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Multiscale Analysis of the 7 December 1998 Great Salt Lake–Effect Snowstorm

2001· article· en· W2180914439 on OpenAlexaboutno aff
W. James Steenburgh, Daryl J. Onton

Bibliographic record

VenueMonthly Weather Review · 2001
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsnot available
Fundersnot available
KeywordsMesoscale meteorologyShoreWinter stormMM5Front (military)GeologySnowPrecipitationSalt panClimatologyStormConvergence zoneSubmarine pipelineOceanographyMeteorologyGeomorphologyGeography

Abstract

fetched live from OpenAlex

The large-scale and mesoscale structure of the Great Salt Lake–effect snowstorm of 7 December 1998 is examined using radar analyses, high-density surface observations, conventional meteorological data, and a simulation by the Pennsylvania State University–National Center for Atmospheric Research fifth generation Mesoscale Model (MM5). Environmental conditions during the event were characterized by a lake–700-hPa temperature difference of up to 22.5°C, a lake–land temperature difference as large as 10°C, and conditionally unstable low-level lapse rates. The primary snowband of the event formed along a land-breeze front near the west shoreline of the Great Salt Lake. The snowband then migrated eastward and merged with a weaker snowband as the land-breeze front moved eastward, offshore flow developed from the eastern shoreline, and low-level convergence developed near the midlake axis. Snowfall accumulations reached 36 cm and were heaviest in a narrow, 10-km-wide band that extended downstream from the southern shore of the Great Salt Lake. Thus, although the Great Salt Lake is relatively small in scale compared to the Great Lakes, it is capable of inducing thermally driven circulations and banded precipitation structures similar to those observed in lake-effect regions of the eastern United States and Canada.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.287
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.000

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 head0.022
GPT teacher head0.250
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 teacher head, not a consensus.

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

Citations50
Published2001
Admission routes1
Has abstractyes

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