JpGU-AGU Joint Meeting 2017

講演情報

[EE] ポスター発表

セッション記号 M (領域外・複数領域) » M-GI 地球科学一般・情報地球科学

[M-GI28] [EE] Data assimilation: A fundamental approach in geosciences

2017年5月22日(月) 15:30 〜 17:00 ポスター会場 (国際展示場 7ホール)

コンビーナ:中野 慎也(情報・システム研究機構 統計数理研究所)、藤井 陽介(気象庁気象研究所)、宮崎 真一(京都大学理学研究科)、三好 建正(理化学研究所計算科学研究機構)

[MGI28-P02] Observation impact on the medium and the long-term range forecast on an eddy-resolving ocean forecast system based on ROMS

*瀬藤 聡1黒田 寛5高橋 大介5東屋 知範5奥西 武2長谷川 大介2筧 茂穂2金子 仁2清水 勇吾1日高 清隆1廣江 豊1山崎 恵市1亀田 卓彦1青木 一宏1種子田 雄3森永 健司4岡崎 誠4増島 雅親4西本 篤史1 (1.国立研究開発法人水産研究・教育機構中央水産研究所、2.国立研究開発法人水産研究・教育機構東北区水産研究所、3.国立研究開発法人水産研究・教育西海区水産研究所、4.国立研究開発法人水産研究・教育機構国際水産資源研究所、5.国立研究開発法人水産研究・教育機構北海道区水産研究所)

Japan domestic fisheries research institutions constitute a horizontally close-arranged monitoring system around the coastal and the offshore region of Japan in the western North Pacific. Most of these hydrographic data (hereafter FRDATA) have been introduced for an eddy-resolving ocean forecast system, named by the FRA-ROMS (Kuroda et al. 2016, Ishii et al., 2016, Kodama et al. 2015), which developed by Japan Fisheries Research and Education Agency and is based on ROMS (Regional Ocean Modeling System) assimilated with satellite SSH/SST and hydrographic data such as GTSPP and FRDATA. The assimilation scheme, which is founded on the MOVE system developed by the Japan Meteorological Research Institute, is characterized by the following three steps; (1) minimizing the nonlinear cost functions by using a pre-conditioning method, (2) analyzing temperature-salinity profiles by using vertical coupled EOF modes, and (3) assimilating the data analyzed into an ocean model, namely, making reliable reanalysis data by using the Incremental Analysis Updates method. We assessed the relative impact of FRDATA by comparing modeled fields with assimilated and withheld FRDATA. The coastal FRDATA enabled to finely represent hydrographic structures in the coastal region and to remarkably improve the coastal forecast on the medium range forecast (about 1-month). On the other hand, the offshore FRDATA contributed to improve the accuracy not only on the long-term forecast (about 2-months) of some synoptic phenomena (e.g. the Kuroshio) but also of some coastal changes caused by such the phenomena.