Internal strategy briefing·NSF NCAR / RAL·Draft — July 2026

Where NCAR software can lead — and get paid for it

A scan of what RAL and the wider labs are building, the gaps worth solving, and where the money is when core NSF funding can no longer be assumed. Ten project ideas, scored on a matrix you can re-weight yourself.

Read-only research scan 10 project ideas ranked Funding-diversification lens Sources cited throughout

Bottom line up front

The short version

  1. The premise is now existential, not aspirational. In December 2025 OMB directed NSF to break up NCAR; a January 2026 letter proposed transferring the NCAR-Wyoming Supercomputing Center and narrowing NCAR's scope. UCAR sued, and on June 1, 2026 a federal judge blocked the supercomputer transfer as "arbitrary and capricious," citing brain-drain already underway. Core NSF support (~$123M, roughly half of NCAR's budget) faces a proposed ~40% cut. Diversifying funding is survival work.
  2. AI weather modeling has gone open-weight — so the model is no longer the moat. GraphCast, GenCast, Pangu, FourCastNet, ECMWF's AIFS and Microsoft's Aurora are all runnable by anyone. When everyone has the model, the scarce goods are trusted verification, AI-ready data stewardship, and domain coupling — exactly where a neutral public institution beats a corporation, and where RAL already owns the assets (MET/METplus, the new BEACON testbed, GDEX).
  3. There is a software-specific funding map that doesn't compete with shrinking core dollars. NSF CSSI and PESOSE, NOAA EPIC, DOE ASCR/SciDAC, DARPA, cloud-provider open-data programs, weather-data commercial contracts, and climate philanthropy (with a warm Schmidt Sciences board tie via UCAR) each fund different slices of this work.
  4. The best bets play to a software/DevX team's strengths and to public-good positioning simultaneously. They are buildable, they fill a documented gap, and they attract non-NSF money.
Top bet · 01

AI-model verification & benchmarking service

Turn MET/METplus + BEACON into the trusted, neutral "referee" for the open-weight AI-model boom.

Top bet · 02

MPAS developer-experience kit

Lower the adoption wall for the model NCAR is betting its future on — a DevX team's sweet spot.

Top bet · 03

ML-ready cloud-optimized data pipeline

Be the open, cited source of AI-ready weather data — the layer Big Tech and startups depend on.

01  Why now

The funding squeeze makes software strategy a survival strategy

NCAR has no direct federal budget line; it is a Federally Funded R&D Center run by UCAR under a cooperative agreement with NSF. That structure made it acutely exposed when, in December 2025, OMB Director Russell Vought announced NSF would "break up" NCAR. A January 2026 Dear Colleague Letter proposed transferring the NCAR-Wyoming Supercomputing Center — home to the Derecho supercomputer — to another steward, divesting research aircraft, and closing or narrowing the Mesa Lab.

UCAR sued NSF, NOAA, OMB and Commerce in March 2026. On June 1, 2026, Judge R. Brooke Jackson granted a preliminary injunction blocking the supercomputing-facility transfer, finding the action "arbitrary and capricious" and citing evidence of political retaliation and of a "brain drain" of supercomputing experts already occurring. The injunction blocks only the first concrete step; the broader restructuring review is unresolved.

Underneath the headline fight is a budget reality: NSF core funding to NCAR was roughly $123M in FY2025 — about half of NCAR's ~$311M total, with the rest from NOAA, NASA, DOD, FAA, DOE and EPA. The FY2026 request proposed a ~40% cut to NCAR (~$50M) inside a much larger proposed NSF reduction; Congress rejected the deepest cuts, but NSF still trimmed hundreds of individual program budgets by up to 30%.

Read this as leverage, not doom

The same pressure that threatens the institution is the reason software that earns external revenue or attracts non-NSF grants is suddenly the most strategically valuable thing a lab can build. Every idea below is chosen partly for how many sponsors could plausibly pay for it.

02  The landscape

The AI-weather field went open-weight — which changes where the value is

Between 2023 and 2026, data-driven weather models moved from demos to operations. The striking pattern for strategy: the flagship models are largely open. When the model is a commodity, competitive advantage shifts to the things around it that are hard to fake — and several of those are natural public-institution roles.

Who owns the models

Deterministic

GraphCast · Pangu · FourCastNet

Google DeepMind, Huawei, NVIDIA. Graph/transformer/Fourier models; a 10-day GraphCast runs in ~60s on one GPU. All openly available; FourCastNet anchors NVIDIA's open Earth-2 stack (launched Jan 2026).

