Sampling Errors in Ensemble Kalman Filtering. Part I: Theory

By W. Sacher, P. Bartello. 2008

63 citations recorded by the source corpus.

Subfields: Atmospheric Science, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/cf061c05929fb7c649bd6be22f8ac05a1cceb27e

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. Alexis Hannart
2. P. Raanes
3. Marc Bocquet
4. Pavel Sakov
5. P. V. Leeuwen
6. Alberto Carrassi

In Environmental Engineering

1. Alexis Hannart
2. Marc Bocquet
3. P. Raanes

In Global and Planetary Change

1. Alexis Hannart
2. P. Raanes
3. Marc Bocquet
4. Pavel Sakov
5. P. V. Leeuwen

Citations in the corpus

• Expanding the validity of the ensemble Kalman filter without the intrinsic need for inflation (2015, methodology): “…it is likely that inflation is actually compensating for the misestimation of errors generated by model nonlinearity.20 16 In accounting for sampling error, the EnKF-N framework dif ers q ite significantly from that of van Leeuwen (1999), Furrer and Bengtsson (2007), and Sacher and Bartello (2008).”
• Ensemble Kalman filtering without the intrinsic need for inflation (2011, methodology): “The impact of these errors on the analysis can be objectively estimated using the results ofvan Leeuwen(1999); Furrer and Bengtsson (2007); Sacher and Bartello(2008).”