BRIGHT EVIDENCE PACK / Deployed
A world-class forecast now fits on one machine.
Weather forecasting has spent fifty years as one of the largest users of supercomputing. AI models trained on the archive now produce comparable global forecasts in minutes, and the weights are published. The measurements they depend on are the part that has not become cheap.
Canonical Bright record · JSON evidence pack · Key-facts embed
Dates and assessment
- Source published
- 2025-12-17
- Bright published
- 2026-09-09
- Substantive update
- 2026-09-12
- Evidence state
- Deployed
- Independent verification
- Not established by this source review
- Last source review
- 2026-09-12
The claim in context
The human problem
Many countries cannot run their own global forecast, and a forecast made elsewhere is not always tuned to the hazard that arrives at home.
The prior constraint
Observations must first be assembled into a coherent estimate of the whole atmosphere, then stepped forward by equations. Both halves ran on national supercomputers.
AI’s actual role
NVIDIA's January 2026 Earth-2 release published open weights spanning the chain: HealDA turns observations into an initial atmosphere, Atlas forecasts to 15 days, StormScope predicts local weather zero to six hours ahead. ECMWF's AIFS has been operational since 25 February 2025 and NOAA's AIGFS since 17 December 2025.
The documented result
NOAA reports that a single 16-day AIGFS forecast uses 0.3% of the computing resources of the operational GFS and finishes in about 40 minutes. ECMWF reports gains of up to 20% on tropical cyclone tracks from AIFS at a 28 km grid against 9 km for its physics model.
Why it may matter
The compute barrier to a world-class forecast has effectively collapsed. The observing barrier has not: SOFF reports that less than 10% of the required basic weather and climate data are available from least developed countries and small island developing states, and WMO notes that Germany has more GBON-compliant observing stations than the whole of the African continent.
Limitations
- Open weights are not the same as an operational service, and NVIDIA's own assimilation paper reports analyses about twice as far from the truth as ERA5.
- NOAA states that AIGFS version 1.0 degrades tropical cyclone intensity forecasts, which future versions will address.
- Speed and energy comparisons are mostly published by the organisations that built the models; NOAA's fractions come from a real operational pipeline, most multipliers do not.
- Every one of these models is trained on a reanalysis built from the observing network. No model can supply a measurement that was never made.
Original evidence
- NOAA deploys new generation of AI-driven global weather models · government
- ECMWF’s AI forecasts become operational · institution
- Closing the gaps in the observing network · World Meteorological Organization · institution
- Systematic Observations Financing Facility · institution
- NVIDIA: Earth-2 open weather model announcement · institution
Attribution
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