Are we misdiagnosing ensemble forecast reliability? On the insufficiency of Spread-Error and rank-based reliability metrics

TL;DR

This paper critically examines the limitations of traditional reliability metrics for ensemble forecasts, demonstrating their insufficiency and proposing a new diagnostic to improve reliability assessment.

Contribution

It introduces a novel reliability diagnostic based on three statistics that better distinguish climatology and predictability contributions, addressing shortcomings of existing metrics.

Findings

Spread-Error equality is necessary but not sufficient for reliability.

Rank histograms can falsely indicate reliability due to covariance structure.

The new diagnostic separates climatology and predictability effects.

Abstract

It has been documented that Spread-Error equality and a flat rank histogram are necessary but insufficient for demonstrating ensemble forecast reliability. Nevertheless, these metrics are heavily relied upon, both in the literature and at operational numerical weather prediction centers. In this study, we demonstrate theoretically why the Spread-Error relationship is necessary but insufficient for diagnosing reliability up to second order, even when mean bias is absent or accounted for. Assuming joint normality between ensemble members and the reference truth, we further show with idealized experiments that the same covariance structure responsible for this insufficiency also produces false diagnoses of reliability with the rank histogram and the reliability component of the continuous rank probability score. Under this structure and when the ensemble mean is meaningfully different from climatology, the truth lies among the least (most) extreme members when climatological variance is excessive (deficient) in each member. Importantly, this behavior is also shown to be plausible in operational ensemble weather forecasts. Combining these results with calibration principles from statistical postprocessing leads us to conclude that both perfect dispersion and underdispersion are ill-defined. When diagnostics are misinterpreted as indicating the latter, improper tuning can lead to further deterioration of forecast quality, even while improving Spread-Error and rank histogram behavior. To address these issues, we propose a new reliability diagnostic based on three easily computed statistics, motivated by the structure of the joint distribution of ensemble members and the reference truth up to second order. The diagnostic separates contributions to unreliability originating from climatology and predictability, enabling a more precise and robust characterization of ensemble behavior.