- Understand what Atmospheric Statistics is and how it supports Atmospheric science and policy.
- Learn the core computational tools for this course (Python, Jupyter, Git, etc.).
- Set up a reproducible analysis environment.
- Get comfortable with version control, documentation, and data management best practices.
- Practice running and documenting a simple analysis workflow.
Atmospheric Statistics applies statistical theory and data analysis to Atmospheric data — such as air and water quality, climate variables, ecological counts, or satellite observations. It focuses on understanding uncertainty, variability, and patterns in Atmospheric systems.
Core questions addressed:
- How certain are we about pollution levels in a city?
- How do rainfall, temperature, and vegetation interact?
- What is the confidence level in climate trend estimates?
Key applications:
- Climate change studies (trend analysis, anomaly detection)
- Water and air quality monitoring
- Atmospheric impact assessment
- Remote sensing data validation
- Sheldon Ross – A First Course in Probability
- Larry Wasserman – All of Statistics
- MIT OpenCourseWare – Probability and Statistics
- Applied Linear Statistical Models – Kutner et al.
- Rob Hyndman – Forecasting: Principles and Practice (Free online and best for time-series analysis.) website from Rob Hyndman to proper documentation
- Penn State STAT 501 / 510 (Free lecture notes) Very clear explanations of regression and applied statistics.
- Daniel J. Jacob – Introduction to Atmospheric Chemistry One of the best open resources connecting theory to observations. https://acmg.seas.harvard.edu/people/faculty/djj/book/
- Wallace & Hobbs – Atmospheric Science: An Introductory Survey Strong physical grounding; widely used in atmospheric science.
- Seinfeld & Pandis – Atmospheric Chemistry and Physics The reference for aerosol, gas-phase chemistry, and atmospheric processes.
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Jake VanderPlas – Python Data Science Handbook Excellent for NumPy, Pandas, Matplotlib, and practical workflows. https://jakevdp.github.io/PythonDataScienceHandbook/
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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (one of the best for Machine learning, Deep learning)