1. Ensemble reliability and the signal-to-noise paradox in large-ensemble subseasonal forecasts
- Author
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Roberts, Christopher David and Vitart, Frederic
- Subjects
Physics - Atmospheric and Oceanic Physics - Abstract
Recent studies have suggested the existence of a `signal-to-noise paradox' (SNP) in ensemble forecasts that manifests as situations where the correlation between the forecast ensemble mean and the observed truth is larger than the correlation between the forecast ensemble mean and individual forecast members. A perfectly reliable ensemble, in which forecast members and observations are drawn from the same underlying probability distribution, will not exhibit an SNP if sample statistics can be evaluated using a sufficiently large ensemble size ($N$) over a sufficiently large number of independent cases ($M$). However, when $M$ is finite, an apparent SNP will sometimes occur as a natural consequence of sampling uncertainty, even in a perfectly reliable ensemble with many members. In this study, we evaluate the forecast skill, reliability characteristics, and signal-to-noise properties of three large-scale atmospheric circulation indices in 100-member subseasonal reforecasts. Consistent with recent studies, this reforecast dataset exhibits an apparent SNP in the North Atlantic Oscillation (NAO) at subseasonal lead times. However, based on several lines of evidence, we conclude that the apparent paradox in this dataset is a consequence of large observational sampling uncertainties that are insensitive to ensemble size and common to all model comparisons over the same period. Furthermore, we demonstrate that this apparent SNP can be eliminated by application of an unbiased reliability calibration. However, this is achieved through overfitting such that sample statistics from calibrated forecasts inherit the large sampling uncertainties present in the observations and thus exhibit unphysical variations with lead time. Finally, we make several recommendations for the robust and unbiased evaluation of reliability and signal-to-noise properties in the presence of large sampling uncertainties.
- Published
- 2024