Experimental Subseasonal‐to‐Seasonal (S2S) Forecasting of Atmospheric Rivers Over the Western United States
Bibliographic record
Abstract
Abstract A multimodel evaluation of subseasonal‐to‐seasonal (S2S) hindcast skill of atmospheric rivers (ARs) out to 4‐week lead over the western United States is presented for three operational hindcast systems: European Centre for Medium‐Range Weather Forecasts (ECMWF; Europe), National Centers for Environmental Prediction (NCEP; U.S.), and Environment and Canada Climate Change (ECCC; Canada). Ensemble mean biases and Brier Skill Scores are examined for no, moderate, and high levels of AR activity (0, 1–2, and 3–7 AR days/week, respectively). All hindcast systems are more skillful in predicting no and high AR activity relative to moderate activity. There are isolated regions of skill at week‐3 over 150–125°W, 25–35°N for the no and high AR activity levels, with larger magnitude and spatial extent of the skill in ECMWF and ECCC compared to NCEP. The spatial pattern of this skill suggests that for high AR activity, a southwest‐to‐northeast orientation is more predictable at subseasonal lead times than other orientations, and for no AR activity, more skill exists in the subtropical North Pacific, upstream of central and southern California. AR hindcast skill along the western U.S. is most strongly increased in hindcasts initialized during Madden‐Julian Oscillation (MJO) Phases 1 and 8, and hindcast skill is substantially decreased over California in hindcasts initialized during MJO Phase 4. Skill modulations in the ECMWF hindcast system conditioned on El Niño‐Southern Oscillation phase are weaker than those conditioned on particular MJO phases. This work provides hindcast skill benchmarks and uncertainty quantification for experimental real‐time forecasts of AR activity during winters 2019–2021 as part of the S2S Prediction Project Real‐time Pilot Initiative in collaboration with the California Department of Water Resources.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".