TY - JOUR

T1 - How Efficient IsModel-to-Model Data Assimilation atMitigating Atmospheric Forcing Errors in a Regional Ocean Model?

AU - Shapiro, Georgy

AU - Poovadiyil, Salim

PY - 2023/4/27

Y1 - 2023/4/27

N2 - This paper examines the efficiency of a recently developed Nesting with Data Assimilation (NDA) method at mitigating errors in heat and momentum fluxes at the ocean surface coming from external forcing. The analysis uses a set of 19 numerical simulations, all using the same ocean model and exactly the same NDA process. One simulation (the reference) uses the original atmospheric data, and the other eighteen simulations are performed with intentionally introduced perturbations in the atmospheric forcing. The NDA algorithm uses model-to-model data assimilation instead of assimilating observations directly. Therefore, it requires a good quality, although a coarser resolution data assimilating parent model. All experiments are carried out in the South East Arabian Sea. The variables under study are sea surface temperature, kinetic energy, relative vorticity and enstrophy. The results show significant improvement in bias, root-mean-square-error, and correlation coefficients between the reference and the perturbed models when they are run in the data assimilating configurations. Residual post-assimilation uncertainties are similar or lower than uncertainties of satellite based observations. Different length of DA cycle within a range from 1 to 8 days has little effect on the accuracy of results.

AB - This paper examines the efficiency of a recently developed Nesting with Data Assimilation (NDA) method at mitigating errors in heat and momentum fluxes at the ocean surface coming from external forcing. The analysis uses a set of 19 numerical simulations, all using the same ocean model and exactly the same NDA process. One simulation (the reference) uses the original atmospheric data, and the other eighteen simulations are performed with intentionally introduced perturbations in the atmospheric forcing. The NDA algorithm uses model-to-model data assimilation instead of assimilating observations directly. Therefore, it requires a good quality, although a coarser resolution data assimilating parent model. All experiments are carried out in the South East Arabian Sea. The variables under study are sea surface temperature, kinetic energy, relative vorticity and enstrophy. The results show significant improvement in bias, root-mean-square-error, and correlation coefficients between the reference and the perturbed models when they are run in the data assimilating configurations. Residual post-assimilation uncertainties are similar or lower than uncertainties of satellite based observations. Different length of DA cycle within a range from 1 to 8 days has little effect on the accuracy of results.

KW - data assimilation

KW - Indian Ocean

KW - uncertainty

KW - kinetic energy

KW - sea surface temperature

U2 - 10.3390/jmse11050935

DO - 10.3390/jmse11050935

M3 - Article

VL - 11

JO - Journal of Marine Science and Engineering

JF - Journal of Marine Science and Engineering

IS - 5

ER -