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Record W2946097049 · doi:10.15191/nwajom.2019.0703

Gulf of Alaska Cyclone in DaytimeMicrophysics RGB Imagery

2019· article· en· W2946097049 on OpenAlexaboutno aff
Michael T. Lawson, Kevin Fuell

Bibliographic record

VenueJournal of Operational Meteorology · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsnot available
FundersNational Aeronautics and Space Administration
KeywordsMeteorologyEnvironmental scienceDaytimeNowcastingCloud coverRemote sensingCloud heightRGB color modelCloud topCloud computingGeologyAtmospheric sciencesGeographyComputer scienceArtificial intelligence

Abstract

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The NASA Short-term Prediction Research and Transition (SPoRT) Center has provided National Weather Service (NWS) Alaska Region forecasters with the experimental Daytime Microphysics (DtMicro) red-green-blue (RGB) product to support forecasting aviation hazards (Berndt et al. 2017), which has become an integral tool in the forecast process. On 11 March 2018, a rapidly deepening cyclone entered the Gulf of Alaska and developed gale-force winds and relatively strong convection for late winter, while lowlevel clouds and fog remained in its wake. The multispectral DtMicro RGB (Rosenfeld and Lensky 1998; EUMETSAT User Service Division 2009) provided an efficient product to analyze cloud properties and surface features with improved efficiency compared to single-channel visible or infrared imagery. The DtMicro RGB and 0.64 µm visible images (Fig. 1) show a mature, occluded cyclone over the Gulf of Alaska at 2030 UTC (1230 LST). The DtMicro RGB combines visible and infrared channels related to cloud brightness, particle size, and temperature in order to analyze convective clouds and other cloud and surface features (Table 1; Lensky and Rosenfeld 2008). A limb correction and intercalibration was applied to infrared channels using the technique outlined in Elmer et al. (2016). These adjustments allow for greater consistency across the imager swath and consistency between numerous polar-orbiting satellites. The RGB clearly delineates the back-bent occlusion (dark orange to red), and dry air wrapping into the system, allowing a view of the low to mid-level clouds in the center of the image. Bright orange/yellow combinations over the Alaska panhandle and northwestern Canada are mountain-wave cirrus clouds composed of small ice particles. The magenta cloud features within the dry slot are deeper convective cells, and cyan to yellowgreen cloud features are low-level water clouds. Near Illiamna, the tan to dull green coloring indicates very low stratus and fog, which is not distinguishable from snow cover in the visible imagery.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.002

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 head0.015
GPT teacher head0.235
Teacher spread0.220 · 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 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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