[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

[AAS01-06] Model Parameter Estimation with Data Assimilation Using NICAM-LETKF

★Invited Papers

*小槻 峻司¹、佐藤 陽祐²、寺崎 康児¹、八代 尚¹、富田 浩文¹、佐藤 正樹³、三好 建正¹ (1.国立研究開発法人　理化学研究所　計算科学研究センター、2.名古屋大学大学院　工学研究科、3.東京大学　大気海洋研究所)

キーワード：モデルパラメータ推定、大規模凝結、アンサンブルカルマンフィルタ、雲水量、放射収支、NICAM-LETKF

This study aims to improve forecasts of numerical weather prediction (NWP) models by optimizing model parameters with data assimilation. Kotsuki et al. (2018a, JGR) succeeded in improving global precipitation forecasts at 112-km-resolution NICAM (Nonhydrostatic ICosahedral Atmospheric Model) by estimating a parameter called B1 of Berry (1967)’s large-scale condensation scheme using satellite-observed precipitation data and the Local Ensemble Transform Kalman Filter (LETKF).

Extending the previous study, this study explores to improve the forecasts further using other satellite observations. This study estimates the parameter B1 as a global-constant parameter with cloud liquid water (CLW) data observed by GCOM-W/AMSR2. The parameter estimation successfully reduces excessive bias in CLW although precipitation forecasts are degraded. In addition, this study extends to estimate spatial distributions of the B1 parameter. The spatially-varying B1 parameter shows the best agreement to the spatial pattern of observed LWP. This presentation will include the most recent progress up to the time of the meeting.