Tootfinder

From Licensing to Open Access: Designing a Sustainable Transition in Operational Weather Data
Emma Pidduck, Umberto Modigliani, Victoria L. Bennett, Fabio Venuti, Florian Pappenberger, Florence Rabier
https://arxiv.org/abs/2605.21673 https://arxiv.org/pdf/2605.21673 https://arxiv.org/html/2605.21673
arXiv:2605.21673v1 Announce Type: new
Abstract: This translational article documents the European Centre for Medium-Range Weather Forecasts (ECMWF) transition from a restricted data licensing model to open access under CC BY 4.0, completed in October 2025. The policy context included EU open data requirements and alignment with international data exchange frameworks. The transition was implemented through a tiered service model that kept core forecast data open while offering operationally supported delivery as a cost-recovered service. Between 2020 and 2025, ECMWF executed an iterative planning cycle: setting an annual target for revenue reduction, specifying additions to the open tier under that target, provisioning infrastructure, and assessing outcomes to update assumptions. Drawing on internal administrative records (2014 - 2025), we describe design choices, operational constraints, and early outcomes. In the six months following the end of the transition, more than 93% of previously paying organisations retained a Service Agreement, while open endpoint download volumes increased substantially. We discuss trade-offs in defining the open tier (resolution, parameters, schedule), the reduction of compliance overheads formerly associated with redistribution restrictions, and the scalability implications of global distribution. We note an emerging sustainability question as AI-based forecast products become freely available. The early evidence is consistent with the view that a tiered service model can be designed to reconcile open-access obligations with operational sustainability, subject to monitoring over longer contract renewal cycles (typically annual).
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"Several of my peers now “AI-wash” their work, meaning that even when they can perfectly competently execute on their jobs to the satisfaction of their management teams, said managers are unhappy if the engineers haven’t used AI in the work… so now they’re lying about using LLMs even in contexts where their professional judgement is that they aren’t the appropriate tool. They just do the work, the same way they have for decades, and say Claude did it. "

Story Protocol, a blockchain-based IP ownership network that raised $140M, rebrands as Data Foundation to build an on-chain registry for AI training data (Olivier Acuna/CoinDesk)
https://www.coindesk.com/business/2026/06/

a16z-backed crypto firm rebrands, shifts focus to solving AI’s global copyright headache
The startup formerly known as Story Protocol raised $140 million to secure internet rights and is now building an audit layer for data consent, licensing, and provenance for tech firms.

🎯 Futures deliver deferred results for async computations, with composition via await_all and await_first. Context associates data with a coroutine for easy adaptation of global-variable code.
🗓️ Roadmap: Complete Async API (v0.6) shipped March 2026, with Threads & Stabilization in Summer 2026, Framework Adapters in Q3 2026, and a stable v1.0 release targeted for November 2026 on PHP 8.6.
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🌍 🛰️ New five-year agreement signed between GÉANT and #EUMETSAT
We are proud to announce the signature of a five-year agreement between GÉANT and EUMETSAT, advancing our long-standing collaboration on #network and #connectivity

GÉANT to provide Single Point of Contact (SPOC) and connectivity services to EUMETSAT in collaboration with NRENs for the next five years | GÉANT CONNECT Online
GÉANT and EUMETSAT, the European Organisation for the Exploitation of Meteorological Satellites, are proud to announce the signature of a five-year agreement, advancing their long-standing collaboration on network and connectivity services to support the global distribution of meteorological satellite data that is essential for weather forecasting, climate monitoring, and environmental research. The new framework will

Quantification of atmospheric carbon dioxide from the Geostationary Operational Environmental Satellite (GOES East)
Aaron Sonabend-W, Sean Campbell, John Platt, Christopher Van Arsdale, Anna M. Michalak
https://arxiv.org/abs/2605.23991 https://arxiv.org/pdf/2605.23991 https://arxiv.org/html/2605.23991
arXiv:2605.23991v1 Announce Type: new
Abstract: There is a growing urgency to track greenhouse gasses with the resolution, precision and accuracy needed to support independent verification of $CO_2$ fluxes at local to global scales. The current generation of space-based sensors, however, only provides sparse observations in space and time. This challenge has fueled interest in the potential use of data from existing missions originally developed for other applications for inferring global greenhouse gas variability. The Advanced Baseline Imager (ABI) onboard the Geostationary Operational Environmental Satellite (GOES-East), operational since 2017, provides full coverage of much of the western hemisphere at 10-minute intervals from geostationary orbit at 16 wavelengths at an approximately 2$km^2$ spatial resolution. Here, we leverage this high spatial coverage and temporal revisit to develop a single-pixel, physics-guided neural network to estimate dry-air column $CO_2$ mole fraction ($XCO_2$). The model employs a time series of GOES-East's 16 spectral bands, ECMWF ERA5 lower tropospheric meteorology, MODIS surface reflectance, solar and satellite viewing geometry, and day of year. Training used collocated GOES-East and OCO-2/OCO-3 observations. We also present case studies illustrating the use of the model to observe $XCO_2$ enhancements over urban areas and drawdown over agricultural regions. Overall, while the precision of GOES-East derived $XCO_2$ can never rival that of dedicated instruments, the unprecedented combination of contiguous geographic coverage, 10-minute temporal frequency, and multi-year record offers the potential to observe aspects of atmospheric $CO_2$ variability currently unseen from space.
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