Forecasting in CPT Simon J Mason simoniri columbia
Forecasting in CPT Simon J. Mason simon@iri. columbia. edu International Research Institute for Climate and Society The Earth Institute of Columbia University SAWS Training Workshop on the Climate Predictability Tool (CPT) Pretoria, South Africa, 07 – 16 May, 2011
Cross-validation
Retroactive forecasting Given data for 1951 -2000, it is possible to calculate a retroactive set of probabilistic forecasts. CPT will use an initial training period to cross-validate a model and make predictions for the subsequent year(s), then update the training period and predict additional years, repeating until all possible years have been predicted.
Linear Regression To make a probabilistic prediction we assume normally distributed errors in the forecast based on: 1. Past errors 2. Sampling errors in the regression (intercept and slope)
Tools ~ Forecast ~ Series Crossvalidation of FMA rainfall at a station in NE Brazil, using ECHAM 4. 5.
To set the forecast precision: Tools ~ Options ~ Forecast Settings ~ Precision
To prevent forecasts of negative rainfall: Tools ~ Options ~ Data ~ Zero-Bound
How are probabilistic forecasts derived in CPT?
To reduce the problem of bimodal forecasts: Tools ~ Options ~ Data ~ Transform Y Data
Why is the category with the best guess forecast not always the category with the highest probability?
- Slides: 11