Comparison of Satellite and Aircraft Measurements of Cloud Microphysical Properties in Icing Conditions During ATREC/AIRS-II
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".