Prequel to the Stories of Warm Conveyor Belts and Atmospheric Rivers: The Moist Tongues Identified by Rossby and His Collaborators in the 1930s
Bibliographic record
Abstract
Abstract The model of atmospheric rivers (ARs) has been around since the 1990s. A closely related model is the warm conveyor belt (WCB) developed in the 1970s. Looking further back in time, a phenomenon known as the “moist tongue” was intensively investigated in the late 1930s and early 1940s by Rossby and his collaborators using the innovation of isentropic analysis. This article aims to establish a historical perspective on the development of the moist tongue model and its relevance to the current models of WCBs and ARs. As it turns out, the moist tongue was identified as an extension of moist air into a region of lower moisture content on the selected isentropic charts. Most moist tongues are driven by large-scale cyclonic and anticyclonic eddies and are often accompanied by surface cold fronts in close proximity. Ahead of the moist tongues, areas of continuous precipitation are caused mainly by the motion of moist air up the steep isentropic slopes over warm fronts or topographical features. In the warm season, the mere presence of a moist tongue could be sufficient to give thunderstorms. A reanalysis dataset is used to reexamine the structures and evolutions of two moist tongue events in 1936. It is shown that not all but some of the moist tongues fit well with the modern conceptual models of WCB and AR. These two case studies also serve to elucidate the usefulness of reanalysis data for investigating historical high-impact weather events that were poorly understood due to the lack of observational data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".