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| Created: | Mar 15, 2019 at 4:47 p.m. | |
| Last updated: | Apr 04, 2019 at 11:44 a.m. | |
| Citation: | See how to cite this resource |
| Sharing Status: | Public |
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Abstract
Hydrological and meteorological information can help inform the conditions and risk factors related to the environment and their inhabitants. Due to the limitations of observation sampling, gridded data sets provide the modeled information for areas where data collection are infeasible using observations collected and known process relations. Although available, data users are faced with barriers to use, challenges like how to access, acquire, then analyze data for small watershed areas, when these datasets were produced for large, continental scale processes. In this tutorial, we introduce Observatory for Gridded Hydrometeorology (OGH) to resolve such hurdles in a use-case that incorporates NetCDF gridded data sets processes developed to interpret the findings and apply secondary modeling frameworks (landlab).
LEARNING OBJECTIVES
- Familiarize with data management, metadata management, and analyses with gridded data
- Inspecting and problem solving with Python libraries
- Explore data architecture and processes
- Learn about OGH Python Library
- Discuss conceptual data engineering and science operations
Use-case operations:
1. Prepare computing environment
2. Get list of grid cells
3. NetCDF retrieval and clipping to a spatial extent
4. Extract NetCDF metadata and convert NetCDFs to 1D ASCII time-series files
5. Visualize the average monthly total precipitations
6. Apply summary values as modeling inputs
7. Visualize modeling outputs
8. Save results in a new HydroShare resource
For inquiries, issues, or contribute to the developments, please refer to https://github.com/freshwater-initiative/Observatory
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This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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