Accession Number:

AD1067667

Title:

Operational Ocean Data Assimilation

Descriptive Note:

[Technical Report, Book Chapter]

Corporate Author:

NAVAL RESEARCH LAB STENNIS DETACHMENT STENNIS SPACE CENTER MS

Personal Author(s):

• Jacobs, Gregg A
• Barron, Charlie N
• Blain, Cheryl A
• Carrier, Matthew J
• D'Addezio, Joseph M
• Helber, Robert W
• May, Jackie C
• Ngodock, Hans E
• Osborne, John J
• Orzech, Mark D
• Rowley, Clark D
• Souopgui, Innocent
• Smith, Scott R
• Veeramony, Jay
• Yaremchuk, Max

Report Date:

2018-09-03

Pagination or Media Count:

34

Abstract:

Operational ocean data assimilation is necessary to continually correct and maintain accurate ocean forecasts. The primary GODAE objective was prediction of mesoscale eddies, and the science community has successfully addressed this issue. Here, we examine a data assimilation process for this problem, beginning with a generalized solution of 4D variation assimilation 4DVar so that assumptions will be clear as we reduce to a 3DVar that is often used operationally. The primary difficulty lies in specifying the covariances that relate variables at different locations in space and time. Simplifications are applied to provide covariances that sufficiently describe the relations and are computationally feasible. Some deficiencies are introduced through the assumptions leading to the 3DVar and within the covariances, and this points to areas of future research. Prior assumptions were predicated on the expected observing systems and numerical model capabilities, which were all consistent with prediction of mesoscale features. We believe that numerical models and observations will surpass present capability, and there is strong motivation to move data assimilation forward to achieve prediction at scales not now feasible.

Descriptors:

• boundaries
• energy
• frequency
• mathematical filters
• oceanography
• oceans
• standards
• statistics
• databases
• dynamics
• energy transfer
• information science
• physics
• surface temperature
• trajectories
• accuracy
• altimeters
• covariance
• data science
• geopotential

Subject Categories:

• Physical and Dynamic Oceanography
• Information Science

Distribution Statement:

[A, Approved For Public Release]