Influence of ozone and temperature climatology on the accuracy of satellite total ozone retrieval
Bibliographic record
Abstract
Deviation of assumed ozone profile shape from true profile in the radiative transfer calculation affects the accuracy of total ozone (TOZ) retrieval. Earlier studies have identified high profile shape sensitivity of retrieved TOZ in polar latitudes, in particular at high solar zenith angles (SZA). This paper is devoted to the question of how TOZ retrievals are influenced by the choice of ozone and temperature profiles from various currently available climatologies. Ozone and temperature profiles from those climatologies are applied in the Weighting Function Differential Optical Absorption Spectroscopy (WFDOAS) algorithm to retrieve TOZ from GOME spectral measurements. Comparison of the retrieved TOZ with ground based measurements from selected stations in the polar, middle‐, and low‐latitude regions indicate both systematic error and random errors associated with the profile shapes. Those errors become prominent at SZA more than 70°. The systematic error might be caused by the differences in the ozone number density peak altitude between the climatological profile and the actual profile. Biases in true temperature with respect to the climatological temperature profile contribute to the TOZ error by their impact on the ozone absorption coefficient and molecular scattering. Our studies based on GOME spectral measurements and synthetic radiance show that at high SZA, more than 10% systematic error in the retrieved TOZ can be easily introduced by the choice of climatological profiles. The random error is of the same order of magnitude, and it can be related to day‐to‐day variability of ozone and temperature profiles. In this paper we show that an improved and updated ozone and temperature climatological profiles can reduce the systematic errors in the retrieved TOZ from satellite spectral measurements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".