Validation of SAGE III/ISS solar water vapor data with correlative satellite and balloon-borne measurements
Bibliographic record
Abstract
Since June 2017, the Stratospheric Aerosol and Gas Experiment III instrument on the International Space Station (SAGE III/ISS) has been providing vertical profiles of upper tropospheric to stratospheric water vapor (WV) retrieved from solar occultation transmission measurements. The goal of this paper is to evaluate the publicly released SAGE III/ISS beta version 5.1 WV retrieval through intercomparison with independent satellite- and balloon-based measurements, and to present recommendations for SAGE III/ISS data quality screening criteria. Overall, we find that SAGE III/ISS provides high quality water vapor measurements. Low quality profiles are predominately due to retrieval instabilities in the upper stratosphere that cause step-like changes in the profile, and aerosol/cloud-related interferences (below ~20 km). Above 35 km, the retrieved uncertainty and noise in the data rapidly grow with increasing altitude due to relatively low extinction signal from water vapor. Below the tropopause, retrieved uncertainty increases with decreasing altitude due to enhanced molecular scattering and aerosol extinction. After screening low-quality data using the procedures described herein, SAGE III/ISS WV is shown to be in good agreement with independent satellite and balloon-based measurements. From 20 – 40 km, SAGE III/ISS WV v5.1 data exhibit a bias of 0.0 to -0.5 ppmv (~10 %) relative to the independent data, depending on the instrument and altitude. Despite its status as a beta version, the level of SAGE III/ISS WV agreement with independent data is similar to previous SAGE instruments, and therefore the data are suitable for scientific studies of stratospheric water vapor.
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 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".