An open verification & benchmarking service for the AI-weather era
GraphCast. GenCast. Pangu-Weather. FourCastNet. ECMWF's AIFS. Microsoft's Aurora. Every major AI weather model of the last three years has shipped as open weights — runnable by any lab, any vendor, any government with a GPU. In January 2026, NVIDIA's Earth-2 stack made an open, production-grade model pipeline available to anyone.
The model is no longer the moat.
Operational adoption is blocked on trust, not accuracy.
A vendor can't credibly grade its own homework. A startup's leaderboard is a marketing asset, not a standard. As models become commodities, trust becomes the scarce resource — and trust isn't manufactured in the same lab that built the model being judged.
Not "build a competing model." Be the referee everyone accepts.
The community verification standard, already embedded across NOAA's Unified Forecast System.
Already benchmarking AI against physics models — 30+ projects spanning government, industry, and international partners.
Developmental Testbed Center ties run directly into NOAA and the U.S. Air Force.
Decades of institutional expertise in the metrics themselves — not just the software that computes them.
We are not starting from zero. We are 70% there.
Proper scoring rules for ensembles, spatial and neighborhood verification methods, dedicated treatment of extremes, and in-situ observations — not reanalysis — as ground truth.
An open benchmark, versioned like software, that any model — academic, national, or commercial — can submit to.
A qualification path operational adopters can point to: NOAA research-to-operations, DOD, and FAA.
Reproducible, citable, and free of vendor lock-in, so results can be checked by anyone.
The field is at its adoption inflection point: AI models are good enough to deploy and everyone knows it, but nobody has agreed on how to compare them for operational use. Standards set at moments like this persist for a decade — the way WeatherBench 2 became the default research benchmark simply by being first, rigorous, and open. The first mover on the operational standard doesn't just get early credit. It gets durable influence over how the entire field measures itself.
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Strategically, this earns external money at the exact moment NSF core funding faces a proposed ~40% cut.
Fund a small team — illustratively, 3–4 FTE for 18 months — to:
Clear. Modest. Catalytic.
Figures illustrative — scoped together with leadership and funders.
When everyone has the model, the institution everyone trusts to judge them wins.