Advanced Data Assimilation into Ocean Models
This research aims to develop and test a flexible data assimilation system using advanced techniques like 3DVar, 4DVar, and Kalman filtering to improve ocean model accuracy.
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 (Application Deadline)
Funding Amounts: $86,962 stipend plus $3,000 travel allowance; relocation and health insurance benefits available.
Summary: Fellowship to develop and evaluate advanced data assimilation systems for ocean models using techniques like 3DVar, 4DVar, and ensemble Kalman filtering to improve ocean environment analysis and prediction accuracy.
Key Information: Open to U.S. citizens and permanent residents; postdoctoral applicants only; research conducted at Naval Research Laboratory, Stennis Space Center, MS.
Description
This fellowship opportunity at the Naval Research Laboratory (NRL) supports research focused on implementing and evaluating a flexible, advanced data assimilation system for ocean models at basin, regional, and high-resolution limited-area scales. The system aims to assimilate diverse oceanographic observations such as sea surface temperature (SST), sea surface height (SSH), acoustic Doppler current profiler (ADCP) data, autonomous underwater vehicles (AUVs), unmanned underwater vehicles (UUVs), and high-frequency (HF) radar data into numerical ocean models with varying resolutions and geographic coverage.
Current operational data assimilation methods primarily use Optimal Interpolation (OI), which has limitations in handling multiple observation types and correcting significant error sources in nested model systems, such as initial and boundary conditions. This research seeks to advance assimilation capabilities by developing systems employing variational techniques, specifically Three-Dimensional Variational (3DVar) and Four-Dimensional Variational (4DVar) methods, adapted from meteorological applications. The 4DVar approach requires the development of tangent linear and adjoint models of the ocean system. Additionally, ensemble-based Kalman filtering techniques are considered to enhance assimilation performance.
The goal is to improve the accuracy of ocean environment analyses and forecasts, which are critical for scientific understanding and operational applications.
