The State of Earth Observing System Today: Updates Compiled from Recent EUMETSAT Meteorological Satellite Conferences
Bibliographic record
Abstract
Abstract This work presents highlights from the 2021 and 2022 EUMETSAT conferences, drawn from their presentations, posters, and panel discussions and placed into the wider context of the global Earth Observing (EO) system. These highlights collectively reveal much about the current state of knowledge about the EO system, its potential future evolutions, and how that system can be used to produce useful products and services. European, American, and Asian space agencies presented their visions for the next generation of operational satellite programs and demonstrated how these will continue to improve environmental forecasting and monitoring products. User communities presented updates on the use of satellite data, including climate records, novel precipitation retrievals, drought monitoring, weather forecasting, and retrievals of a broadening range of trace gases. On the technology side, discussion on the impact of artificial intelligence (AI) on Earth observation was a major theme, particularly for weather forecasting, data assimilation, or other environmental predictions. Cloud computing was another topic due to its potential to streamline the workflow of EO scientists, enhancing collaborations and unlocking access to previously unavailable data or computing resources. Finally, discussion on the miniaturization of observational instruments was another major theme of both conferences, highlighting both the possibility of novel or enhanced observations and the emerging economic case for commercial entities to operate fleets of meteorological satellites.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".