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Advances in Meteorology
Volume 2017 (2017), Article ID 9315601, 13 pages
https://doi.org/10.1155/2017/9315601
Research Article

A Potential Density Gradient Dependent Analysis Scheme for Ocean Multiscale Data Assimilation

1Key Laboratory of Marine Environmental Information Technology, State Oceanic Administration, National Marine Data and Information Service, Tianjin 300171, China
2NOAA/Earth System Research Laboratory, Boulder, CO, USA
3Key Laboratory of Physical Oceanography, MOE China, Ocean University of China, Qingdao 266100, China
4Qingdao National Laboratory for Marine Science and Technology, Qingdao 266100, China

Correspondence should be addressed to Jinkun Yang

Received 21 May 2017; Revised 20 September 2017; Accepted 3 October 2017; Published 5 December 2017

Academic Editor: Stefania Bonafoni

Copyright © 2017 Hongli Fu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This study addresses how to maintain oceanic mixing along potential density surface in ocean data assimilation (ODA). It is well known that the oceanic mixing across the potential density surface is much weaker than that along the potential density surface. However, traditional ODA schemes allow the mixing across the potential density surface and thus may result in extra assimilation errors. Here, a new ODA scheme that uses potential density gradient information of the model background to rescale observational adjustment is designed to improve the quality of assimilation. The new scheme has been tested using a regional ocean model within a multiscale 3-dimensional variational framework. Results show that the new scheme effectively prevents the excessive unphysical projection of observational information in the direction across potential density surface and thus improves assimilation quality greatly. Forecast experiments also show that the new scheme significantly improves the model forecast skills through providing more dynamically consistent initial conditions