Probabilistic

GenCast · AIFS-ENS · Aurora

GenCast (diffusion ensemble) beat ECMWF's 51-member ENS on 97.2% of targets. ECMWF's AIFS is operational with open weights; Microsoft Aurora 1.5 was open-sourced Nov 2025. Ensembles are now cheap.

Hybrid

NeuralGCM · ML parameterizations

Physics + ML hybrids aim for stability over long runs. Documented weak spot: offline skill doesn't guarantee stable online coupling, and models "extrapolate poorly" out of the training climate — an open research-and-tooling gap.

Who is filling the gaps commercially

While the models opened up, private companies moved into the observation and delivery layer — often straight into gaps left by NOAA cuts. WindBorne fed NOAA balloon data at no cost during 2025 radiosonde suspensions; Tomorrow.io and Spire sell microwave-sounder and radio-occultation data into AWIPS2 under multi-million-dollar contracts; Brightband is under a NOAA CRADA to make the NOAA-NASA archive AI-ready (the "NNJA-AI" dataset); Climavision's private X-band radar feed reached operational AWIPS2 in January 2026. NCAR already has live technical relationships in this ecosystem (WindBorne; The Weather Company running NCAR's MPAS in its GPU "GRAF" system).

The strategic thesis

NCAR should not try to out-train Google on a global model. It should own the roles a corporation can't credibly hold: the neutral referee that verifies everyone's models, the trusted steward of AI-ready public data, and the domain coupler that turns raw forecasts into fire / air-quality / water / energy decisions. These are software products, they are fundable from many sponsors, and RAL is already partway into each.

03  The gaps

Documented software gaps a mid-sized team could realistically close

Each of these is backed by recent literature or community signal (see sources). They are the raw material for the project portfolio in the next section.

Verification standards lag the models

New work shows deterministic metrics unfairly favor NWP over AI ensembles (Gneiting's "Potential CRPS"), reanalysis is a poor proxy for real observations (WeatherReal), global-average scores hide regional inequity (SAFE), and there is no standard benchmark for ML data assimilation (DAMBench). MET/METplus has no publicly documented AI-model verification extension yet.

Data isn't AI-ready

Pangeo names "a growing gap between industry (high) and scientific software (low)" and "no widely accepted standard for cloud-optimized netCDF." Converting agency archives (HRRR, AMPS, big ensembles) to Zarr / VirtualiZarr / Icechunk is real, ongoing friction. GDEX's relaunch on Kubernetes with Zarr framing is the opening.

Regional & coupled AI is underserved

Big Tech optimizes global medium-range. Limited-area, high-res, impact-coupled AI (fire, air quality, hydrology, energy) is exactly RAL's applied territory and largely open. NCAR's MILES-CREDIT platform is a credible base to build on.

Fortran ↔ ML coupling is fragile

Flagship codes (WRF, MPAS, CESM, CTSM) stay Fortran while emulators are PyTorch/JAX. Production-grade coupling (FTorch-class) plus online-stability tooling is a recognized, cross-cutting need — high leverage because it unblocks everything else.

Reproducibility & provenance

A 32-author 2025 review finds "provision of reproducible workflow pipelines remains an exception." Containerization (Apptainer), pinned environments, and W3C-PROV lineage capture are known remedies with weak adoption.

The MPAS adoption cliff

NCAR has stopped active WRF development in favor of MPAS, but regional MPAS "is only getting started" and community contributions "are just starting to come in." The onboarding / DevX gap is concrete and immediate — and squarely a software team's job.

04  The portfolio

Ten ideas, scored — and you set the weights

Each idea is scored 1–5 on five criteria. Because "which idea wins" depends on what you value, the ranking is live: drag the weight sliders and the table re-sorts. Defaults weight fundability highest, reflecting the moment. Click any column header to sort by it.

Criteria — Impact: community + operational value · Feasibility: can a RAL software team realistically do it (skills, data, compute) · Fundability: breadth of non-NSF sponsors & revenue potential · Strategic fit: leverages NCAR's unique public-good advantages · Ease: speed to a useful MVP (5 = easiest). Composite is a weighted average scaled to 100.

# Project Impact Feasib. Fund. Strat. fit Ease Composite

The ideas in detail

05  The funding map

Who could actually pay for this

The point of diversification is that no single sponsor gates the whole portfolio. Each source below fits a different slice.

