Assessment of errors and biases in retrievals of X <sub>CO2</sub> , X <sub>CH4</sub> , X <sub>CO</sub> , and X <sub>N2O</sub> from a 0.5 cm <sup>−1</sup> resolution solar viewing spectrometer
Bibliographic record
Abstract
Abstract. Bruker™ EM27/SUN instruments are mobile commercial solar-viewing near-IR spectrometers. They show promise for expanding the global density of atmospheric column measurements of greenhouse gases and are being marketed for such applications. They have been shown to measure the same variations of atmospheric gases within a day as the high resolution spectrometers of the Total Carbon Column Observing Network (TCCON). However, there is little known about the long-term stability and uncertainty budgets of EM27/SUN measurements. In this study, which includes a comparison of 186 measurement days spanning 11 months, we note that atmospheric variations of Xgas within a single day are well captured by these low-resolution instruments, but over several months, the measurements drift noticeably. We present comparisons between EM27/SUN instruments and the TCCON using GFIT as the retrieval algorithm. In addition, we perform several tests to evaluate the robustness of the performance and determine the largest sources of errors from these spectrometers. We include comparisons of XCO2 and XCH4, and for the first time present comparisons of XCO and XN2O. Specifically we note EM27/SUN biases of 0.03 %, 0.75 %, −0.12 %, and 2.43 % for XCO2, XCH4, XCO, and XN2O respectively with 1σ precisions of 0.08 % and 0.06 % for XCO2 and XCH4 from measurements in Pasadena. We also identify significant error caused by non-linear sensitivity when using an extended spectral range detector used to measure CO and N2O.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".