Quantify and Evaluate the Forecast Uncertainty from Global Ensemble Forecast System (invited)

A forecast uncertainty can be quantified using a variety of methods, depending on the available information and the forecasting techniques used. Commonly, a probability forecast is one of the main approaches to communicate the uncertainty associated with the ensemble forecast which helps users make informed decisions.

Ensemble forecasting is one of the numerical weather prediction systems run in ensemble mode that generates multiple simulations with slightly different initial conditions and stochastic parameterizations during the model integration. The spread among these ensemble members indicates the forecast uncertainty. For example, an ensemble forecast might indicate a range of possible rainfall amounts, temperature values, or storm tracks. However, the spread may not be optimum due to imperfect model systems. The forecast range may be more or less than forecast errors (root mean square errors) which truly indicates the overdispersion and underdispersion respectively.

The model spread analysis is a fundamental methodology to diagnose model capability to present the forecast uncertainty in addition to ensemble forecasting skills. An optimum ensemble configuration, through adjusting the initial perturbations and model dynamic/physical perturbations (stochastic parameterization), can provide insight into forecast uncertainty. It's worth noting that no forecast is entirely certain, especially for long-range predictions. Weather forecast uncertainty is a complex topic that requires a combination of statistical analysis, meteorological expertise, and the use of advanced numerical models to provide the most accurate and informative predictions (see: Zhu et al, 2023: Quantify the Coupled GEFS Forecast Uncertainty for the Weather and Subseasonal Prediction. JGR Atmosphere).