Recommendations for spectral fitting of SO <sub>2</sub> from miniature multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements
Bibliographic record
Abstract
Abstract. Fitting sulfur dioxide (SO2) differential slant column densities (dSCDs) from multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements of scattered sunlight is challenging because actinic light intensity is low in the wavelength regions where the SO2 absorption features are strongest. SO2 dSCDs were fit with different wavelength windows (λlow to λhigh) from ambient measurements with calibration cells of 2.2×1017 and 2.2×1016 molec. cm−2 inserted in the light path at different viewing elevation angles using an Ocean Optics USB2000 spectrometer in a miniature MAX-DOAS instrument. SO2 dSCDs were the least accurate, and fit errors were highest for fitting windows with λlow < 307 or λlow > 312 nm. The SO2 dSCDs also exhibited an inverse relationship with the depth of the differential features in the SO2 absorption cross section for fitting windows with λlow < 307 nm. Spectra measured at low viewing elevation angles (i.e., α=2∘) exhibited less accurate SO2 dSCDs for the same fitting windows compared with higher angles. The use of a 400 nm short-pass filter or a polynomial to account for stray light (the offset function) increased the accuracy of the SO2 dSCDs for many different fitting windows, decreased fit errors, and decreased the dSCDs' dependence on the depth of the SO2 differential absorption features. These results suggest that the radiance at shorter wavelengths was increased by stray light. The inaccuracies at lower fitting wavelengths were increased by stray light originating from light with λ > 400 nm. Deviation of the SO2 dSCD from the true value depended on the SO2 concentration for some fitting windows rather than exhibiting a consistent bias. Uncertainties in the SO2 dSCD reported by the fit algorithm were more than 50 % less than the true error for many windows, particularly for the measurements without the filter or offset function. For retrievals with the filter or offset function, increasing λhigh > 320 nm tended to decrease the reported fit uncertainty but did not increase the accuracy. Based on the results of this study, a short-pass filter and a fitting window of 307.5 < λ < 319 nm are recommended for the retrieval of SO2 SCDs from miniature MAX-DOAS measurements. If a filter is not available or conflicts with other species to be determined (e.g., NO2 or HCHO), the offset function should be enabled, and a fit window 307.5 < λ < 319 nm is still recommended.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".