“Variable localization” in an ensemble Kalman filter: Application to the carbon cycle data assimilation
By Ji-Sun Kang, Eugenia Kalnay, Junjie Liu, I. Fung, Takemasa Miyoshi, Kayo Ide. 2011
146 citations recorded by the source corpus.
Subfields: Atmospheric Science, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/289fff2036d283988497e44e0692d0371b05b33b
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
- Masaru Kunii
- Manuel Pulido
- Juan J. Ruiz
- Toshiyuki Imamura
- Keiichi Kondo
- Takemasa Miyoshi
- Martin Leutbecher
- Mohamad E. Gharamti
In Global and Planetary Change
- Masaru Kunii
- Manuel Pulido
- Juan J. Ruiz
- Toshiyuki Imamura
- Keiichi Kondo
- Takemasa Miyoshi
- Martin Leutbecher
- Mohamad E. Gharamti
Citations in the corpus
- Mesoscale Data Assimilation for a Local Severe Rainfall Event with the NHM–LETKF System (2014, methodology): “Kunii and Miyoshi (2012) used the WRF–LETKF model to examine the influence of sea surface temperature (SST) perturbations on analyses.”
- Estimating Model Parameters with Ensemble-Based Data Assimilation: A Review (2013, methodology): “Kang 2009 and Kang et al. 2011 used a parameter estimation technique based on the LETKF and successfully estimated the spatial distribution and seasonal variation of CO2 surface fluxes.”
- The 10,240‐member ensemble Kalman filtering with an intermediate AGCM (2014, methodology): “The 3week 10,240-member data assimilation experiment follows Miyoshi [2005] and a number of follow-on studies under the perfect model scenario [e.g., Kang et al., 2011; Lien et al., 2013; Miyoshi and Kondo, 2013; 30 60 120 240 480 960 1920 3840 7680 2560 5120 10240 T im in g (s ec .)”
- The Gaussian Approach to Adaptive Covariance Inflation and Its Implementation with the Local Ensemble Transform Kalman Filter (2011, background): “Kang (2009) and Kang et al. (2011) found that covariance localization between variables played an essential role in reducing sampling errors in data assimilation of carbon dioxide and the determination of surface carbon fluxes.”
- Data assimilation in the geosciences: An overview of methods, issues, and perspectives (2017, unknown): “In a multivariate setting, variable localization was also proposed and tested by Kang et al. (2011), where cross correlations between carbon dioxide concentration and meteorological variables other than wind variables are set to zero.”