National Weather Service Picks Google Cloud for Forecasting Supercomputer Shift

The agency aims to have its cloud-based forecasting environment operating at scale by early 2027, giving it more room to combine fast AI models with physics-based forecasting.

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National Weather Service Picks Google Cloud for Forecasting Supercomputer Shift
National Weather Service Picks Google Cloud for Forecasting Supercomputer Shift

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The National Weather Service has chosen Google Cloud to host the next generation of its weather-modeling supercomputer, moving the backbone of its forecasting operations out of agency data centers. The target is an at-scale cloud environment by early 2027, after a smaller early-access phase and performance testing. That date signals infrastructure readiness—not that every new forecasting system will be in production. The system being moved is WCOSS, the Weather and Climate Operational Supercomputing System. Today, two on-premises supercomputers run the models behind NWS forecasts. Rather than replacing those machines one for one, the agency says cloud capacity could let it shift computing resources more quickly as needs change, including during severe weather. That matters because conventional numerical forecasts and AI models use computing power differently. NWS is not abandoning physics-based simulation. It expects hybrid systems that combine physical models with AI, while cautioning that AI weather prediction is still early and can struggle in extreme conditions. The agency already operates AI models including AIGFS, AIGEFS, and Hybrid-GEFS. Some were developed in Google DeepMind’s cloud and then deployed on NWS hardware. The new environment could also help move Warn-on-Forecast—from a demonstration toward operations. That system is designed to improve warnings for tornadoes, severe thunderstorms, and flash floods, but its cloud-oriented design has been harder to operationalize without an operational cloud platform. The immediate question is whether NWS can validate performance at final scale while migrating the production suite that underpins its forecasts.

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3 key points

Rather than replacing its aging supercomputers one-for-one, NWS will use Google Cloud as the primary HPC provider for WCOSS, with an at-scale environment expected by early 2027. The move could make it easier to allocate compute for AI and hybrid forecasts, especially during severe weather, and help advance cloud-native systems such as Warn-on-Forecast from demonstration toward operations. It is not a full shift away...

  1. 01

    NWS will begin with early access, then performance-test final scale; 2027 marks infrastructure readiness, not universal production deployment.

  2. 02

    Existing AI models AIGFS, AIGEFS, and Hybrid-GEFS were developed partly in Google DeepMind’s cloud and deployed on-premises.

  3. 03

    Cloud flexibility addresses differing compute footprints of numerical and AI forecasting, including demand spikes during severe weather.

The National Weather Service has selected Google Cloud as the primary high-performance-computing provider for a move that will take the backbone of its weather-modeling operations out of agency data centers. The shift is meant to give NWS more flexibility as AI-based forecasting develops, with a full-scale cloud environment targeted for the start of 2027.

The system being moved is the Weather and Climate Operational Supercomputing System, or WCOSS. NWS currently relies on two operational supercomputers in on-premises data centers to run the weather models that support its forecasts. Agency officials are treating the expiration of that equipment as an opening to modernize rather than simply replace it.

A different computing shape for AI forecasts

Traditional numerical weather prediction and AI prediction have different computing footprints, according to NWS central operations director David Michaud. An on-premises system has a finite set of compute resources; cloud infrastructure can, in the agency’s view, shift the balance of those resources more quickly as needs change, including during severe weather.

That flexibility does not mean NWS is abandoning physical simulation. The agency expects future forecasting systems to combine traditional physics-based models with AI models, seeking the strengths of both approaches. Its officials also caution that AI weather prediction remains at an early stage and can be limited in extreme-weather situations.

Existing models gain a more natural home

NWS already runs AI-based models: AIGFS, a global forecasting model; AIGEFS, a global ensemble model that provides probabilistic information; and Hybrid-GEFS, which combines a traditional numerical model with AI. Some of that AI-model work was developed in Google DeepMind’s cloud environment before being deployed to NWS’s existing on-premises infrastructure.

Projects the cloud environment could support

  • AI and hybrid forecasting models whose compute needs differ from conventional numerical models.
  • The Warn-on-Forecast System, designed to improve warnings for tornadoes, severe thunderstorms, and flash floods.
  • A path from demonstration to operations for systems designed to run in the cloud.

Warn-on-Forecast has been operating in demonstration mode, but NWS Meteorological Development Laboratory director Richard Bandy said the lack of an operational cloud environment had complicated putting it into production. Moving the broader production suite to the cloud gives the agency a path toward operationalizing projects built for that setting.

A phased migration, not an immediate switch

NWS plans to reduce risk with a smaller early-access environment before testing performance at final scale. The early-2027 target is therefore a milestone for infrastructure readiness, not evidence that every cloud-designed forecasting project will be operational by then. The immediate test is whether the agency can validate performance while moving a system that underpins its model suite.

Sources

  1. fedscoop.comNWS transition to cloud supercomputing could help fuel AI weather prediction

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