bikram Bhusal University of Nebraska - Lincoln 1110 S. 67th Street Omaha, NE 68182 bbhusal2@huskers.unl.edu Maha Moussa Utah State University 3900 Old Main Hill Logan, UT 84322 maha.moussa@usu.edu

Document Type

Dataset

DCMI Type

Dataset

File Format

.gz, .r, .csv, .txt

Viewing Instructions

To download the country-specific datasets, run the scripts in each country-level directory in the snoconus.data R project. Since the meteorological agency websites used to download the data may have changed since publication, this may require modifying the provided code and/or acquiring additional credentials from the above agencies. Also, not all data used in this project may be available, and data for additional years and stations mat be available as provided by the meteorological agencies. To extract the annual maximum snow loads, run the R files in the scripts directory of snoconus.maxes, given that the data sources specified in snoconus.data and the GHNCD archive have been downloaded.

Publication Date

7-30-2025

Funder

National Institute of Building Sciences

Publisher

Utah State University

Language

eng

Disciplines

Civil and Environmental Engineering

License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Additional Files

snoconus_readme.txt (10 kB)

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Research Organization Registry Funder ID

https://ror.org/05spz0x25