Comparing the ensemble and extended Kalman filters for in situ soil moisture assimilation with contrasting conditions

@inproceedings{Fairbairn2015ComparingTE,
  title={Comparing the ensemble and extended Kalman filters for in situ soil moisture assimilation with contrasting conditions},
  author={David Fairbairn and Alina L. Barbu and J.-F. Mahfouf and Jean-Christophe Calvet and E. Gelati},
  year={2015}
}
Two data assimilation (DA) methods are compared for their ability to produce an accurate soil moisture analysis using the Meteo-France land surface model: (i) SEKF, a simplified extended Kalman filter, which uses a climatological background-error covariance, and (ii) EnSRF, the ensemble square root filter, which uses an ensemble background-error covariance and approximates random rainfall errors stochastically. In situ soil moisture observations at 5 cm depth are assimilated into the surface… CONTINUE READING

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