The 10,240‐member ensemble Kalman filtering with an intermediate AGCM
By Takemasa Miyoshi, Keiichi Kondo, Toshiyuki Imamura. 2014
94 citations recorded by the source corpus.
Subfields: Atmospheric Science, Global and Planetary Change, Oceanography
View Paper: https://www.semanticscholar.org/paper/f211ae6939c66873fbf216d4752418ee0c5c0252
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Atmospheric Science
- Takemasa Miyoshi
- Martin Leutbecher
- J. Poterjoy
- Stephen G. Penny
- Mohamad E. Gharamti
- Marc Bocquet
- Masaru Kunii
- Manuel Pulido
In Global and Planetary Change
- Takemasa Miyoshi
- Martin Leutbecher
- J. Poterjoy
- Stephen G. Penny
- Mohamad E. Gharamti
- Marc Bocquet
- Masaru Kunii
- Manuel Pulido
In Oceanography
Citations in the corpus
- A local particle filter for high-dimensional geophysical systems (2015, background): “Perhaps counterintuitively, these limitations arise due to increased computational resources, and have already become challenges at the RIKEN Advanced Institute for Computational Science (AICS, e.g., Miyamoto et al., 2013; Miyoshi et al., 2014, 2015).”
- Ensemble size: How suboptimal is less than infinity? (2018, methodology): “Miyoshi et al. (2014) summarize work with an ensemble Kalman filter applied to an intermediate complexity atmospheric model.”
- A Localized Particle Filter for High-Dimensional Nonlinear Systems (2016, background): “For example, Miyoshi et al. (2014) show that ensembles of ;1000 members can provide accurate representations of nonGaussian prior probabilities that occur for atmospheric quantities such as moisture.”