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Record W2105176637 · doi:10.5194/nhess-7-231-2007

On the effects of vertical air velocity on winter precipitation types

2007· article· en· W2105176637 on OpenAlexafffund
Julie M. Thériault, Ronald E. Stewart

Bibliographic record

VenueNatural hazards and earth system sciences · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaInstitute for Catastrophic Loss Reduction
KeywordsSnowPrecipitationEnvironmental sciencePrecipitation typesWarm frontWinter stormRain and snow mixedFreezing rainAtmospheric sciencesAir mass (solar energy)ClimatologyMeteorologyGeologyBoundary layer

Abstract

fetched live from OpenAlex

Abstract. The various precipitation types formed within winter storms (such as snow, wet snow and freezing rain) often lead to very hazardous weather conditions. These types of precipitation often occur during the passage of a warm front as a warm air mass ascends over a cold air mass. To address this issue further, we used a one-dimensional kinematic cloud model to simulate this gentle ascent (≤10 cm/s) of warm air. The initial temperature profile has an above 0°C inversion, a lower subfreezing layer, and precipitation falls from above the temperature inversion. The cloud model is coupled to a double-moment microphysics scheme that simulates the production of various types of winter precipitation. The results are compared with those from a previous study carried out in still air. Based on the temporal evolution of surface precipitation, snow reaches the surface significantly faster than in still air whereas other precipitation types including freezing rain and ice pellets have a shorter duration. Overall, even weak background vertical ascent has an important impact on the precipitation reaching the surface, the time of the elimination of the melting layer, and also the evolution of the lower subfreezing layer.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.843
Threshold uncertainty score0.227

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.013
GPT teacher head0.236
Teacher spread0.224 · 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

Citations24
Published2007
Admission routes2
Has abstractyes

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