Hradec Králové total ozone assimilated time series from 1961 to 2010
Bibliographic record
Abstract
Homogenized data series of total ozone measurements taken by the regularly and well calibrated Dobson and Brewer spectrophotometers at Hradec Králové (Czech) and the data from the re-analyses ERA-40 and ERA-Interim were assimilated and combined to investigate differences between the particular data sets over Central Europe, the NH mid-latitudes. The Dobson-to-Brewer transfer function and the algorithm for approximation of the data from the re-analyses were developed, tested and applied for creation of instrumentally consistent and completed total ozone data series of the 50-year period 1961-2010 of observations. The assimilation has reduced the well-known seasonal differences between Dobson and Brewer data below the 1% calibration limit of the spectrophotometers. Incorporation of the ERA-40 and ERA-Interim total ozone data on days with missing measurements significantly improved completeness and reliability of the data series mainly in the first two decades of the period concerned. Consistent behaviour of the original and assimilated data sets was found in the pre-ozone-hole period (1961-1985). In the post-Pinatubo (1994-2010) era the data series show seasonal differences that can introduce uncertainty in estimation of ozone recovery mainly in the winter-spring season when the effect of the Montreal Protocol and its Amendments is expected. All the data sets confirm substantial depletion of ozone also in the summer months that gives rise to the question about its origin. The assimilated and completed data series of total ozone will be further analyzed to quantify chemical ozone losses and contribution of natural atmospheric processes to the ozone depletion over the region. This case study points out importance of selection and evaluation of the quality and consistency of the input data sets used in estimation of long-term ozone changes including recovery of the ozone layer over the selected areas.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".