5.4. Citations

[Hunt2007]

Hunt, B. R., Kostelich, E. J., & Szunyogh, I. (2007). Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Physica D: Nonlinear Phenomena, 230(1-2), 112–126. http://doi.org/10.1016/j.physd.2006.11.008

[Miyoshi2005]

Miyoshi, T. (2005). Ensemble Kalman Filter Experiments with a Primitive-Equation Global Model. University of Maryland. Retrieved from http://hdl.handle.net/1903/3046

[Sluka2016]

Sluka, T. C., Penny, S. G., Kalnay, E., & Miyoshi, T. (2016). Assimilating atmospheric observations into the ocean using strongly coupled ensemble data assimilation. Geophysical Research Letters, 43(2), 752–759. https://doi.org/10.1002/2015GL067238

[Whitaker2012]

Whitaker, J. S., & Hamill, T. M. (2012). Evaluating Methods to Account for System Errors in Ensemble Data Assimilation. Monthly Weather Review, 140(9),3078–3089. https://doi.org/10.1175/MWR-D-11-00276.1

[Zhang2004]

Zhang, F., Snyder, C., & Sun, J. (2004). Impacts of Initial Estimate and Observation Availability on Convective-Scale Data Assimilation with an Ensemble Kalman Filter. Monthly Weather Review, 132(5), 1238–1253. https://doi.org/10.1175/1520-0493(2004)132<1238:IOIEAO>2.0.CO;2