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Record W4402883532 · doi:10.5194/egusphere-2024-2978

Environments and lifting mechanisms of cold-frontal convective cells during the warm-season in Germany

2024· preprint· en· W4402883532 on OpenAlexaff
George Pacey, Stephan Pfahl, Lisa Schielicke

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicAeolian processes and effects
Canadian institutionsWestern University
FundersDeutsche Forschungsgemeinschaft
KeywordsConvectionMeteorologyClimatologyEnvironmental scienceAtmospheric sciencesGeologyGeography

Abstract

fetched live from OpenAlex

Abstract. Convection often initiates in proximity to cold fronts during the warm-season, but how various processes favour convective initiation at different regions relative to the front is still not well-understood. By combining automatic front detection methods and a convective cell tracking and detection dataset, the environments and availability of different lifting mechanisms are analysed. Our results indicate that pre-surface-frontal cells form in the environments with the highest surface dew points and convective available potential instability (CAPE). At other front relative regions, cells form in environments with lower CAPE and surface dew points, though still significantly higher than regions without cells. Mid-level relative humidity discriminates particularly well between post-frontal cell locations and regions without cells. Pre-surface-frontal cells form in environments with the strongest large-scale lifting at 850 hPa and 700 hPa and also with the strongest convective inhibition. We also observe importance of large-scale lifting post-frontal, particularly at 500 hPa. Observational sunshine duration data indicate less sunshine before cell initiation compared to regions without cells at most front relative regions, which highlights that solar heating may not be responsible for the majority of cold-frontal cell initiation. The results in this study are an important step towards a deeper understanding of the drivers of cold-frontal convection at different regions relative to the front.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.067
Threshold uncertainty score0.525

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.005
GPT teacher head0.182
Teacher spread0.177 · 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 teacher head, 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
Published2024
Admission routes1
Has abstractyes

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