A study of polar ozone depletion based on sequential assimilation of satellite data from the ENVISAT/MIPAS and Odin/SMR instruments
Bibliographic record
Abstract
Abstract. The objective of this study is to demonstrate how polar ozone depletion can be mapped and quantified by assimilating ozone data from satellites into the wind driven transport model DIAMOND, (Dynamical Isentropic Assimilation Model for OdiN Data). By assimilating a large set of satellite data into a transport model, ozone fields can be built up that are less noisy than the individual satellite ozone profiles. The transported fields can subsequently be compared to later sets of incoming satellite data so that the rates and geographical distribution of ozone depletion can be determined. By tracing the amounts of solar irradiation received by different air parcels in a transport model it is furthermore possible to study the photolytic reactions that destroy ozone. In this study, destruction of ozone that took place in the Antarctic winter of 2003 and in the Arctic winter of 2002/2003 have been examined by assimilating ozone data from the ENVISAT/MIPAS and Odin/SMR satellite-instruments. Large scale depletion of ozone was observed in the Antarctic polar vortex of 2003 when sunlight returned after the polar night. By mid October ENVISAT/MIPAS data indicate vortex ozone depletion in the ranges 80–100% and 70–90% on the 425 and 475 K potential temperature levels respectively while the Odin/SMR data indicates depletion in the ranges 70–90% and 50–70%. The discrepancy between the two instruments has been attributed to systematic errors in the Odin/SMR data. Assimilated fields of ENVISAT/MIPAS data indicate ozone depletion in the range 10–20% on the 475 K potential temperature level, (~19 km altitude), in the central regions of the 2002/2003 Arctic polar vortex. Assimilated fields of Odin/SMR data on the other hand indicate ozone depletion in the range 20–30%.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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 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".