Review article | Towards a Global Ground-Based Earth Observatory (GGBEO): Leveraging existing systems and networks
Peer-Reviewed Publication
Updates every hour. Last Updated: 25-Dec-2025 07:11 ET (25-Dec-2025 12:11 GMT/UTC)
To address the global environmental and climate crisis and advance the UN SDGs, it is essential to fully leverage Earth observations (EO) through the integration of atmospheric, hydrological, cryospheric, lithospheric, ecological, and socio-economic data. Based on discussions from the 2023 “Towards Global Earth Observatory” workshop, this paper highlights the fragmented nature of current EO and data repositories and emphasizes the need for a more coordinated global Ground-Based Earth Observatory (GGBEO). We summarize the status of key in-situ and ground-based remote sensing systems and outline the actions required to build an integrated GGBEO with interoperable data repositories, unified observation networks, and sustainable long-term support.
High-resolution data from the GHGSat satellite constellation reveal facility-level methane emissions at thousands of individual sites worldwide, according to a new study. The findings provide a far more detailed picture of methane emissions from the energy sector, offering new insights for global inventories and mitigation strategies. Methane is among the most powerful drivers of atmospheric warming a after carbon dioxide, and much of it comes from human activities – often from concentrated “point sources” such as individual oil, gas, and coal facilities. Methane emissions from these industries are generally estimated in two ways: bottom-up inventories and top-down atmospheric measurements. Bottom-up methods, based on limited ground measurements or assumptions about industrial activity, can miss unexpected or short-lived leaks. On the other hand, top-down approaches provide more direct data, but typically lack the resolution or frequency needed to identify individual emission sources. Together, these limitations highlight the need for improved tools that capture both detailed and comprehensive emissions information.
According to Dylan Jervis and colleagues, the GHGsat satellite network offers an ideal combination of high spatial resolution, strong sensitivity, and broad global coverage to better understand where methane is released and how those emissions change over time. Using global GHGSat observations from 2023, Jervis et al. grouped repeated methane plume detections from the same locations into time series and calculated how frequently each site emits above the satellites’ detection limit. They then used these patterns to estimate yearly average emissions for 3,114 oil, gas, and coal facilities worldwide, which totaled roughly 8.3 million tons per year of methane released. They found that oil and gas emissions fluctuate much more than coal emissions, meaning they require many more satellite observations to detect and quantify, which the authors argue is an important consideration for future monitoring rules. When these high-resolution estimates were compared with other major global inventories and lower-resolution satellite datasets, coal emissions matched well at the country level, while oil and gas emissions showed only moderate agreement and weak correlation at finer scales. The authors note that these differences highlight the importance of high-resolution satellites for identifying large, variable emitters that broader inventories overlook or misrepresent.
Harvard atmospheric scientists directly sampled 5-day old wildfire smoke in the upper troposphere and found large particles that are not reflected in current climate models.
A death toll of more than 1,100 is expected to rise significantly after a rare convergence of heavy rains and multiple cyclones that devastated multiple countries in south and southeast Asia. Cyclones — rotating storms with high winds in the Pacific Ocean — don’t normally manifest near the Earth’s equator, but last week’s extreme weather saw three such storms form in the area. As the frequency and intensity of cyclones ramp up, a team of researchers is calling for better forecasting with the development of intelligent observation networks.