Radar reflectivity analysis using GIS

Hari Kandel
Lake Superior State University, School of Natural Resources and the Environment

This activity is part of the On the Cutting Edge Peer Reviewed Teaching Activities collection. (view details)

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Scientific Accuracy
Alignment of Learning Goals, Activities, and Assessments
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Initial Publication Date: May 15, 2019 | Reviewed: December 10, 2020 (see revision history: 2 events)

Summary

This exercise is about visualizing and analyzing radar reflectivity data for a hurricane event using GIS. Radar, an acronym for radio detection and ranging, is an active remote sensor that uses radio waves to determine the range, altitude, direction of movement, and speed of objects. NEXRAD (Next Generation Weather Radar) system currently collects data at 160 sites throughout the United States. Students will visualize and export Level-II base reflectivity product from one of the NEXRAD sites: Wilmington, NC for the date of September 14, 2018, the date Hurricane Florence hit the state.

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Learning Goals

Explore where and how to access NEXRAD data,
Describe the key characteristics of radar data,
Visualize selected sweep of radar reflectivity using a NOAA Weather and Climate Toolkit
Export the reflectivity data into a format readable by ArcGIS and calculate rain rate.
Evaluate the accuracy of the radar-derived rain rate and sources of uncertainty associated with it.

Context for Use

This exercise is designed as a spatial analysis laboratory activity for a GIS course in which students are in their second or third GIS course in the college. Students with background knowledge on weather and radar, and introductory GIS skills would easily excel in this activity. This exercise can be customized to analyze any large scale extreme weather event from any other location.

Description and Teaching Materials

Radar reflectivity-rain rate analysis for a hurricane in GIS (Microsoft Word 2007 (.docx) 104kB May2 19)

Teaching Notes and Tips

Teaching tips are provided on the uploaded file.

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Assessment

Students' learning will be assessed using their responses on questions as they progress through the exercise and finally using the map they prepare in ArcMap.