Towards a Global Ground-Based Earth Observatory (GGBEO): Leveraging existing systems and networks
Bibliographic record
Abstract
To tackle the planetary environmental and climate crisis and meet the United Nations’ Sustainable Development Goals (SDGs), we must fully leverage the potential of Earth observations (EO). This involves integrating globally sourced data on the atmosphere, hydrosphere, cryosphere, lithosphere, along with ecological and socio-economic information. By harmonizing and integrating these diverse data sources, we can more effectively incorporate observational data into multi-scale modeling and artificial intelligence (AI) frameworks. This paper is based on discussions from the “Towards Global Earth Observatory” workshop held from May 8–10, 2023, organized by the World Meteorological Organization (WMO) and the Atmosphere and Climate Competence Center (ACCC), in collaboration with the Institute for Atmospheric and Earth System Research (INAR) at the University of Helsinki. The current state of EO and data repositories is fragmented, highlighting the need for a more integrated approach to establish a new global Ground-Based Earth Observatory (GGBEO). Here, we summarize the current status of selected in-situ and ground-based remote sensing observation systems and outline future actions and recommendations to meet scientific, societal, and economic needs. In addition, we identify key steps to create a coordinated and comprehensive GGBEO system that leverages existing investments, networks, and infrastructures. This system would integrate regional and global ground-based in situ and remote sensing systems, marine, and airborne observational data. An integrated approach should aim for seamless coordination, interoperable and harmonized data repositories, easily searchable and accessible data, and sustainable long-term funding.
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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 | 0.018 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.016 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".