Every year, extreme weather events move faster, strike harder, and become less predictable. The radar networks, satellites, and computer models that power your storm warnings are evolving—and with them, the question of who controls critical weather data.
Today's forecast system is a hybrid. The National Weather Service (NWS) issues warnings reaching 330 million Americans. But increasingly, private companies—NVIDIA with its new Earth-2 AI models, drone operators like WindBorne, and satellite firms like Saildrone—are stepping into the data pipeline. This shift raises urgent questions: Will innovation make forecasts better for everyone, or will premium predictions create a two-tiered warning system?
The tension came into sharp focus during the 2024 hurricane season. When Hurricane Milton made its rapid intensification across the Gulf of Mexico in October, public forecasters using traditional models initially underestimated the speed of strengthening. Private forecasting firms with access to commercial satellite data and AI-driven analysis updated their models hours before NWS issued revised estimates. The difference meant different evacuation timelines for millions—a preview of what unequal data access could mean in real emergencies.
How Today's Public Weather System Works
The NWS doesn't work in isolation. Its warnings—for tornadoes, hurricanes, flash floods, and blizzards—depend on a complex backbone of government infrastructure and increasingly, private partnerships.
The current backbone includes:
- Government radar stations, weather satellites, and atmospheric balloons that collect raw weather observations
- Numerical models that blend physics-based predictions with real-time data to forecast storm paths and intensity
- Private companies already embedded in the system—like PlanetiQ, which signed a $24 million NOAA contract to supply atmospheric profiles from commercial satellites
This public-private collaboration has yielded real progress. In the 1980s, hurricane track predictions were off by an average of 300 miles. Today, that margin has shrunk to 50 miles. Free, accurate warnings have saved countless lives.
How private data improves forecasts today: Saildrone's autonomous ocean drones collect atmospheric and ocean temperature data in hurricane formation zones—regions where traditional observation platforms rarely venture. This data feeds directly into NWS models, improving intensity predictions for storms in their development phase. Similarly, PlanetiQ's constellation of small satellites measures atmospheric pressure and humidity profiles continuously, filling gaps left by aging weather balloons. These improvements matter: better intensity forecasts mean more accurate storm surge predictions and evacuation guidance.
The Pressure Points: Budget Limits and Staffing Gaps
But the system is under stress. NOAA and the NWS face staffing shortages and budget pressures that are pushing them to reshape how forecasting works.
What's changing:
- Commercial data with delays: Private firms like WindBorne and Saildrone now supply weather observations via drones and balloons. Often, these superior datasets are held private for 48 hours to years before being released publicly—a practice meant to protect commercial value and recoup investment. WindBorne's high-altitude balloons, for instance, provide atmospheric measurements that improve hurricane intensity forecasts, but the company retains exclusive access for 72 hours to sell forecasting services to commercial users.
- Proposed restructuring: Policymakers are exploring models where NOAA focuses on core observations while advanced forecasting moves to the private sector, potentially through commercialized services that charge for premium predictions.
- AI model acceleration: Companies are deploying machine learning to crunch observational data faster than traditional physics-based models. Some private weather services now update forecasts every 6 minutes instead of every 3-6 hours, but this high-frequency data remains proprietary.
- NOAA's emerging response: NOAA is preparing its own drone initiative—Meteodrones for Beyond Visual Line of Sight (BVLOS) operations—planned for 2026 deployment. These autonomous atmospheric sensors will allow NOAA to collect high-resolution data in hazardous regions where manned operations are unsafe, reinforcing the private fill-in strategy: public agencies adopt advanced drone and satellite platforms to compete with commercial data suppliers.
Supporters of this shift argue it would free public resources for innovation and attract capital to weather infrastructure that government budgets can't afford. Critics worry it could fracture the free, universal warning system that protects entire communities equally and create financial incentives to withhold data during critical moments.
The Real-World Stakes: How Storm Warnings Could Change
The potential restructuring isn't abstract. If forecasting becomes increasingly privatized, the way you receive storm warnings could shift dramatically.
Concrete examples of private innovation already outpacing public forecasts:
- Flash flood prediction: Traditional NWS flash flood warnings rely on observational data that can lag 15-30 minutes behind current conditions. Private weather services using real-time satellite precipitation estimates and AI models now issue flash flood alerts 45 minutes earlier in some cases. During the 2023 Vermont flooding events, communities with access to premium forecast services received alerts before official NWS warnings, giving them critical extra time for evacuation.
- Severe weather rotation detection: AI-powered analysis of radar data can now identify rotation signatures in thunderstorms milliseconds after they appear—faster than human meteorologists can process the same data. Companies like Weather Underground and Tomorrow.io have deployed these systems commercially, enabling earlier tornado warnings for paying subscribers.
