Research field (1)：
Atmospheric and hydrospheric science
Research theme for competitive and other funds (2)：
2021 - 2025 Hybrid data assimilation methods with a particle filter for forecasts of extreme weather events
2016 - 2020 Development of a multi-scale data assimilation method based on huge ensemble data assimilation
Papers (13)：
Shunji Kotsuki, Takemasa Miyoshi, Keiichi Kondo, Roland Potthast. A Local Particle Filter and Its Gaussian Mixture Extension Implemented with Minor Modifications to the LETKF. Geoscientific Model Development. 2022. 15. 8325-8348
Keiichi KONDO, MIYOSHI, Takemasa. Non-Gaussian statistics in global atmospheric dynamics: a study with a 10 240-member ensemble Kalman filter using an intermediate atmospheric general circulation model. Nonlinear Processes in Geophysics. 2019. 26. 211-225
Sam Hatfield, Peter Düben, Matthew Chantry, Keiichi Kondo, Takemasa Miyoshi, Tim Palmer. Choosing the Optimal Numerical Precision for Data Assimilation in the Presence of Model Error. 2018. 10. 2177-2191
Shu-Chih Yang, Shu-Hua Chen, Keichii Kondo, Takemasa Miyoshi, Yu-Chieng Liou, Yung-Lin Teng, Hui-Ling Chang. Multilocalization data assimilation for predicting heavy precipitation associated with a multiscale weather system. 2017. 9. 1684-1702
Keiichi Kondo, Takemasa Miyoshi, H. L. Tanaka. Parameter Sensitivities of the Dual-Localization Approach in the Local Ensemble Transform Kalman Filter (vol 9, pg 174, 2013). SOLA. 2015. 11. E3-E3
Takemasa Miyoshi, Keiichi Kondo. A Multi-Scale Localization Approach to an Ensemble Kalman Filter (vol 9, pg 170, 2013). SOLA. 2015. 11. E2-E2
