Intercomparison of atmospheric water vapour measurements in theCanadian high Arctic
Bibliographic record
Abstract
Abstract. Water vapour is a critical component of the Earth system. Techniques to acquire and improve measurements of atmospheric water vapour and its isotopes are under active development. This work presents a detailed intercomparison of water vapour total column measurements taken between 2006 and 2014 at a Canadian high Arctic research site. Instruments include radiosondes, sun photometers, a microwave radiometer, and emission and solar absorption Fourier transform spectrometers (FTSs). Good agreement is observed between all combination of datasets, with correlation coefficients ≥ 0.90 showing high correlations. A variety of biases and calibration issues are revealed and discussed for all instruments. A new FTS dataset, resulting from the MUSICA (Multi-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) retrieval technique, is shown to offer accurate measurements of water vapour total columns; however, measurements show a small wet bias of approximately 6 %. A new dataset derived from Atmospheric Emitted Radiance Interferometer (AERI) measurements is also shown to provide accurate water vapour measurements, which usefully enables measurements to be taken during day and night (especially valuable during Polar Night). In addition, limited profile comparisons are conducted using radiosonde and ground-based FTS measurements. Results show MUSICA FTS profiles were within 15 % of radiosonde measurements throughout the troposphere.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".