Statistical interpolation of ozone measurements from satellite data (TOMS, SBUV and SAGE II) using the kriging method
Bibliographic record
Abstract
Abstract. This study demonstrates that ordinary kriging in spherical coordinates using experimental semi-variograms provides highly usable results, especially near the pole in winter and/or where there could be data missing over large areas. In addition, kriging allows display of the spatial variability of daily ozone measurements at different pressure levels. Three satellite data sets were used: Total Ozone Mapping Spectrometer (TOMS) data, Solar Backscattered UltraViolet (SBUV), and the Stratospheric Aerosol and Gas Experiment (SAGE II) ozone profiles. Since SBUV is a nadir-viewing instrument, measurements are only taken along the sun-synchronous polar orbits of the satellite. SAGE II is a limb-viewing solar occultation instrument, and measurements have high vertical resolution but poor daily coverage. TOMS has wider coverage with equidistant distribution of data (resolution 1° × 1.25°) but provides no vertical information. Comparisons of the resulting SBUV-interpolated (column-integrated) ozone field with TOMS data are strongly in agreement, with a global correlation of close to 98%. Comparisons of SBUV-interpolated ozone profiles with daily SAGE II profiles are relatively good, and comparable to those found in the literature. The interpolated ozone layers at different pressure levels are shown.Key words: Atmospheric composition and structure (middle atmosphere - composition and chemistry) - Meteorology and atmospheric dynamics (middle atmosphere dynamics)
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".