Forecast verification using information and noise

Abstract Numerical weather prediction (NWP) centres evaluate forecast quality using statistical assessments of error and skill, commonly referred to as scores. Traditional forecast verification relies on metrics such as root‐mean‐squared error, anomaly correlation coefficient, Brier score, and so forth. However, these measures can be sensitive to changes in bias and activity, as well as to intrinsic changes in forecast skill, complicating direct comparisons between deterministic models, ensemble averaging approaches, and machine‐learning‐based forecasts. A clear separation between intrinsic forecast skill and post‐processing enhancements, such as calibration, is essential for accurately assessing the predictive capability of a forecast system. In this work we take forecast reliability and resolution as the fundamental attributes characterising forecast performance, with resolution representing the intrinsic predictive capability of a system—its ability to distinguish among observed events. Recent work introduced information and noise as new metrics designed to provide an unambiguous assessment of statistical resolution. This study aims to introduce these novel scores in an accessible manner, relating them to traditional verification metrics, and to tackle some of the limitations of the original formulation. Additionally, we demonstrate their practical implementation for routine forecast verification in an operational NWP environment and provide examples of their use in the standard NWP research workflow. Examples of application of these new verification metrics to ensemble forecasting and to machine‐learning forecast models are also provided.