NSF CSSICyberinfrastructure
Builds and sustains research software & data infrastructure. Natural home for the data pipeline, reproducibility framework, and Fortran↔ML coupling work. Still NSF, but a different pot than core NCAR funding.
NSF PESOSEOpen-source ecosystems
Funds governance and sustainability around an already-mature open-source product — not the science. Textbook fit for standing up a durable ecosystem around MET/METplus or MPAS. Underused lever.
NOAA EPICUFS / operations
Community modeling grants, hackathons, R2O pathways for the Unified Forecast System (UIFCW26, July 2026). Fits verification, MPAS DevX, and coupled-impacts work; NOAA is eyeing MPAS for a next-gen unified system.
DOE ASCR / SciDACExascale + ML
A recent $13.5M AI/ML-for-climate FOA and standing Earth-System-Model-Development calls. Strong fit for emulators, UQ, and GPU/coupling tooling.
DARPA / DOD / FAAMission-driven
RAL's national-security, aviation, and energy lines already draw here. Fits the applied decision-support platform and high-consequence verification (trust for operational AI).
Cloud open-dataAWS · Google · Microsoft
Storage/egress sponsorship + compute credits. NCAR precedent: CESM LENS on AWS Open Data; Pangeo's Google credits. Directly subsidizes the data pipeline. Google.org's AI-for-Science challenge has a climate track.
Commercial & internationalRevenue, not grants
Utilities, insurers, and foreign met services already pay RAL (Xcel, Kuwait, Korea, Saudi Arabia). The applied platform and verification service (30+ BEACON partners) are the most directly revenue-generating. Note: NCAR captures no license revenue today from heavy commercial use of WRF/MPAS — a conversation worth having.
Climate philanthropySchmidt · Bezos · pooled OSS funds
Schmidt Sciences funds climate software institutes (VESRI/VISS) and has a warm governance tie: Heidi Cullen is both its Director of Climate Initiatives and UCAR Board Vice Chair. Bezos Earth Fund's AI Grand Challenge and the new Open Source for Science Fund are plausible but unproven for atmospheric software — first-mover territory.

The warmest thread to pull first

Across all of philanthropy's 2025–26 surge, essentially none has reached atmospheric science — it's flowing to biomedical AI. That's a gap NCAR can occupy rhetorically and relationally: Schmidt Sciences already funds university atmospheric-science departments and climate-software institutes, and the UCAR board tie is a relationship, not a cold pitch.

06  Honesty box

What this scan is unsure about

  • The restructuring is live. Facts about the breakup, injunction, and budgets were true as of mid-2026 and can change materially. Treat the institutional picture as a moving target.
  • Some signals are conference-reported, not primary. NOAA's "10-year MPAS unified system" direction rests on AMS-talk coverage more than a funded roadmap. The RAL reorg's causal link to the funding threat is inference, not stated.
  • The scores are judgment, not measurement. The 1–5 ratings encode my read of the evidence; that's why the weights are yours to change. Feasibility especially depends on internal team composition I can't see.
  • A few product details need internal confirmation — e.g., whether AQ-WATCH is genuinely an RAL product vs. an adjacent EU project, and current GDEX capacity figures. Verify before citing externally.
  • No confirmed NCAR–NVIDIA/Microsoft AI partnership was found — an absence of evidence, not evidence of absence.

References

Sources

Institutional & funding situation

Colorado Sun — judge blocks NCAR breakup (Jun 2026) Eos — judge blocks NSF from dismantling NCAR Science — court blocks supercomputing-facility transfer Yale Climate Connections — future of NCAR uncertain Science — NCAR trims staff ahead of cuts Eos — NSF cuts program budgets up to 30%

NCAR / RAL software

UCAR — the rise of MPAS (WRF transition) UCAR — CREDIT AI weather platform RAL — BEACON AI Testbed RAL — programs (ERAP/TMAP/WCAP/WISP) DTC — MET / METplus RAL — FastEddy open source NSF NCAR — RDA relaunches as GDEX GitHub — NCAR/miles-credit

AI models, benchmarks & gaps

WeatherBench 2 (benchmark standard) Gneiting et al. — Potential CRPS for fair AI/NWP comparison WeatherReal — reanalysis vs. real observations DAMBench — ML data-assimilation benchmark gap Confidence-guided mixing (hybrid AI-climate stability) LEAP — ML workflows in climate modeling pyESD — statistical downscaling toolkit NVIDIA — CorrDiff generative downscaling Microsoft — Aurora 1.5 open foundation model NOAA WPO — JEDI data assimilation

Funding programs & diversification

NSF — POSE / PESOSE (open-source ecosystems) NOAA — EPIC / Unified Forecast System DOE — data-science/ML for climate FOA Schmidt Sciences — Virtual Institute of Scientific Software Schmidt Sciences — Heidi Cullen (UCAR board tie) Renaissance Philanthropy — Open Source for Science Fund NOAA GSL — Brightband NNJA-AI data CRADA Inside Climate News — WindBorne fills NOAA data gaps NOAA — NOAA–UCAR partnership (Dec 2024)