New method for deriving total ozone from Brewer zenith sky observations
Bibliographic record
Abstract
[1] The Brewer spectrophotometer derives total column ozone from UV measurements using two operational modes: observing either direct sunlight (DS) or zenith sky (ZS) scattered sunlight. While DS measurements are more accurate, ZS measurements are necessary for generating long-term ozone time series unbiased by meteorological conditions and for the validation of satellite algorithms for cloudy scenes. In the ZS mode, column ozone (X) is obtained using the slant path (μ) and a weighted sum of the logarithms of the ZS intensities (F). Prior to this, however, a set of nine empirical coefficients, specific to each instrument, must be derived that relate μ and X to F. This requires a large set of near-simultaneous ZS and DS measurements that span the operational range of X and μ, which is difficult to obtain. In this work, a modified algorithm is presented in which radiative transfer model simulations of ZS observations are used to derive generic coefficients that describe the sky response to different observing conditions. Instrument-specific coefficients are obtained through a simple scaling and offset of these generic values, derived through ZS-DS comparisons. These two parameters may be accurately determined in a relatively short period of time and using a much more limited data set, such as during regular instrument calibrations. Since the new algorithm provides nine coefficients, no changes to the existing Brewer software are required. It is demonstrated that the new algorithm is as useful as the standard Brewer ZS algorithm with nine empirically estimated parameters derived using a vastly more extensive data set.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".