Articles | Volume 17, issue 4
Research article
26 Aug 2021
Research article |  | 26 Aug 2021

Can assimilation of satellite observations improve subsurface biological properties in a numerical model? A case study for the Gulf of Mexico

Bin Wang, Katja Fennel, and Liuqian Yu

Data sets

Ocean forecasting in terrain-following coordinates : Formulation and skill assessment of the Regional Ocean Modeling System D. B. Haidvogel, H. Arango, W. P. Budgell, B. D. Cornuelle, E. Curchitser, E. D. Lorenzo, K. Fennel, W. Geyer, A. Hermann, L. Lanerolle, J. Levin, J. C. McWilliams, A. J. Miller, A. M. Moore, T. M. Powell, A. F. Shchepetkin, C. R. Sherwood, R. P. Signell, J. C. Warner, and J. Wilkin

Assess- 50 ment of Data Assimilative Ocean Models in the Gulf of Mexico Using Ocean Color E. Chassignet, H. Hurlburt, O. Smedstad, C. Barron, D. Ko, R. Rhodes, J. Shriver, A. Wallcraft, and R. Arnone

Ocean currents, temperatures, and others measured by drifters and profiling floats for the Lagrangian Ap- proach to Study the Gulf of Mexico Deep Circulation project 2011-07 to 2015-06 (NCEI Accession 0159562) P. Hamilton and Leido

Short summary
We demonstrate that even sparse BGC-Argo profiles can substantially improve biogeochemical prediction via a priori model tuning. By assimilating satellite surface chlorophyll and physical observations, subsurface distributions of physical properties and nutrients were improved immediately. The improvement of subsurface chlorophyll was modest initially but was greatly enhanced after adjusting the parameterization for light attenuation through further a priori tuning.