Description:
Background
The application of AI, including machine learning (ML), has already demonstrated significant advances in research and development, with subsequent improvements in performance at greatly reduced costs and compute time for various NOAA mission areas, such as deep-sea exploration, habitat characterization, fisheries assessments, environmental modeling, and interpretation of earth science observations. The use of ML algorithms has enhanced automated detection capabilities and operational efficiencies during aerial and underwater surveys from ships and autonomous platforms to assess the abundance of marine mammal and fish populations.
ML has also advanced data assimilations and forecast modeling, and specific examples of improvements include quality control of environmental or satellite observations, physical parameterization for environmental modeling including ecosystems, physical and computational performance of numerical earth system models, aiding weather warnings and associated Impact-based Decision Support Services, operations of unmanned systems for a wide range of environmental observations, and supporting partners in wildfire detection and movement.
Research Priorities:
Examples of appropriate subtopics for research applications from small businesses include, but are not limited to the following:
AI for Oceans, Coasts, and Fisheries: Innovative computational approaches to help interpret genetic variation of marine mammal and fish populations and recognize relationships with environmental data.
AI for Earth Observation Tools: AI-ready data and tools for reliable and efficient processing, interpretation, and utilization of earth observations.
AI for Space Data Systems: Cost-effective relay of space data over very long path lengths in real time through improvements to all aspects of materials and structures in antenna design and dual use of other spacecraft subsystems.
