A physically based correction for stray light in Brewer spectrophotometer data analysis
Bibliographic record
Abstract
Brewer ozone spectrophotometers have become an integral part of the global ground-based ozone monitoring network collecting data since the early 1980s. The double-monochromator Brewer version (MkIII) was introduced in 1992. With the Brewer hardware being so robust, both single- and double-monochromator instruments are still in use. The main difference between the single Brewers and the double Brewers is the much lower stray light in the double instrument. Laser scans estimate the rejection level of the single Brewers to be 10 −4.5 , while the doubles improve this to 10 −8 , virtually eliminating the effects of stray light. For a typical single-monochromator Brewer, stray light leads to an underestimation of ozone of approximately 1 % at 1000 DU ozone slant column density (SCD) and can exceed 5 % at 2000 DU, while underestimation of sulfur dioxide reaches 30 DU when no sulfur dioxide is present. This is because even a small additional stray light contribution at shorter wavelengths significantly reduces the calculated SCD at large values. An algorithm for stray light correction based on the physics of the instrument response to stray light (PHYCS) has been developed. The simple assumption is that count rates measured at any wavelength have a contribution from stray light from longer, and thus brighter, wavelengths because of the ozone cross-section gradient leading to a rapid change in intensity as a function of wavelength. Using the longest measured wavelength (320 nm) as a proxy for the overall brightness provides an estimate of this contribution. The sole parameter, on the order of 0.2 % to 0.6 %, that describes the percentage of light at the longest wavelength to be subtracted from all channels is determined by comparing ozone calculations from single- and double-monochromator Brewers making measurements side-by-side. Removing this additional count rate from the signal mathematically before deriving ozone corrects for the extra photons scattering within the instrument that produce the stray light effect. Analyzing historical data from co-located single- and double-monochromator Brewers provides an estimate of how the stray light contribution changes over time in an instrument. The corrected count rates of the measured wavelengths can also be used to improve other calculations: the sulfur dioxide column and the aerosol optical depth, the effective temperature of the ozone layer, or any other products. A multi-platform implementation of PHYCS, rmstray, to correct the count rates for stray light and save the corrected values in a new B-file for use with any existing Brewer data analysis software is available to the global Brewer user community at https://doi.org/10.5281/zenodo.8097038 (Savastiouk and Diémoz, 2023).
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".