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Verification of Mesoscale Numerical Weather Forecasts in Mountainous Terrain for Application to Avalanche Prediction

2003· article· en· W2129569631 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueWeather and Forecasting · 2003
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsFisheries and Oceans CanadaUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaBC HydroDeutscher Akademischer AustauschdienstCanadian Foundation for Climate and Atmospheric Sciences
KeywordsMesoscale meteorologyTerrainMeteorologyNumerical weather predictionPrecipitationEnvironmental scienceQuantitative precipitation forecastNorth American Mesoscale ModelClimatologyWeather Research and Forecasting ModelGlobal Forecast SystemGeologyGeographyCartography

Abstract

fetched live from OpenAlex

Two high-resolution, real-time, numerical weather prediction (NWP) models are verified against case study observations to quantify their accuracy and skill in the mountainous terrain of western Canada. These models, run daily at the University of British Columbia (UBC), are the Mesoscale Compressible Community (MC2) Model and the University of Wisconsin Nonhydrostatic Modeling System (NMS). The main motivations of this work are: 1) to extend the lead time of avalanche forecasts by using NWP-projected meteorological variables as input to statistical avalanche threat models; and 2) to create another tool to help avalanche forecasters in their daily decision-making process.

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.

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 score0.159
Threshold uncertainty score0.300

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 head0.025
GPT teacher head0.228
Teacher spread0.202 · 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