Accounting for Model Errors in Ensemble Data Assimilation
By Hong Li, Eugenia Kalnay, Takemasa Miyoshi, Christopher M. Danforth. 2009
90 citations recorded by the source corpus.
Subfields: Atmospheric Science, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/0e2c91df6538cfa26cb3138003b29066e5ae77dc
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
- Jeffrey L. Anderson
- Richard Menard
- R. Vose
- P. Thorne
- Geir Evensen
- Takemasa Miyoshi
- Marc Bocquet
- J. Poterjoy
In Global and Planetary Change
- Jeffrey L. Anderson
- Richard Menard
- R. Vose
- P. Thorne
- Geir Evensen
- Takemasa Miyoshi
- Marc Bocquet
- Thomas M. Hamill
Citations in the corpus
- Reanalyses Suitable for Characterizing Long-Term Trends (2010, background): “—, —, —, and C. M. Danforth, 2009b: Accounting for model errors in ensemble data assimilation.”
- Localization and Sampling Error Correction in Ensemble Kalman Filter Data Assimilation (2012, methodology): “Li et al. (2009a) presented a method to simultaneously correct for a lack of variance in prior ensembles and estimate the error variance of observations.”
- Error covariance estimation methods based on analysis residuals: theoretical foundation and convergence properties derived from simplified observation networks (2016, methodology): “We also note that the covariance diagnostics (1) and (2) are used for online estimation of observation and model error variances in ensemble Kalman filters (EnKF) (Li et al., 2009) and to scale the background-error covariance in EnKF (Desroziers et al.”