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Record W2289436918 · doi:10.1175/bams-d-14-00173.1

Multi-Radar Multi-Sensor (MRMS) Severe Weather and Aviation Products: Initial Operating Capabilities

2016· article· en· W2289436918 on OpenAlexaboutno aff
Travis M. Smith, Valliappa Lakshmanan, Gregory J. Stumpf, Kiel L. Ortega, Kurt Hondl, Karen Cooper, Kristin M. Calhoun, Darrel M. Kingfield, Kevin L. Manross, Robert Toomey, Jeff W. Brogden

Bibliographic record

VenueBulletin of the American Meteorological Society · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsnot available
Fundersnot available
KeywordsTornadoMeteorologyAviationEnvironmental scienceRadarSevere weatherSuiteQuantitative precipitation estimationPrecipitationStormWinter stormDoppler radarWeather radarRemote sensingComputer scienceGeologyGeographyEngineering

Abstract

fetched live from OpenAlex

Abstract The Multi-Radar Multi-Sensor (MRMS) system, which was developed at the National Severe Storms Laboratory and the University of Oklahoma, was made operational in 2014 at the National Centers for Environmental Prediction. The MRMS system consists of the Warning Decision Support System–Integrated Information suite of severe weather and aviation products, and the quantitative precipitation estimation products created by the National Mosaic and Multi-sensor Quantitative Precipitation Estimation system. Products created by the MRMS system are at a spatial resolution of approximately 1 km, with 33 vertical levels, updating every 2 min over the conterminous United States and southern Canada. This paper describes initial operating capabilities for the severe weather and aviation products that include a three-dimensional mosaic of reflectivity; guidance for hail, tornado, and lightning hazards; and nowcasts of storm location, height, and intensity.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.003

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 head0.028
GPT teacher head0.243
Teacher spread0.215 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations253
Published2016
Admission routes1
Has abstractyes

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