With the global spread of the Argo floats and development of high- resolution ocean models, various global ocean reanalysis datasets have been released.
However, their regions are limited to the North Pacific north of 10°N, and their assimilation methods are a Kalman Filter and variational assimilation methods excluding an Ensemble Kalman Filter.
Thus, it is one of challenging topics in the ocean data assimilation field to construct ocean data assimilation system with an Ensemble Kalman Filter around the Asia-Oceania region for contributing to coastal environmental monitoring and typhoon researches.
We have established a one-way nest high-resolution ocean data assimilation system based on a Local Ensemble Transform Kalman Filter (Hunt et al. 2007) with 10 ensemble members at 1-day interval.
The coastal southeast Asian [98°–115°E, 0°–22°N] (large-scale western Pacific [95°E–165°W, 50°S–50°N]) region has been modeled at a spatial resolution of 1/36° (1/12°) and 50 layers with an intent to be applied for fishery and marine environmental monitoring (tropical cyclone studies).
Sea surface temperatures (SSTs) measured by an infrared sensor of Himawari-8 and a microwave sensor of GCOM-W, sea surface salinity derived from SMOS and SMAP satellites, satellite sea surface height, and in- situ temperatures and salinity from GTSPP and AQC Argo dataset have been assimilated.
To avoid generation of spurious gravity waves and maintain dynamical balance, the incremental analysis update (IAU) filter (Bloom et al. 1996), relaxation-to-priori spread (RTPS; Whitaker and Hamill 2012) rather than a multiplicative covariance error inflation scheme, and an adaptive observation error inflation (AOEI; Minamide and Zhang 2017) method are implemented to the system.
We will show processes constructing the ocean data assimilation system and results of low salinity water transport from the Mekong river in the southeast Asia system and the SST cooling with the passage of tropical cyclones in the western Pacific system.