Assimilation of IASI satellite CO fields into a global chemistry transport model for validation against aircraft measurements
Bibliographic record
Abstract
Abstract. A modelling system for assimilation of CO total columns measured by the IASI/MetOp was developed. The system, based on a sub-optimal Kalman filter coupled with the LMDz-INCA chemistry transport model, allows both assimilating long periods of historical data and making rapid forecasts of the CO concentrations in the middle troposphere based on latest available measurements. Tests of the forecast system were conducted during the international POLARCAT campaigns. A specific treatment that takes into account the representativeness of observations at the scale of the model grid is applied to the IASI CO columns and associated errors before their assimilation in the model. This paper presents the results of assimilation of eight months of historical satellite data measured in 2008. Comparisons of the assimilated CO profiles with independent in situ CO measurements from the MOZAIC program and the POLARCAT aircraft campaigns indicate that the assimilation leads to a considerable improvement of the model simulations in the middle troposphere as compared with a control run with no assimilation. Model biases in the simulation of background values are reduced and improvement in the simulation of very high concentrations is observed. The improvement is due to the transport by the model of the information present in the IASI CO retrievals. The consistency of the improvement contributes to the validation of the IASI CO data.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".