Assessment of ACE-MAESTRO v3.13 multi-wavelength stratospheric aerosol extinction measurements
Bibliographic record
Abstract
Abstract. The Measurement of Aerosol Extinction in the Stratosphere and Troposphere Retrieved by Occultation (MAESTRO) instrument on the SCISAT satellite provides aerosol extinction measurements in multiple solar wavelength bands. In this study, we evaluate the quality and utility of MAESTRO version 3.13 stratospheric aerosol extinction retrievals, from February 2004–February 2021, through comparison with measurements from other satellite instruments. Despite significant scatter in the MAESTRO data, we find that gridded median MAESTRO aerosol extinctions and stratospheric aerosol optical depth (SAOD) values are generally in good agreement with those from other instruments during volcanically quiescent periods. After volcanic eruptions and wildfire injections, gridded median MAESTRO extinction and SAOD are well correlated with other measurement sets but generally biased low by 40 %–80 %. The Ångström exponent (AE), which can provide information on aerosol particle size, is derived from the MAESTRO spectral extinction measurements in the lowermost stratosphere, showing perturbations after volcanic eruptions qualitatively similar to those from the Stratospheric Aerosol and Gas Experiment on the International Space Station (SAGE III/ISS) for the eruptions of Ambae (2018) and Ulawun (2019). Differences in AE anomalies after the 2019 extratropical Raikoke eruption may be due to the different spatiotemporal sampling of the two instruments. Furthermore, we introduce a method to adjust MAESTRO extinction data based on comparison with extinction measurements from SAGE III/ISS during the period from June 2017–February 2021, resulting in improved comparison during volcanically active periods. Our work suggests that empirical bias correction may enhance the utility of MAESTRO aerosol extinction data, which can make it a useful complement to existing satellite records, especially when multi-wavelength solar occultation data from other instruments are unavailable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".