Gulf of Alaska Cyclone in DaytimeMicrophysics RGB Imagery
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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.
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 source (direct Gemma or distilled Codex), 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".