Articles | Volume 17, issue 1
Nat. Hazards Earth Syst. Sci., 17, 17–30, 2017

Special issue: Situational sea awareness technologies for maritime safety...

Nat. Hazards Earth Syst. Sci., 17, 17–30, 2017

Research article 05 Jan 2017

Research article | 05 Jan 2017

Data assimilation of Argo profiles in a northwestern Pacific model

Zhaoyi Wang1,3, Andrea Storto2, Nadia Pinardi2, Guimei Liu1,3, and Hui Wang1,3 Zhaoyi Wang et al.
  • 1National Marine Environmental Forecasting Center of China (NMEFC), Beijing, 100081, China
  • 2Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC), 40139 Bologna, Italy
  • 3Key laboratory of Research on Marine Hazards Forecasting, National Marine Environmental Forecasting Center, Beijing, 100081, China

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.

Final-revised paper