Subseasonal Forecast Skill of Snow Water Equivalent and Its Link with Temperature in Selected SubX Models
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Accurate and skillful subseasonal forecasts have tremendous potential for sectors that are sensitive to hazardous weather and climate events. Analysis of prediction skill for snow water equivalent (SWE) and near-surface air temperature (T2m) is carried out for three (GEPS, GEFS, and FIM) global models from the subseasonal experiment (SubX) project for the 2000–14 period. The prediction skill of SWE is higher than the skill of T2m at week-3 and week-4 lead times in all models. The GEPS forecast tends to yield higher (lower) prediction skill of SWE (T2m) compared to the other two systems in terms of correlation skill score. The snow–temperature relationship in reanalysis is characterized by a strong negative correlation over most of the midlatitude regions and a weak positive correlation over high-latitude Arctic regions. All forecast systems reproduced well these observed features; however, the snow–temperature relationship is slightly weaker in the GEPS model. Despite the apparent lack of skill in temperature forecasts at week 4, all three models are able to predict the sign of temperature anomalies associated with extreme SWE conditions albeit with reduced intensity. The strength of the predicted temperature anomaly associated with extreme snow conditions is slightly weaker in the GEPS forecast compared to reanalysis and the other two models, despite having better skill in predicting SWE. These apparent disparities suggest that weak snow–temperature coupling strength in the model is one of the contributing factors for the lower temperature skill.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it