Abstract
Abstract. Based on a novel estimation of background-error covariances for assimilating Argo profiles, an oceanographic three-dimensional variational (3DVAR) data assimilation scheme was developed for the northwestern Pacific Ocean model (NwPM) for potential use in operational predictions and maritime safety applications. Temperature and salinity data extracted from Argo profiles from January to December 2010 were assimilated into the NwPM. The results show that the average daily temperature (salinity) root mean square error (RMSE) decreased from 0.99 °C (0.10 psu) to 0.62 °C (0.07 psu) in assimilation experiments throughout the northwestern Pacific, which represents a 37.2 % (27.6 %) reduction in the error. The temperature (salinity) RMSE decreased by ∼ 0.60 °C ( ∼ 0.05 psu) for the upper 900 m (1000 m). Sea level, temperature and salinity were in better agreement with in situ and satellite datasets after data assimilation than before. In addition, a 1-month experiment with daily analysis cycles and 5-day forecasts explored the performance of the system in an operational configuration. The results highlighted the positive impact of the 3DVAR initialization at all forecast ranges compared to the non-assimilative experiment. Therefore, the 3DVAR scheme proposed here, coupled to ROMS, shows a good predictive performance and can be used as an assimilation scheme for operational forecasting.
Highlights
Operational prediction systems for forecasting waves, currents and sea level variations are fundamental for maritime safety, serving a wide range of applications such as search and rescue, oil spill, tourism-oriented bulletins, climate change monitoring and many other downstream applications, eventually through downscaling the forecasts into coastal hydrodynamic models
The data assimilation system was implemented in an eddy-resolving configuration of the northwestern Pacific from January to December 2010
A specific feature of our 3DVAR system is the separation of the background-error covariance matrix into vertical and horizontal modes in order to reduce the size of the data assimilation problem
Summary
Operational prediction systems for forecasting waves, currents and sea level variations are fundamental for maritime safety, serving a wide range of applications such as search and rescue, oil spill, tourism-oriented bulletins, climate change monitoring and many other downstream applications, eventually through downscaling the forecasts into coastal hydrodynamic models. The operational northwestern Pacific Ocean model (NwPM) is a regional model at the CGOFS, which is based on the Regional Ocean Model System (ROMS), a free-surface, primitive equation ocean circulation model formulated using terrain-following coordinates It produces daily analyses and forecasts, up to 5 days ahead, of the main ocean variables and provides boundary conditions for the East China Sea model (ECSM) and the South China Sea model (SCSM). In this study we adapted an oceanographic three-dimensional variational data assimilation scheme called OceanVar (Dobricic and Pinardi, 2008) to the ROMS model in order to assimilate temperature and salinity (T /S) measurements from Argo profiles into the CGOFS northwestern Pacific Ocean forecasting system.
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