NOAA GOMO Funds Eight AI and Data Management Pilots for Ocean Observing

NOAA's Global Ocean Monitoring and Observing programme has announced the awardees for its FY26 Innovation Program covering artificial intelligence and data management pilots. The programme funded eight pilot projects, with an expected total investment of around 1.5 million dollars including partner funding. The pilots will test AI tools for observation mission planning, metadata improvement, quality control of observing system data, data visualisation, and evaluating the impact of observations on forecasts and models.

 

Details of the Announcement

 

NOAA has announced new funding. It covers its Innovation Program. This concerns AI and data management. The programme funded eight pilots. These support ocean observing.

The programme has clear goals. It invests in transformative research. This modernises ocean observing. It supports high-quality research. This informs society about the ocean.

 

The Investment

 

The programme involves substantial funding. The total investment is around 1.5 million dollars. This  includes partner contributions. These come from related programmes. Uncrewed systems operations also contributed.

The funding addresses evolving needs. Ocean observing needs are growing. Data demands are increasing. The pilots respond to this. They test AI across several areas.

 

The Mission Planning Pilots

 

Some pilots focus on mission planning. One develops AI route planning. This covers uncrewed vehicles. It optimises their routing. This maximises safety and value.

These pilots support uncrewed systems. Such systems are reshaping observing. They measure ocean and atmosphere interactions. These are critical to predictions. AI enhances their deployment.

 

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The Observation Impact Pilots

 

Other pilots assess observation impact. One quantifies hurricane forecast impact. It links coverage to forecast errors. This translates insights into decisions. It does so at lower cost.

Another builds a virtual testbed. This simulates sensor loss. It optimises infrastructure investments. It uses an AI-based framework. This replaces expensive physics-based simulations.

 

The Metadata and Quality Pilots

 

Several pilots address data quality. Some focus on metadata generation. This is time-intensive manually. AI could automate parts. This supports data reuse.

Others focus on quality control. One assists Argo quality control. It uses machine learning. This reduces operator time. It also improves data consistency.

 

Significance of the Pilots

 

The pilots advance ocean observing. They span diverse applications. These include planning and metadata. Quality control also features. Data visualisation is included too.

The pilots run over several years. They start this fall. They conclude by fall 2028. They explore AI's potential. This supports modernising ocean observation.