They are running ensembles of models to predict the forecast. Chaotic systems are challenging to model, and models aren't perfect, so a collection of models is

They are running ensembles of models to predict the forecast. Chaotic systems are challenging to model, and models aren't perfect, so a collection of models is used (either different initial conditions or different versions of the model, as per https://forecast.weather.gov/glossary.php?word=Ensemble https://forecast.weather.gov/glossary.php?word=Ensemble). This gives you a distribution of predictions, which you can use to produce an average, as well as uncertainty, confidence bands, etc. The quality of these predictions and ensembles is empirically validated using proper scoring rules (https://en.wikipedia.org/wiki/Scoring_rule https://en.wikipedia.org/wiki/Scoring_rule), which is a robust way to measure the quality of a probabilistic forecast. Negative-log-likelihood, a ubiquitous loss function in machine learning, is an example of a proper scoring rule (with a minus in front of it). The science behind forecasting has gotten pretty sophisticated, it's an interesting field.