Severe weather models estimate the probability of a dangerous storm each day, but no forecast is ever certain. Issue a warning at a low probability threshold, and nearly every real severe weather event gets caught — along with many warnings for days that turn out calm, which can erode public trust in future warnings.
The AI Weather Forecasting Lab models 500 forecast days. Lowering the warning-issue threshold increases the odds of catching a genuine severe weather event, at the cost of more false-alarm warnings on days that never actually turn severe.
The public-trust dimension is what separates this from a purely technical threshold-tuning problem: a string of false alarms measurably reduces how seriously people take the next warning, which means the "right" threshold has to account for behavior, not just forecast accuracy.
🧪 Try it yourself: the AI Weather Forecasting Lab simulation lets you move the warning-issue threshold and watch the season-long outcome update live.
🧪 Try it yourself: the AI Weather Forecasting Lab simulation lets you experiment with everything described above directly in your browser.