[M-GI24] Data assimilation: A fundamental approach in geosciences

[MGI24-P08] Observing System Experiments (OSEs) with JMA’s operational seasonal prediction system JMA/MRI-CPS3 for participating the SynObs Flagship OSEs

*Ichiro Ishikawa¹, Yosuke Fujii^1,2, Yuhei Takaya^1,2, Shoji Hirahara¹ (1.Meteorological Research Institute, 2.Numerical Prediction Development Center)

Keywords:ocean observation, data assimilation, seasonal prediction, coupled model, ocean prediction

Ocean observations are essential for ocean and atmospheric forecasting on a variety of time scales ranging from a few days to seasons. Maintaining and developing the ocean observing network, however, requires significant human and financial resources. Therefore, the ocean observation network should be designed to efficiently acquire effective observation data, and their adequacy should be continually evaluated. As a project of the United Nations Decade of Ocean Science for Sustainable Development, Synergistic Observing Network for Ocean Prediction (SynObs) is planning and conducting an observing system experiment named “SynObs Flagship OSE”. The impact of observations evaluated from the Observing System Experiment (OSE) depends on the models and data assimilation systems used, and it is, therefore, desirable to use multiple data assimilation and forecasting systems to obtain robust conclusions, and such international cooperation is very important.
As part of the SynObs Flagship OSE effort, the Meteorological Research Institute is conducting OSEs using the current JMA seasonal forecasting system, JMA/MRI-CPS3 (hereafter CPS3). Currently, we are conducting observing system experiments of ocean data assimilation using the global ocean data assimilation system MOVE/MRI.COM-G3A, which is a component of CPS3. Calculations for two experiments have been completed; one using all available observation data for assimilation except some Argo data withheld for validation (CNTL), and the other using only satellite sea surface temperature and altimetry data (NoInsitu). We also plan to conduct OSEs to study the impacts of the Argo, mooring system, and satellite altimeter. The comparison of CNTL and NoInsitu results suggests that satellite data alone provides a good analysis of water temperature in the tropical Pacific, but for the analysis of salinity, in situ salinity observations are essential. In the poster, we will report on the results of ocean data assimilation, including results from other OSEs. Future experiments targeting ocean monitoring using the higher resolution MOVE/MRI.COM-G3F and experiments for intra-seasonal to seasonal forecasting and sea state prediction using coupled models are also planned, and these plans will also be presented.