Towards An Automated Ship-Borne Fourier-Transform Spectrometer As a Validation Opportunity For Atmospheric CO2, CH4, And CO Column Densities
Bibliographic record
Abstract
Validation opportunities for model data and satellite observations in the short-wave infra-red spectral range for climate monitoring are still sparse above the oceans. Klappenbach et al. (2015) and Knapp et al. (2020) developed a ship-borne setup of a Fourier-transform spectrometer (EM27/SUN FTS) for direct sunlight observations on mobile platforms such as ships or pick-ups. The housing withstands oceanic on-deck-conditions and is equipped with a custom-built fast solar tracker. Knapp et al. (2020) tested the system on a ship cruise from Vancouver, Canada to Singapore for a five-week period in 2019, during which the instrument performed reliably. The tracker provided a pointing precision of better than 0.05° for 79% of the time. The precision of atmospheric total column densities retrieved from the FTS direct sunlight spectra was found to be 0.24ppm for carbon dioxide (CO2), 1.1ppb for methane (CH4), and 0.75ppb for carbon monoxide (CO). Our ultimate goal is to develop the setup towards autonomous operations on ships to routinely collect validation data for CO2, CH4, and CO column densities above the world's oceans. Therefore, we further improved on the FTS box. Most prominent is a simplification of the tracking algorithm from two-dimensional mapping to two one-dimensional functions, moving a 185° fisheye camera onto the tracking rotation stage, and a change to more reliable embedded computers. Those modifications allow for sun tracking down to a solar zenith angle of 75° and increase robustness against mechanical misalignments between tracker and camera. A test campaign was conducted in the vicinity of a local coal power plant in Mannheim, Germany by mounting the FTS box on a pick-up and driving a stop-and-go pattern perpendicular to the plume direction. To this purpose, a 24 V battery powering mode was implemented. We plan another deployment of the instrument on the Japanese research vessel Mirai in February 2021. The campaign is conducted in cooperation with the Japanese National Institute for Environmental Studies (NIES) in the western North Pacific. Such routine validation opportunities of atmospheric CO2, CH4, and CO column densities would be a valuable asset for global climate monitoring. Knapp, M., Kleinschek, R., Hase, F., Agustí-Panareda, A., Inness, A., Barré, J., Landgraf, J., Borsdorff, T., Kinne, S., and Butz, A.: Ship-borne measurements of XCO2, XCH4, and XCO above the Pacific Ocean and comparison to CAMS atmospheric analyses andS5P/TROPOMI, Earth System Science Data Discussions, 2020, 1–20, https://doi.org/10.5194/essd-2020-132, https://essd.copernicus.org/preprints/essd-2020-132/, 2020. Klappenbach, F., Bertleff, M., Kostinek, J., Hase, F., Blumenstock, T., Agusti-Panareda, A., Razinger, M., and Butz, A.: Accurate mobileremote sensing of XCO2 and XCH4 latitudinal transects from aboard a research vessel, Atmospheric Measurement Techniques, 8,5023–5038, https://doi.org/10.5194/amt-8-5023-2015, 2015.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".