A Feature-Based Framework to Investigate Atmospheric Predictability
Bibliographic record
Abstract
Abstract The flow dependence of atmospheric predictability implies that forecast errors grow more rapidly in some atmospheric conditions than in others. A better understanding of this flow dependence thus requires a local analysis of error growth. To facilitate such an analysis, this study introduces a feature-based perspective. While feature identification and tracking is often applied to atmospheric systems, associated forecast errors exhibit small-scale structure and thus lack spatial coherence. Consequently, using a standard feature approach, merging and splitting of features are ubiquitous, which severely limit the ability to automatically identify distinct temporal feature evolutions and subject them to statistical analysis. While the spatial filtering of data alleviates this inherent challenge, it does not resolve it and incurs a loss of information. We overcome this challenge by introducing a feature postprocessing that combines individual features into regional-scale entities, which exhibit much increased spatial and temporal coherence. It is these postprocessed entities that prove suitable for subsequent feature-based analysis. We demonstrate the utility of the feature-based perspective by applying it to the spread of global ensemble experiments designed to assess upscale error growth. Analyses are exemplified that contribute to an improved understanding of the flow dependence of error growth mechanisms and that link error growth characteristics to local atmospheric conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".