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Webinar: Toward Analysis Ready Data—Programmatically Discover, Access, and Subset Daymet V4 Data with Python and ArcGIS

Speakers: Michele Thornton
Contributors: Rupesh Shrestha and Lain Graham

Hosted by: NASA EOSDIS and the ORNL DAAC
Date: August 31, 2021
Contact for the ORNL DAAC: uso@daac.ornl.gov

Keywords: Daymet, netCDF, Python, OPeNDAP, Web Service, Pydap, Xarray, Geospatial

Overview

The Daymet dataset available at NASA's Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC) provides long-term, continuous, gridded estimates of daily weather and climatology variables across North America. In the webinar, we introduced participants to the recently released Daymet Version 4 Data Collection and provided information on how to access NASA data holdings to programmatically discover and subset Daymet data to a region and time period of interest.

Researchers often require data such as Daymet that are formatted in multidimensional scientific formats but are not familiar with access methods that can allow them to programmatically search and discover as well as subset and download those data based on their own search parameters such as a regional and temporal area-of-interest. Demonstrated using NASA metadata API’s and Python Geospatial programming techniques, we provide examples using Jupyter Notebooks that search, subset, and download Daymet V4 data. We also demonstrate the use of Python Geospatial libraries such as Xarray to create climatological data. Thanks to a collaboration between the NASA DAACs and Esri, we also demonstrate an example ArcGIS Notebook workflow that perform spatial analysis using multidimensional tools with Daymet data in the ArcGIS environment.

Webinar LinkYouTube

Presentation SlidesPDF

Dataset

Thornton, M.M., R. Shrestha, Y. Wei, P.E. Thornton, S. Kao, and B.E. Wilson. 2020. Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 4. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1840

Thornton, M.M., R. Shrestha, Y. Wei, P.E. Thornton, S. Kao, and B.E. Wilson. 2020. Daymet: Annual Climate Summaries on a 1-km Grid for North America, Version 4. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1852

Thornton, M.M., R. Shrestha, Y. Wei, P.E. Thornton, S. Kao, and B.E. Wilson. 2020. Daymet: Monthly Climate Summaries on a 1-km Grid for North America, Version 4. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1855

Thornton, M.M., R. Shrestha, P.E. Thornton, S. Kao, Y. Wei, and B.E. Wilson. 2021. Daymet Version 4 Monthly Latency: Daily Surface Weather Data. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1904

Tutorial

Toward Analysis Ready Data—Programmatically Discover, Access, and Subset Daymet V4 Data with Python and ArcGIS


Related Learning Resources

More tutorials related to ORNL DAAC data and web services can be found on the ORNL DAAC's Learning page.