Previous Posts
- Week 8: Simple Solutions Work Better
- Week 7: Understanding the Data Behind the Display
- Week 6: Building Without Breaking
- Week 5: Correct Integration Saves Time
- Week 4: Data Integration Is More Complex Than It Looks
- Week 3: Turning Data Into Decisions
- Week 2: Making Data Actionable
- Week 1: Building Knowledge Before Making Changes
Goal
Continue refining the surface analysis layers by extracting fronts, troughs, and pressure systems, improving the appearance of the extracted symbols, and working toward correctly aligning the surface analysis and surface progs with the map. Continue testing the dashboard during real-world severe weather events. Continue working through layers that have not yet been completed.
Workflow
I used Cursor AI to help refine the extraction of surface-analysis features from NOAA data. My workflow consisted of testing the extracted data against the official surface analysis, making adjustments to improve the display of fronts, troughs, and pressure systems, and repeatedly testing the layers within the dashboard. I also worked on aligning the extracted surface analysis and surface progs with the MapLibre map projection, comparing the results to the official products after each change. After unsuccessfully being able to align the surface data myself, I resorted to ChatGPT to help solve the problem. During severe weather, I continued using the dashboard in real time to verify that the existing tools and layers were functioning properly.
Prompts Used
ChatGPT
- Help with more in-depth prompts for troubleshooting.
- Help refine what is actually necessary and what is excessive to eliminate clutter.
Cursor
- Refine the extraction of fronts, troughs, and pressure systems from the surface analysis data.
- Replace simple lines and points with the proper meteorological symbols for each feature.
- Align the extracted surface analysis and surface progs with the MapLibre map projection.
- Troubleshoot layer alignment and projection issues without affecting existing dashboard functionality.
What Worked
I successfully extracted fronts, troughs, and pressure systems. I improved their display by replacing basic lines and points with the appropriate meteorological symbols. Comparing the extracted features with the official surface analysis showed they were very accurate. I also continued testing the dashboard during severe weather, and the existing tools and layers performed well.
What Didn’t Work
I am still having trouble getting the surface analysis chart and surface progs to align correctly with the MapLibre map. While the data is being extracted correctly, the projection and positioning still need additional work before the layers can be considered fully functional.
Lessons Learned
This week I realized how important map projections and coordinate systems are when working with geospatial weather data. Even when the extracted meteorological data is accurate, displaying it correctly on an interactive map can be one of the most challenging parts of the development process. I also learned the value of validating extracted data against official NOAA products throughout the development process. Furthermore, I realized that even though Cursor AI already aligned other meteorological data (CAPE, Tornado parameter, etc), it can still struggle to align other important data.
Images/Video
Image showing the extracted surface features
Image showing High and Low symbols accurately
Image showing continued misaligned surface prog