Satellite observations of CO<sub>2</sub> from a highly elliptical orbit for studies of the Arctic and boreal carbon cycle
Bibliographic record
Abstract
Abstract Here we report on an observing system simulation experiment (OSSE) to compare the efficacy of near‐infrared satellite observations of CO2 from a highly elliptical orbit (HEO) and a low Earth orbit (LEO), for constraining Arctic and boreal CO2 sources and sinks. The carbon cycle at these latitudes (~50–90°N) is primarily driven by the boreal forests, but increasing anthropogenic activity and the effects of climate change such as thawing of permafrost throughout this region could also have an important role in the coming years. A HEO enables quasi‐geostationary observations of Earth's northern high latitudes, which are not observed from a geostationary orbit. The orbit and observing characteristics for the HEO mission are based on the Weather, Climate and Air quality (WCA) concept proposed for the Polar Communications and Weather (PCW) mission, while those for the LEO mission are based on the Greenhouse gases Observing Satellite (GOSAT). Two WCA instrument configurations were investigated. Adopting the Optimal configuration yielded an observation data set that gave annual Arctic and boreal regional terrestrial biospheric CO2 flux uncertainties an average of 30% lower than those from GOSAT, while a smaller instrument configuration resulted in uncertainties averaging 20% lower than those from GOSAT. For either WCA instrument configuration, much greater reductions in uncertainty occur for spring, summer, and autumn than for winter, with Optimal flux uncertainties for June–August nearly 50% lower than from GOSAT. These findings demonstrate that CO2 observations from HEO offer significant advantages over LEO for constraining CO2 fluxes from the Arctic and boreal regions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 |
| 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".