An Analysis Of The Predictability Of The February 2021 Cold Air Outbreak At S2S Timescales
Bibliographic record
Abstract
The February 2021 cold air outbreak (CAO) brought long-lasting, freezing temperatures and extreme winter weather to the United States and Canada. The event resulted in major societal and economic impacts, including millions of power outages, hundreds of fatalities, and approximately $25 billion in losses. This thesis first analyzes the synoptic evolution of this CAO in order to explain how Arctic air arrived and stalled over central North America. Given the synoptic context, the analysis next considers the subseasonal-to-seasonal (S2S) predictability of this CAO by examining S2S ensemble model predictions from both high-top and low-top models. The S2S predictability analysis is motivated by the idea that processes and variability in the stratosphere, such as sudden stratospheric warmings (SSWs) and stratospheric wave reflection events, can play a significant role in the large-scale evolution and development of CAOs. Given the potential influence of the stratosphere, high-top and low-top forecast data are compared to determine if models that better resolve the stratosphere were able to predict the high impact nature of the CAO with longer lead times. The results suggest that the high-top models did better at predicting both the onset date and the duration of the CAO at S2S lead-times (i.e., week 3 and 4) but did not outperform the low-top models at synoptic time scales (i.e., week 2). The results highlight the importance of resolving the stratosphere to best realize the S2S prediction windows-of-opportunity that result from stratospheric variability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".