- Tornado genesis prediction: NVIDIA's Earth-2 models have demonstrated the ability to predict tornado formation 15-20 minutes earlier than traditional physics models in controlled tests. This earlier warning could mean the difference between people sheltering in place and people remaining outdoors in dangerous conditions.
Potential benefits of more private involvement:
- Denser, higher-resolution data from commercial satellites and AI models enable hyper-local predictions—think street-level flood risk or rotation-level storm details delivered minutes ahead.
- Faster innovation through competition: proprietary hurricane models have already begun outperforming some government models on specific variables.
- AI breakthroughs like NVIDIA's Earth-2 deliver nowcasting (0–6 hour forecasts) in minutes rather than hours, potentially saving lives in fast-moving events.
The key risks:
- Paywalls and inequality: Premium forecasts—delivered first and with highest precision—could flow to paying subscribers and well-resourced businesses, while rural or low-income communities rely on basic public forecasts. This already happens: wealthy suburbs in tornado alley subscribe to premium services while rural counties use only NWS warnings.
- Data delays: Historical NOAA contracts have withheld crucial hurricane track data for months or years. If this pattern continues, early warning advantages accrue only to those with access. In the 2024 hurricane season, one private data provider delayed sharing superior hurricane track data for 5 days, citing contractual restrictions.
- Vendor lock-in: If a single company's data becomes dominant in national forecasting, a shift in pricing, terms, or service could disrupt the entire warning system.
The Scenario: Which Future Do We Get?
| Scenario | Public Access Model | Impact on Storm Preparation |
|---|---|---|
| Full public sharing | All data released immediately | Free, reliable, universal warnings for all |
| Delayed proprietary data | 48 hours to 5 years embargo | Paying customers get edge; public warnings lag |
| Heavy privatization | Basic free forecasts + premium paid services | Unequal warning lead times by income/geography |
NVIDIA Earth-2: Open Models, Private Infrastructure
The emergence of NVIDIA's Earth-2 illustrates the contradictions at the heart of this shift. Earth-2 consists of open AI models—Nowcasting, Medium Range, and Global Data Assimilation—that are freely available on Hugging Face and GitHub and already outperform traditional physics-based forecasting on several weather variables. This open licensing is crucial: it stands as a counterpoint to the proprietary vendor lock-in that could emerge if forecasting depends entirely on closed commercial systems.
In practice, Earth-2 works like this: The model takes current atmospheric observations (temperature, pressure, humidity, wind) and generates future weather states minute-by-minute. For the next 6 hours, these AI predictions outperform traditional models on metrics like precipitation accuracy and wind field representation. For forecasts 6-15 days out, Earth-2 shows promise on large-scale patterns but requires ensemble approaches to match traditional accuracy.
Unlike fully proprietary tools locked behind corporate walls, Earth-2 invites collaboration from researchers worldwide. Universities and meteorological agencies are already testing Earth-2 locally, and some early adopters report 30-40% speedups in their forecasting operations compared to running traditional models. This openness could supercharge public forecasting if adopted by NOAA. Our earlier Earth-2 coverage explored how these models deliver nowcasting in minutes, potentially saving lives in fast-moving storm events.
But there's a catch: Earth-2 runs on NVIDIA infrastructure and serves enterprise clients. A meteorological agency wanting to run Earth-2 operationally needs NVIDIA GPU clusters—expensive capital expenditure. NOAA would need to either purchase hardware or pay for NVIDIA cloud services to operationalize Earth-2 at scale. That raises questions about cost, dependency, and whether an open model on proprietary infrastructure truly democratizes forecasting or creates a different kind of lock-in. The key distinction remains: Earth-2's open licensing model offers agencies the legal freedom to run forecasts independently, unlike systems where code and data both remain proprietary.
Beyond Warnings: Environmental Monitoring and Transparency
The weather data question extends beyond storm alerts. Commercial satellites now track methane leaks, industrial flaring, and emissions with increasing precision. This independent monitoring helps regulators and environmental watchdogs expose violations.
But proprietary high-resolution imagery could complicate this picture. If key datasets are available only to paying subscribers, journalists and grassroots monitors may lose the ability to independently verify environmental violations. That shifts power: from "hidden gets exposed" to "exposure depends on who can afford the data."
The Path Forward: Questions for Policymakers and Companies
The core challenge isn't choosing between innovation and access—we need both. The real question is how to structure partnerships and policies to ensure that happens.
What needs to happen:
- Contract terms that prioritize rapid data sharing to public systems over commercial delays
- Investment in open models like Earth-2 that lower barriers to advanced forecasting
- Safeguards ensuring rural and low-income communities receive the same warning lead times as affluent areas
- Transparency about which forecasts are funded by taxpayers and which serve private interests
As extreme weather accelerates, the decisions made in the next few years will shape whether warnings remain a public good—equally reliable for all 330 million Americans—or become a privileged service.
