日本地球惑星科学連合2018年大会

講演情報

[EE] ポスター発表

セッション記号 A (大気水圏科学) » A-AS 大気科学・気象学・大気環境

[A-AS01] 高性能スーパーコンピュータを用いた最新の大気科学

2018年5月20日(日) 10:45 〜 12:15 ポスター会場 (幕張メッセ国際展示場 7ホール)

コンビーナ:瀬古 弘(気象研究所)、小玉 知央(独立行政法人海洋研究開発機構)、滝川 雅之(独立行政法人海洋研究開発機構、共同)、三好 建正(理化学研究所計算科学研究機構)

[AAS01-P03] Accounting for the observation error correlation in data assimilation

★Invited Papers

*寺崎 康児1三好 建正1 (1.理化学研究所計算科学研究機構)

キーワード:データ同化、観測誤差相関、条件数、Reconditioning

It is known that some observations such as satellite radiances have the spatial and inter-channel error correlations. The current data assimilation systems in the operational centers mostly neglect the observation error correlations. It is important to account for the observation error correlations to effectively draw the information from the observation “BigData.”

In this study, we developed a method to account for the observation error correlations in the local ensemble transform Kalman filter (LETKF: Hunt et al. 2007), and performed idealized experiments with the Lorenz-96 model (Lorenz et al. 1998) and the non-hydrostatic icosahedral atmospheric model (NICAM: Satoh et al. 2013). The condition number of the observation error covariance matrix (R matrix), or the ratio of the maximum and minimum eigenvalues, is essential for the stable performance of the LETKF. Reconditioning can make the R matrix well-conditioned by adding a small constant to all the diagonal terms of the R matrix. We examined that the reconditioning not only stabilizes the LETKF but also greatly improves the analysis accuracy by including the observation error correlations.