Assimilating operational SST and sea ice analysis data into an operational circulation model for the coastal seas of China

Abstract

The prediction of sea surface temperature (SST) is an essential task for an operational ocean circulation model. A sea surface heat flux, an initial temperature field, and boundary conditions directly affect the accuracy of a SST simulation. Here two quick and convenient data assimilation methods are employed to improve the SST simulation in the domain of the Bohai Sea, the Yellow Sea and the East China Sea (BYECS). One is based on a surface net heat flux correction, named as Qcorrection (QC), which nudges the flux correction to the model equation; the other is ensemble optimal interpolation (EnOI), which optimizes the model initial field. Based on such two methods, the SST data obtained from the operational SST and sea ice analysis (OSTIA) system are assimilated into an operational circulation model for the coastal seas of China. The results of the simulated SST based on four experiments, in 2011, have been analyzed. By comparing with the OSTIA SST, the domain averaged root mean square error (RMSE) of the four experiments is 1.74, 1.16, 1.30 and 0.91°C, respectively; the improvements of assimilation experiments Exp2, 3 and 4 are about 33.3%, 25.3%, and 47.7%, respectively. Although both two methods are effective in assimilating the SST, the EnOI shows more advantages than the QC, and the best result is achieved when the two methods are combined. Comparing with the observational data from coastal buoy stations, show that assimilating the high-resolution satellite SST products can effectively improve the SST prediction skill in coastal regions. . Assimilating operational SST and sea ice analysis data into an operational circulation model for the coastal seas of China. Acta Oceanologica Sinica,

Comparison and validation of global and regional ocean forecasting systems for the South China Sea

“…After the hindcast run, the system conducted an assimilation run in 2012 using the EnOI method; the alongtrack SLA data from AVISO had been assimilated as the observations with a 7-day time window. Details of the EnOI applied in SCSOFS can be referred to in Ji et al (2015). The daily-averaged assimilated results were archived during the assimilation run, and were compared and validated in this paper.…”

“…Therefore, SST error is a crucial criterion of the numerical model skill, especially for an operational ocean circulation model. In fact, the SST simulation error is affected by several factors, for example the limitation of physical model, the surface atmospheric forcing conditions, the bias of initial field, and the uncertainty from the open boundary, as pointed out by Ji et al (2015). Although the SST data have been assimilated into both MO and SCSOFS, the assimilated SST still has some errors for both systems.…”

Improvements in the regional South China Sea Operational Oceanography Forecasting System (SCSOFSv2)

“…In SCSOFSv1, the anomalies are computed by subtracting a 10-year average from long-term (typically 10 years) model free-run snapshots with a 5 d interval for the ocean state, i.e., the sea surface height and three-dimensional temperature, salinity, zonal velocity, and meridional velocity. In addition, the ensemble is selected within a 60 d window around the target assimilation date from each year, resulting in a total of about 130 members (Ji et al, 2015;Zhu et al, 2016). However, in SCSOFSv2, a Hanning low-pass filter is employed to create the running mean according to Lellouche et al (2013) in order to obtain the intra-seasonal variability of the ocean state.…”

The improvements to the regional South China Sea Operational Oceanography Forecasting System (SCSOFSv1)

“…sea surface height and three-dimensional temperature, salinity, zonal velocity, and meridional velocity. And the ensemble is selected within a 60 d window around the target assimilation date from each year, adding up to about 130 members in total (Ji et al, 2015;Zhu et al, 2016). However, in SCSOFSv2, a Hanning low-pass filter is employed to create running mean according to Lellouche et al (2013) would be decreased from 7 days to 1.5 hours by using FGAT.…”