Climate Time Series Analysis
Five-day course, given by Dr. Manfred Mudelsee and based on his book. Consists of lectures and computer tutorials.
Contact CRA for your course proposal or pricing. Here is the contact form.
Audience
- Climatologists
- Hydrologists
- Statisticians
- Risk analysts
References
- Climate Service Center, Hamburg, Germany (August 2011)
Day 1: Introduction
- Climate archives
- Timescales
- Fundamental concepts of time series analysis
Day 1: Persistence models
- Stochastic processes
- Short versus long memory
Day 2: Bootstrap confidence intervals
- Statistical estimation
- Standard errors, bias, confidence intervals
- Classical methods
- Bootstrap resampling
- Monte Carlo methods
- Hypothesis tests
Day 2: Regression I
- Linear regression
- Nonlinear regression
- Nonparametric regression or smoothing
- Example: climate transitions
Day 3: Spectral Analysis
- Spectrum and periodogram
- Multitaper estimation
- Lomb–Scargle periodogram
- Example: solar cycles
Day 3: Extreme value time series
- Data types and risk estimation
- Stationary models (GEV, GP)
- Nonstationary models (Poisson)
- Example: flood risk analysis
- Example: hurricane risk
Day 4: Correlation
- Pearson's correlation coefficient
- Spearman's rank correlation coefficient
- Example: runoff variations
Day 4: Regression II
- Errors-in-variables model
- Prediction
- Example: climate sensitivity
- Example: proxy calibration
Day 5: Future directions
- Timescale modelling
- Higher dimensions
- Climate models
- Optimal estimation
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Springer
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