Ocean Modeling Data Assimilation Researcher

Peraton is seeking a Ocean Modeling Data Assimilation Researcher at Stennis Space Center in MS.  The ocean has historically been under-observed, and thus instruments are steadily being designed to provide more observations with higher resolution in both space and time. One such instrument is NASA’s wide-swath Surface Water and Ocean Topography (SWOT) altimetry satellite which will provide sea surface height (SSH) observations at a 1-2km spatial resolution within a ~120km wide swath over a 21 day repeat orbital cycle. This unprecedented high spatial resolution poses a challenge to the Navy’s current operational data assimilation (DA) systems, which were designed for sparse datasets.

This project will perform direct SSH assimilation using NCODA-4DVAR in an operational, Navy relevant domain. The capability of a 4DVAR system to correct over time and space, along with the dynamics of the ocean model, will allow information from assimilated SSH observations to propagate to the subsurface by the dynamics of the tangent linear and adjoint of the model. The advantage of this approach is a substantial increase in the number of SSH observations assimilated into the model, which will significantly impact the accuracy of our ocean forecasts.

Responsibilities will include:

• Change NCODA schema for processing SSH observations to account for temporal density over a time window as opposed to a single time.
• Develop a procedure one for computing the mean model SSH.
• Develop a procedure for online de-tiding of the model.
• Evaluate the direct assimilation of SSH observations within a 4DVAR DA system, creating a substantial increase in the number of SWOT SSH observations utilized, and applying the increase of temporally and spatially dense SSH observations assimilated to reduce the error within an ocean forecast.
  • Measured the number of SSH observations assimilated using the new method and compare against the number of SSH observations used in the current operational method through Improved Synthetic Observation Profiles (ISOP).
  • Assess the accuracy of forecasts produced by direct SSH assimilation by examining the Root Mean Square Error against a forecast produced with ISOP.
  • Investigate the effects on computational expense when implementing direct SSH assimilation.
• Present results at academic conferences.
• Publish results in peer reviewed journals.

PhD in Mathematics or an Applied Science Field

• Must have coding experience in MATLAB, Python, Fortran, and Julia.
• Must have experience with Linux, Shell Scripting, VSCode, and High Performance Computing (HPC).
• Must be knowledgeable in Numerical Methods, Data Analysis, Data Assimilation, Ocean Modeling.
• Must be familiar with U.S. Navy standard ocean models.
• Must have the ability to obtain a Secrete Clearance
• Experience working in a Department of Defense research facility strongly preferred.

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