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This resource does not have an owner who is an active HydroShare user. Contact CUAHSI (help@cuahsi.org) for information on this resource. |
| Type: | Resource | |
| Storage: | The size of this resource is 9.4 MB | |
| Created: | Jun 27, 2024 at 1:47 p.m. | |
| Last updated: | Jun 27, 2024 at 1:59 p.m. | |
| Citation: | See how to cite this resource | |
| Content types: | Geographic Feature Content |
| Sharing Status: | Public |
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| Views: | 175 |
| Downloads: | 8 |
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Abstract
This resource enables users to retrieve Multi-Radar Multi-Sensor (MRMS) rainfall data for selected locations based on a given shapefile. The included Jupyter Notebook guides users to perform geospatial analysis of the region of interest and retrieve data accordingly. It facilitates the visualization of rainfall data over specified areas and time periods. This tool supports environmental studies, urban planning, and any field where precise weather data analysis is crucial. The resource is designed to be user-friendly, accommodating users with varying levels of technical expertise in handling and analyzing geospatial data. This resource was developed as part of the activities for developing low-cost rainfall sensors under the CUAHSI INSTRUMENTATION DISCOVERY TRAVEL GRANT. It serves as a supportive tool for validating and calibrating rainfall measurements obtained from these sensors.
Subject Keywords
Coverage
Spatial
Content
Readme.txt
Retrieving and Visualizing MRMS Rainfall Data for Selected Locations and Time Periods This HydroShare resource was developed during the INSTRUMENTATION DISCOVERY TRAVEL GRANT and is designed to support users in retrieving and visualizing Multi-Radar Multi-Sensor (MRMS) rainfall data. This resource is particularly useful for hydrologists, meteorologists, and researchers interested in analyzing rainfall patterns over specific areas and timeframes. Required Libraries: To effectively use the Jupyter notebook included in this resource, the following Python libraries need to be installed: xarray (xr): Essential for handling multi-dimensional datasets and facilitating operations on such data, especially suited for large-scale geospatial data. datetime: Provides classes for manipulating dates and times in Python, enabling both basic and complex temporal calculations. timedelta: Critical for performing arithmetic on datetime objects to manage time intervals effectively. glob: Useful for file handling; it allows searching through directories to find files matching a specified pattern, aiding in batch processing. os: Provides a method to use operating system dependent functionality such as file reading/writing, and managing directories. geopandas (gpd): Extends the data types provided by pandas to include spatial operations on geometric types, which is crucial for handling and analyzing geospatial data. Data Source: The MRMS rainfall data utilized in this resource is obtained from the following URL: https://mtarchive.geol.iastate.edu This URL serves as a primary source for downloading historical MRMS data, which is essential for the analyses performed in the included Jupyter notebook.
Data Services
Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
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| CUAHSI | IDGT |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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