Optimization of unmanned observing swarms supporting assimilative ocean forecast systems
This project optimizes UUV swarms to improve ocean forecasts by assimilating data from various observing systems, enhancing accuracy and probabilistic modeling.
This grant is no longer accepting proposals
NRC Research Associateship Programs has archived this opportunity.
Funder: NRC Research Associateship Programs
Due Dates: May 1, 2025
Funding Amounts: $86,962 stipend plus $3,000 travel allowance; relocation assistance available for eligible awardees. Duration typically 2-3 years.
Summary: Supports postdoctoral research optimizing unmanned underwater vehicle swarms for enhanced ocean data assimilation and probabilistic ocean forecasting.
Key Information: Open to U.S. citizens and permanent residents; requires Ph.D. earned within last 5 years; research conducted onsite at Naval Research Laboratory, Stennis Space Center, MS.
Description
This fellowship opportunity at the Naval Research Laboratory (NRL) focuses on advancing ocean data assimilation and probabilistic forecasting by developing control and optimization strategies for swarms of unmanned underwater vehicles (UUVs). The research aims to improve global and regional ocean forecasts of temperature, salinity, and mixed-layer depth by integrating observations from underwater, airborne, and satellite systems into assimilative forecast models.
Key research priorities include:
- Automated targeting for unmanned ocean observing systems.
- Representing forecast uncertainty in ocean and acoustic systems.
- Energy management for persistent observing swarms.
The work supports coupled atmosphere-ocean-ice-wave forecast systems, moving beyond deterministic models to probabilistic ocean forecasting. Long-term goals include transitioning cutting-edge research capabilities to operational forecast centers.
Keywords associated with this research include ocean data assimilation, ocean gliders, autonomous underwater vehicles (AUVs), 3DVAR/4DVAR data assimilation methods, and forward data assimilation.
