Comparison of improved Aura Tropospheric Emission Spectrometer (TES) CO <sub>2</sub> with HIPPO and SGP aircraft profile measurements
Bibliographic record
Abstract
Abstract. Comparisons are made between mid-tropospheric Tropospheric Emission Spectrometer (TES) carbon dioxide (CO2) satellite measurements and ocean profiles from three Hiaper Pole-to-Pole Observations (HIPPO) campaigns and land aircraft profiles from the United States Southern Great Plains (SGP) Atmospheric Radiation Measurement (ARM) site over a 4-yr period. These comparisons are used to characterize the bias in the TES CO2 estimates and to assess whether calculated and actual uncertainties and sensitivities are consistent. The HIPPO dataset is one of the few datasets spanning the altitude range where TES CO2 estimates are sensitive, which is especially important for characterization of biases. We find that TES CO2 estimates capture the seasonal and latitudinal gradients observed by HIPPO CO2 measurements; actual errors range from 0.8–1.2 ppm, depending on the campaign, and are approximately 1.4 times larger than the predicted errors. The bias of TES versus HIPPO is within 0.85 ppm for each of the 3 campaigns; however several of the sub-tropical TES CO2 estimates are lower than expected based on the calculated errors. Comparisons of aircraft flask profiles, which are measured from the surface to 5 km, to TES CO2 at the SGP ARM site show good agreement with an overall bias of 0.1 ppm and rms of 1.0 ppm. We also find that the predicted sensitivity of the TES CO2 estimates is too high, which results from using a multi-step retrieval for CO2 and temperature. We find that the averaging kernel in the TES product corrected by a pressure-dependent factor accurately reflects the sensitivity of the TES CO2 product.
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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) | 0.000 | 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 | 0.000 | 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 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".