Toward High Precision XCO<sub>2</sub>Retrievals From TanSat Observations: Retrieval Improvement and Validation Against TCCON Measurements
Bibliographic record
Abstract
Abstract TanSat is the 1st Chinese carbon dioxide (CO2) measurement satellite, launched in 2016. In this study, the University of Leicester Full Physics (UoL‐FP) algorithm is implemented for TanSat nadir mode XCO2retrievals. We develop a spectrum correction method to reduce the retrieval errors by the online fitting of an 8thorder Fourier series. The spectrum‐correction model and its a priori parameters are developed by analyzing the solar calibration measurement. This correction provides a significant improvement to the O2A band retrieval. Accordingly, we extend the previous TanSat single CO2weak band retrieval to a combined O2A and CO2weak band retrieval. A Genetic Algorithm (GA) has been applied to determine the threshold values of post‐screening filters. In total, 18.3% of the retrieved data is identified as high quality compared to the original measurements. The same quality control parameters have been used in a footprint independent multiple linear regression bias correction due to the strong correlation with the XCO2retrieval error. Twenty sites of the Total Column Carbon Observing Network (TCCON) have been selected to validate our new approach for the TanSat XCO2retrieval. We show that our new approach produces a significant improvement on the XCO2retrieval accuracy and precision when compared to TCCON with an average bias and RMSE of −0.08 ppm and 1.47 ppm, respectively. The methods used in this study can help to improve the XCO2retrieval from TanSat and subsequently the Level‐2 data production, and hence will be applied in the TanSat operational XCO2processing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".