Dense profiling of UTLS water vapour from low earth orbit using spatial heterodyne spectroscopy: Practical considerations, challenges and solutions
Bibliographic record
Abstract
The Spatial Heterodyne Observations of Water instrument (SHOW) is a limb imaging instrument that is being developed to provide accurate, dense, high vertical resolution measurements of water vapour in the upper troposphere and lower stratosphere. SHOW utilizes a field widened spatial heterodyne spectrometer operating in the limb viewing configuration to observe limb scattered sunlight in a small ~3 nm spectral window centered near 1365 nm. Vertically resolved images of the limb absorption spectrum are obtained with each frame that are inverted using non-linear optimal estimation to extract the vertical distribution of water vapour. The large throughput and high spectral resolution (0.02 nm unapodized) provided by the field widened SHS allows vertical profiles with a target vertical resolution of < 500 m to be obtained with rapid along track sampling (~50 km below 20 km and between 100 km – 300 km above 20 km) from a low earth orbit satellite. In this paper, we present the SHOW measurement concept and examine the practical considerations that influence design tradeoffs. We discuss the challenges and solutions that have been identified to optimize the instrument configuration and present an end-to-end simulation of the level 0 measurements, calibrations and level 2 water vapour product.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".