Forecasting northern polar stratospheric variability with competing statistical learning models
Bibliographic record
Abstract
The northern polar stratosphere plays an important role in modulating wintertime near‐surface temperatures in midlatitudes. Therefore forecasting northern polar stratospheric variability has the potential to extend the skill of seasonal winter weather forecasts. This research explores the utility of state‐of‐the‐art statistical learning models for medium‐range (10–20 day lead time) forecasting of northern polar stratospheric variability. In addition to indices representing the El Niño Southern Oscillation, the Quasi‐Biennial Oscillation, and the 11‐year solar cycle, indices of the upward flux of wave activity from the troposphere into the stratosphere (WAF) are used as predictors for modelling and forecasting stratospheric temperature and geopotential height anomalies. The WAF is known to provide the primary source of intraseasonal variability in the wintertime stratospheric polar vortex. Multiple linear regression, random forest, artificial neural network, and support vector regression (SVR) models were trained over the 1980–2005 time period, and replicated real‐time forecasts were generated for the 2005–2011 time period. The highest correlation skill for 10 day forecasts (ρ=0.41 for temperature, andρ=0.49 for geopotential height) was found using the SVR model, which represents an improvement in skill over previous work that did not include WAF, or lagged predictors. Using a permutation‐based method, the importance of each predictor was ranked, and the WAF predictors were found to be most important for improving forecast skill in all models. In addition to improved forecast skill, it is argued that the methods presented here represent an advance due to their increased simplicity and relative ease of physical interpretation.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".