日本地球惑星科学連合2022年大会

講演情報

[E] 口頭発表

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-GI29] Data assimilation: A fundamental approach in geosciences

2022年5月26日(木) 13:45 〜 15:15 104 (幕張メッセ国際会議場)

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)、コンビーナ:藤井 陽介(気象庁気象研究所)、三好 建正(理化学研究所)、コンビーナ:加納 将行(東北大学理学研究科)、座長:三好 建正(理化学研究所)、藤井 陽介(気象庁気象研究所)

14:45 〜 15:00

[MGI29-05] A Multi-scale Localization for the Local Ensemble Transform Kalman Filter with Attenuation of Ensemble Perturbation

*小槻 峻司1,2、Bishop Craig3 (1.千葉大学 環境リモートセンシング研究センター、2.理化学研究所 計算科学研究センター、3.メルボルン大学)

キーワード:データ同化、数値気象予測、マルチスケール、局所化

Recent numerical weather prediction systems have significantly improved medium-range forecasts by implementing hybrid background error covariance, for which climatological (static) and ensemble-based (flow-dependent) error covariance are combined. While the hybrid approach has been investigated mainly in variational systems, Kotsuki and Bishop (2022) constructed hybrid background error covariance for the local ensemble transform Kalman filter (LETKF) by adding collections of climatological perturbations to the forecast ensemble, and succeeded in improving forecast errors using an intermediate global atmospheric model known as SPEEDY. Kotsuki and Bishop (2022) also proposed the new localization method that attenuates the ensemble perturbation (Z-localization) instead of inflating observation error variance (R-localization). While the Z-localization can apply different localization scales for flow-dependent perturbation and climatological static perturbations, the optimal localization for climatological perturbations differed slightly from that for flow-dependent perturbations.
This study extends the previous study, and explores a multi-scale localization using the Z-localization. We generate smoothed ensemble perturbations based on ensemble forecasts, and construct the background error covariance using both original and smoothed ensemble perturbations. Using the Z-localization, larger localization scales are applied for the smoothed ensemble perturbation than that for the original ensemble perturbations. This multi-scale localization with the Z-localization successfully results in smaller forecast errors than the standard LETKF. The multi-scale localization was also combined with the hybrid background error covariance method. This combined approach showed beneficial impacts for reducing forecasts errors. This talk will include the most recent progress up to the time of the conference.