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Record W1492336272

Comparison of Satellite and Aircraft Measurements of Cloud Microphysical Properties in Icing Conditions During ATREC/AIRS-II

2004· article· en· W1492336272 on OpenAlexaboutno aff
Louis Nguyen, Patrick Minnis, Douglas A. Spangenberg, Michele L. Nordeen, Rabindra Palikonda, M. M. Khaiyer, Ismail Gültepe, Andrew Reehorst

Bibliographic record

Venue11th Conference on Aviation, Range, and Aerospace and the 22nd Conference on Severe Local Storms · 2004
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsIcingEnvironmental scienceSatelliteMeteorologyCloud computingCloud topScatterometerGeostationary Operational Environmental SatelliteRemote sensingGeographyComputer scienceAerospace engineeringEngineeringWind speed
DOInot available

Abstract

fetched live from OpenAlex

COMPARISON OF SATELLITE AND AIRCRAFT MEASUREMENTS OF CLOUD MICROPHYSICALPROPERTIES IN ICING CONDITIONS DURING ATREC/AIRS-IILouis Nguyen*, Patrick MinnisNASA Langley Research Center, Hampton, VA, USADouglas A. Spangenberg, Michele L. Nordeen, Rabindra Palikonda, Mandana M. KhaiyerAnalytical Services and Materials Inc., Hampton, VA, USAIsmail GultepeMeteorological Service of Canada, Toronto, Ontario M3H 5T4Andrew L. ReehorstGlenn Research Center, Cleveland, OH, USA1. INTRODUCTIONSatellites are ideal for continuous monitoring ofaircraft icing conditions in many situations overextensive areas. The satellite imager data are used todiagnose a number of cloud properties that can be usedto develop icing intensity indices. Developing andvalidating these indices requires comparison withobjective “cloud truth” data in addition to conventionalpilot reports (PIREPS) of icing conditions. Minnis et al.(2004a,b) examined the relationships between PIREPSicing and satellite-derived cloud properties. TheAtlantic-THORPEX Regional Campaign (ATReC) andthe second Alliance Icing Research Study (AIRS-II) fieldprograms were conducted over the northeastern USAand southeastern Canada during late 2003 and early2004. The aircraft and surface measurements areconcerned primarily with the icing characteristics ofclouds and, thus, are ideal for providing some validationinformation for the satellite remote sensing product.This paper starts the process of comparing cloudproperties and icing indices derived from theGeostationary Operational Environmental Satellite(GOES) with the aircraft in situ measurements ofseveral cloud properties during campaigns and some ofthe The comparisons include cloud phase, particle size,icing intensity, base and top altitudes, temperatures,and liquid water path. The results of this study arecrucial for developing a more reliable and objectiveicing product from satellite data. This icing product,currently being derived from GOES data over the USA,is an important complement to more conventionalproducts based on forecasts, and PIREPS.2. DATAThe satellite data consist of 4-km GOES-12 pixelswith associated spectral radiances and cloud properties

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.458
Threshold uncertainty score0.714

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.032
GPT teacher head0.247
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations2
Published2004
Admission routes1
Has abstractyes

Explore more

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