Recent space sensor developments at ABB Analytical for weather forecasting applications
Bibliographic record
Abstract
The generation of daily weather forecasts relies to a large extent on the use of data from dedicated satellites. In recent years, several programs have been targeted at improving the sensors providing weather forecast data, both in polar and geostationary orbits. As part of the US National Polar-orbiting Operational Environmental Satellite System (NPOESS), the Cross-track Infrared Sounder (CrIS) is the new generation of sounding instruments providing vertical profiles from polar orbits of atmospheric temperature, pressure, and humidity. ABB Analytical has developed the interferometer module and the internal calibration target for the CrIS sensor. An overview of these contributions is presented. The Hyperspectral Environmental Suite (HES) is meant to be the next generation of US weather sounders from geostationary orbits. In Europe, the Meteosat Third Generation will also make use of an improved infrared sounder, the IRS. ABB Analytical has developed a generic interferometer module suitable for these two programs. Starting from the basic mission requirements, ABB Analytical has derived independently the requirements for the interferometer modules that would satisfy the needs of these two programs. A design was developed and two prototypes were built and tested. This paper also describes some of the results of this work.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".