Assessment of the quality of the ozone measurements from the Odin/SMR instrument using data assimilation
Bibliographic record
Abstract
Space-based remote-sensing instruments providing atmospheric measurements have different time and space resolutions, and coverage. This makes the direct comparison of the measurements very difficult. Data assimilation has proven to be a far more powerful tool than simple interpolation techniques to create three-dimensional analyzed fields for a given data set. In this paper, we describe how the assimilation of ozone data from the Odin/SMR instrument can be used to assess its precisions and biases against other ozone-measuring instruments. To assess the quality of Odin/SMR ozone retrievals by MOLIERE-5 against ozonesondes, Envisat/MIPAS, Earth Probe/TOMS, and UARS/HALOE data, we use a three-dimensional variational assimilation scheme applied to the Météo-France MOCAGE chemistry transport model. The MOCAGE-PALM assimilation system has been already used by Météo-France and CERFACS to analyse the Envisat/MIPAS data for the ASSET intercomparison exercise. We have further developed and calibrated the configuration of this system to better account for the Odin/SMR ozone profiles. The upgraded system is used to assimilate the Odin/SMR ozone during the August 2003 – November 2003 period and intercomparisons are made with the other ozone measuring techniques. The Odin/SMR analysis and the other ozone data sets are in good agreement at mid and high latitudes, while in the lower tropical stratosphere, we found a positive bias of the Odin/SMR, Envisat/MIPAS, and Earth Probe/TOMS data compared to measurements from UARS/HALOE and ozonesondes. The precision of Odin/SMR ozone retrievals in terms of standard deviation is about 20% in the tropics, below 10% at high southern latitudes, and below 5% at high northern latitudes. PACS No.: 82.33.Tb
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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 | 0.001 | 0.000 |
| 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 | 0.001 | 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 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".