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

[MGI24-P07] Intercomparison and ensemble project of coastal ocean prediction models in Japan

*Naoki Hirose¹, Shuichi Watanabe², Shoichiro Kido³, Shun Ohishi⁴, Nariaki Hirose⁵, Takashi Sakamoto⁶, Teiji In² (1.Research Institute for Applied Mechanics, Kyushu University, 2.Japan Marine Science Foundation, 3.Japan Agency for Marine-Earth Science and Technology, 4.RIKEN, 5.Meteorological Research Institute, Japan Meteorological Agency, 6.Ocean Eyes)

Keywords:ocean model, data assimilation, coastal ocean dynamics, multi-model ensemble

Ocean data assimilation and prediction models have been consistently advanced and become more and more popular these days. But the variety and discrepancy of systems might lead the user oscillations. Thus we decided to start intercomparison and ensemble project of most coastal ocean prediction models in Japan including DREAMS, MOVE, JCOPE, LORA, and SEAoME. First, we select a few small zones to closely compare the differences among these ocean DA models. The intercomparison is more important than the comparison to observation data at the initial phase. One purpose of this intercomparison is to define metrics to effectively measure the coastal ocean dynamics. Second, in-situ and remotely-sensed measurement data are used to find the problems of each ocean model. We probably need to repeat the forward and inverse estimation processes to improve the forecast performance of individual models. Third, the ensemble combinations are finally conducted and the multi-model ensemble prediction will be provided with the support of Japan Marine Science Foundation. The small experimental zones will be extended to all coastal waters along the Japanese archipelago after 2028.