Verification: Skill Scores and Bias
Verification: Skill Scores and Bias
💡 Key Takeaways
- Define verification in weather, climate, and disasters forecasting and explain why it matters for decision-making.
- Identify key skill scores (e.g., Brier score, CRPS, ROC AUC) and what they indicate about forecast accuracy and reliability.
- Distinguish bias from random error and learn how forecast bias can affect decisions and how to detect it.
- Explore how to improve verification with calibration, post-processing, and ensemble methods, and how to read verification plots.
❓ Frequently Asked Questions
What is forecast verification and why is it important in weather, climate, and disasters?
Forecast verification compares predictions to what actually happened to assess accuracy, reliability, and usefulness. It helps improve models, quantify risk, and guide decision-making.
What is a skill score and how does it differ from plain accuracy?
A skill score compares forecast performance to a reference forecast (e.g., climatology or persistence). It shows improvement over the baseline; values >0 indicate better-than-baseline skill, 0 means no improvement, and negative indicates worse.
What is the Brier score and what does it measure?
The Brier score measures the accuracy of probabilistic forecasts for binary events. It is the mean squared difference between forecast probabilities and observed outcomes (0 or 1); lower scores indicate better accuracy.
What is bias in forecasts and how can it be detected and addressed?
Bias is a systematic over- or under-prediction. It is detected by comparing long-term forecast averages to observations and via reliability checks. It can be addressed with bias correction, recalibration, or model improvements.