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Record W4253174704 · doi:10.5194/acp-2019-920

Observational evidence of moistening the lowermost stratosphere via isentropic mixing across the subtropical jet

2019· preprint· en· W4253174704 on OpenAlexafffund
Jeffery Langille, Adam Bourassa, Laura L. Pan, Daniel Letros, B. H. Solheim, D. A. Degenstein, Daniel Zawada

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsUniversity of Saskatchewan
FundersCanadian Space Agency
KeywordsTropopauseStratosphereAtmospheric sciencesClimatologyTroposphereContext (archaeology)Isentropic processJet (fluid)Environmental scienceQuasi-biennial oscillationGeologyJet streamPhysics

Abstract

fetched live from OpenAlex

Abstract. Isentropic mixing across and above the subtropical jet in the presence of a double tropopause may be a significant mechanism for moistening the lowermost stratosphere. We present an analysis of high spatial resolution two-dimensional measurements of the water vapour distribution that were obtained using the Spatial Heterodyne Observations of Water instrument during a demonstration flight from NASA’s high altitude ER-2 airplane. We focus on a set of measurements from 37° to 44° North, obtained on July 21, 2017 during a flight off the West coast of North America, where the instrument sampled the region above and poleward of the subtropical jet. Analysis of these measurements reveals poleward mixing of moist filaments in the region of a double tropopause. These moist filaments are examined in the context of the meteorological fields in the ECMWF (ERA-interim) model output. It is shown that the observed moist filaments are consistent with isentropic mixing across and above the sub-tropical jet. These new observations provide further evidence that the tropospheric intrusion associated with the double tropopause contributes to moistening of the lowermost stratosphere

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.054
GPT teacher head0.282
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes2
Has abstractyes

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