Melissa Kenney
University of Minnesota
| Subject Areas: | Water quality,Transdisciplinary analysis of water issues,social sciences,decision sciences,water management,REU |
Recent Activity
ABSTRACT:
The purpose of this resource is to demonstrate how the CUAHSI JupyterHub platform can be used to perform basic hydrologic data analysis. Temperature data was collected from the NOAA Global Historical Climatology network for two sites in the greater Seattle area. These data are organized using Python classes, and plotted in various ways to demonstrate common data analysis steps.
For more information about the GHCN data included in this resource, see; https://docs.opendata.aws/noaa-ghcn-pds/readme.html
ABSTRACT:
The purpose of this resource is to demonstrate how the CUAHSI JupyterHub platform can be used to perform basic hydrologic data analysis. Temperature data was collected from the NOAA Global Historical Climatology network for two sites in the greater Seattle area. These data are organized using Python classes, and plotted in various ways to demonstrate common data analysis steps.
For more information about the GHCN data included in this resource, see; https://docs.opendata.aws/noaa-ghcn-pds/readme.html
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Created: April 14, 2025, 7:32 p.m.
Authors: Cogswell, Clara · Anthony Michael Castronova
ABSTRACT:
The purpose of this resource is to demonstrate how the CUAHSI JupyterHub platform can be used to perform basic hydrologic data analysis. Temperature data was collected from the NOAA Global Historical Climatology network for two sites in the greater Seattle area. These data are organized using Python classes, and plotted in various ways to demonstrate common data analysis steps.
For more information about the GHCN data included in this resource, see; https://docs.opendata.aws/noaa-ghcn-pds/readme.html
Created: April 25, 2025, 2:40 p.m.
Authors: Kenney, Melissa · Anthony Michael Castronova
ABSTRACT:
The purpose of this resource is to demonstrate how the CUAHSI JupyterHub platform can be used to perform basic hydrologic data analysis. Temperature data was collected from the NOAA Global Historical Climatology network for two sites in the greater Seattle area. These data are organized using Python classes, and plotted in various ways to demonstrate common data analysis steps.
For more information about the GHCN data included in this resource, see; https://docs.opendata.aws/noaa-ghcn-pds/readme.html