📘 README: Climate Temperature Analysis — Module 3 Assignment
🌍 Project Overview This notebook analyzes historical temperature data for a selected city using NOAA’s GHCN Daily dataset hosted on AWS. The goal is to: Calculate record and average high/low temperatures for the 1991–2020 climate normal period. Visualize how a specific year (2020) compares to those long-term trends using Matplotlib.
🧰 Tools & Libraries Python Pandas for data manipulation Matplotlib for visualization NOAA GHCN Daily data via AWS S3
📈 What This Notebook Does Loads daily temperature data for a selected station (e.g., Los Angeles, CA: USW00093134) Filters and processes data for: Record high/low temperatures (1991–2020) Average high/low temperatures (1991–2020) Actual high/low temperatures for 2020 Creates a clear, annotated plot comparing all three datasets
🧪 How to Run It Open the notebook in Jupyter or VS Code (cloud or local). Make sure you have internet access to read from AWS S3. Run each cell in order. The plot will appear at the end.
📍 Station Selection To analyze a different city: Replace the station_id (e.g., 'USW00093134') with your desired GHCN station code. You can find station IDs in the GHCN station inventory.
📚 Learning Goals Practice working with large climate datasets in the cloud Understand climate normals and variability Build reproducible, beginner-friendly code and documentation
🤝 Acknowledgments NOAA for providing open access to GHCN Daily data ATMS 523 instructors and classmates for support and inspiration Bokeh’s weather dashboard example for visualization inspiration