Assimilation of IASI satellite CO fields into a global chemistry transport model for validation against aircraft measurements
Bibliographic record
Abstract
Abstract. This work evaluates the IASI CO product against independent in-situ aircraft data from the MOZAIC program and the POLARCAT aircraft campaign. The validation is carried out by analysing the impact of assimilation of eight months of IASI CO columns retrieved for the period of May to December 2008 into the global chemistry transport model LMDz-INCA. A modelling system based on a sub-optimal Kalman filter was developed and 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. Comparisons of the assimilated CO profiles with in situ CO measurements 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. Our analysis also shows the impact of assimilation of CO on the representation of transport into the Arctic region during the POLARCAT summer campaign. A considerable increase in CO mixing ratios over the Asian source region was observed when assimilation was used leading to much higher values of CO during the cross-pole transport episode. These higher values are in good agreement with data from the POLARCAT flights that sampled this plume.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".