Labs
All course labs are listed below. Labs will usually be scheduled during Friday class sessions and are intended to completed in the 50 minute time slot, although you may occasionally need extra time.
| Lab | Title | Date |
|---|---|---|
| Lab 1: Setup & Hello World | Julia Installation, GitHub Classroom Workflow, and Quarto Basics | Fri., Aug. 29 |
| Lab 2: Julia Data Handling | DataFrames.jl, Plotting with CairoMakie, TX precipitation datasets, Basic exploratory data analysis | Fri., Sep. 5 |
| Lab 3: GEV Inference | GEV parameter estimation with Turing.jl, Benchmark with Extremes.jl, Confidence intervals for return levels | Fri., Sep. 12 |
| Lab 4: Bayesian Rainfall Analysis | Bayesian GEV model setup, Prior specification strategies, Two-station comparison problem | Fri., Sep. 19 |
| Lab 5: Regional Pooling Implementation | No pooling vs complete vs partial pooling, Bias-variance trade-offs, Shrinkage effects | Fri., Sep. 26 |
| Lab 6: Why Uncertainty Matters | Extreme values with depth-damage curves, Coastal flood applications, Uncertainty propagation beyond rainfall | Fri., Oct. 3 |
| Lab 7: Model Implementation and Testing | Hydrological model implementation, time-stepping algorithms, validation strategies | Fri., Oct. 17 |
| Lab 8: Bayesian Calibration | Turing.jl inference setup, Prior specification for hydro parameters, MCMC sampling and diagnostics | Fri., Oct. 24 |
| Lab 9: Quantile Mapping Implementation | Empirical and parametric quantile mapping, bias correction validation, correlation preservation challenges | Fri., Oct. 31 |
| Lab 10: HMM & NHMM Implementation | 2-state HMM implementation, NHMM extensions, climate change scenarios, synthetic weather generation | Fri., Nov. 7 |
| Lab 11: ML Downscaling Implementation | Neural networks for bias correction, supervised learning approaches, practical ML implementation | Fri., Nov. 14 |
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