Local Ensemble Transform Kalman Filtering with an AGCM at a T159/L48 Resolution

By Takemasa Miyoshi, S. Yamane. 2007

300 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change, Oceanography

View Paper: https://www.semanticscholar.org/paper/780f5c76013848b253ea5f96100db5b45358bc9c

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

1. Takemasa Miyoshi
2. Jeffrey L. Anderson
3. Yan Chen
4. Marc Bocquet
5. Eugenia Kalnay
6. Dean S. Oliver
7. J. Whitaker
8. Hong Li

In Global and Planetary Change

1. Takemasa Miyoshi
2. Jeffrey L. Anderson
3. Eugenia Kalnay
4. Marc Bocquet
5. J. Whitaker
6. Dean S. Oliver
7. Hong Li
8. Yan Chen

In Oceanography

1. J. Whitaker
2. Masaru Kunii
3. Yan Chen
4. Masaki Satoh
5. Hirofumi Tomita
6. Toshiyuki Imamura
7. Keiichi Kondo
8. Yoichiro Ota

Citations in the corpus

• The Non-hydrostatic Icosahedral Atmospheric Model: description and development (2014, result): “Previous studies have applied the LETKF to regional and global atmospheric models and have shown promising results (Miyoshi and Aranami 2006; Miyoshi and Yamane 2007).”
• Simultaneous estimation of covariance inflation and observation errors within an ensemble Kalman filter (2009, background): “scheme above. In fact, Miyoshi and Yamane (2007) reported the adaptive inflation did not work”
• Adaptive covariance relaxation methods for ensemble data assimilation: experiments in the real atmosphere (2017, methodology): “(3) The LETKF computes Eqs (1) and (2) efficiently by an eigenvalue decomposition of the matrix inside the curly brackets of Eq.”
• Localization and regularization for iterative ensemble smoothers (2017, background): “This is consistent with what has been observed with local data [16, 22].”
• The 10,240‐member ensemble Kalman filtering with an intermediate AGCM (2014, methodology): “The existing LETKF code by Miyoshi [2005] and follow-on updates [Miyoshi and Yamane, 2007; Miyoshi et al., 2007; Miyoshi, 2011] have been modified by taking advantage of the efficient eigenvalue decomposition software “EigenExa,” particularly optimized for the K computer…”
• Mesoscale Data Assimilation for a Local Severe Rainfall Event with the NHM–LETKF System (2014, methodology): “The LETKF scheme has been applied to atmospheric models (e.g., Miyoshi and Aranami 2006; Miyoshi and Yamane 2007; Schutgens et al. 2010; Kang et al. 2011; Miyoshi and Kunii 2012), ocean models (e.g., Shchepetkin and McWilliams 2005; Penny 2011), and even a Martian atmosphere model (Hoffman et al.…”
• The Gaussian Approach to Adaptive Covariance Inflation and Its Implementation with the Local Ensemble Transform Kalman Filter (2011, methodology): “The corresponding radius of in- fluence is 2 ffiffiffiffiffiffiffiffiffi 10/3 p times the one standard deviation radius (Gaspari and Cohn 1999; Hamill et al. 2001; Miyoshi and Yamane 2007), so that the present localization setting considers observations up to 10 grid points away.”
• Ensemble Data Assimilation with the NCEP Global Forecast System (2008, methodology): “This “observationerror localization,” tested in the LETKF by Miyoshi and Yamane (2007) , allows the influence of observations to decay smoothly to zero at a specified distance from an analysis grid point without increasing the effective number of degrees of freedom in the ensemble (as is the case w”