MAESTRO instrument operation and performance over two decades in orbit
Bibliographic record
Abstract
Abstract. MAESTRO (Measurement of Aerosol Extinction in the Stratosphere and Troposphere Retrieved by Occultation), a dual UV (ultraviolet) and Visible-NIR (visible-near-infrared) spectrometer, has been operating aboard the Canadian satellite SCISAT for over 21 years. Recently, MAESTRO version 4.5 data products were released, consisting of volume mixing ratio profile data for NO2 and O3 retrieved from the UV channel, as well as a separate O3 product from the Visible-NIR channel. An aerosol extinction product is currently under development. Motivated by the instrument’s longevity, this paper will review the MAESTRO operations and performance over its lifetime, examining the key issues that impact its retrievals of atmospheric constituents. These include: a) the design of the MAESTRO spectrometer measurement schemes for sunset and sunrise occultations, including the role of long-term changes in the measured spectral intensities from the UV and Visible-NIR spectrometers, b) the determination of the position of the MAESTRO field of view (FOV) on the Sun, c) changes in the MAESTRO FOV position during occultations, including the impacts of this on the Level 1 transmittance calculation(s), d) understanding the relative position between the MAESTRO FOV and the ACE-FTS (Atmospheric Chemistry Experiment – Fourier Transform Spectrometer) FOV which are crucial for incorporating the input ACE-FTS atmosphere data in the Level 2 retrieval, e) verifying on-orbit the wavelength assignment at the detector arrays affected by a sudden persistent change in the instrument’s thermal environment, and f) the approaches taken to determine the MAESTRO measurement tangent heights, which are the pivotal steps in the MAESTRO retrieval of atmospheric constituents.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".