Data assimilation research:

RMS error in the zonal velocity (m/sec) in a data assimilation experiment with the NCEP Global Forecasting System with observations obtained from a model “nature” run. The observational errors in the zonal and meridional velocities were of 1m/sec.

From Szunyogh et al, 2004)

Local Ensemble Kalman Filtering project

Profs. Eugenia Kalnay (Meteorology and IPST) and Yorke (IPST) started in 1999 a multidisciplinary group on data assimilation and ensemble forecasting. Profs. Ed Ott (Physics), Brian Hunt (Mathematics), Istvan Szunyogh (IPST and Meteorology) soon joined them, with the collaboration of Profs. Eric Kostelich (Arizona State University) and Tim Sauer (George Mason University). They developed a novel and efficient new method for data assimilation denoted Local Ensemble Kalman Filter (LEKF). This method has been tested in simple models and with simulated data in the NCEP Global Forecasting System (GFS), and has been shown to be accurate and very efficient. This method was extended to 4 dimensions allowing the assimilation of observations at the right time, the main advantage of 4D-Var but without the need to develop an adjoint model. Currently they are working on testing the LEKF (with a very fast new algorithm) with real observations using both the NCEP GFS and the NASA finite volume general circulation model (fvGCM).

The following students have worked on major components of this and related projects:

D. J. Patil (Ph. D. in 2002, low dimensionality in ensembles)

Aleksei Zimin (Ph. D. in 2003, LEKF on the Lorenz-96 model)

Matteo Corazza (Ph. D. in 2003, “errors of the day” and LEKF on a QG model)

Michael Oczkowski (Ph. D. in 2004, energetics of low dimensionality evolution)

Takemasa Miyoshi (comparison of LEKF and 3D Var on two models)

Dagmar Merkova (LEKF applied to a regional model)

Shu-Chih Yang (Comparison of 3 and 4D Var in QG model, breeding in coupled ocean-atmosphere models)

Malaquias Peña (Ph. D. in 2004, breeding in a simple coupled model, one and two-way atmospheric-ocean coupling)

Hong Li (LEKF on the NASA fvGCM, land-use impact on trends of US surface temperatures)

Junjie Liu (LEKF on the NASA fvGCM)

References:

Corazza, M., E. Kalnay, D. J. Patil, R. Morss, I. Szunyogh, B. R. Hunt, E. Ott, and M. Cai, 2003: Use of the breeding technique to estimate the structure of the analysis “errors of the day”. Nonlinear Processes in Geophysics, 10, 233-243.

Ott, Edward, B. R. Hunt, I. Szunyogh, A. Zimin, E. Kostelich, M. Corazza, E. Kalnay, D.J. Patil, and J. A. Yorke, 2002: A Local Ensemble Kalman Filter for Atmospheric Data Assimilation. Posted http://arXiv.org/abs/physics/0203058.

Peña, M., E. Kalnay and M. Cai, 2003: Statistics of coupled ocean and atmosphere intraseasonal anomalies in Reanalysis and AMIP data. Nonlinear Processes in Geophysics, European Geophysical Society, 10, 245-251.

Seon-ki Park and E. Kalnay, 2004: Inverse three-dimensional variational data assimilation for an advection-diffusion problem: Impact of diffusion and hybrid application. Geophysical Research Letters, Vol. 31, L04102, 5pp.

Vukicevic, T., Kalnay, E., Vonder Haar, T. 2004: The Need for a National Data Assimilation Education Program. Bulletin of the American Meteorological Society: Vol. 85, No. 1, pp. 48–49.

Evans, Erin, Nadia Bhatti, Jacki Kinney, Lisa Pann, Malaquias Peña, Shu-Chih Yang, Eugenia Kalnay,

and James Hansen, 2004: RISE undergraduates find Lorenz’s model regime changes predictable. Bulletin of the American Meteorological Society: Vol. 85, No. 4, pp. 520–524.

E. Ott, B.R. Hunt, I. Szunyogh, A.V. Zimin, E.J. Kostelich, M. Corazza, E. Kalnay, D.J. Patil, and J.A. Yorke, 2004: A Local Ensemble Kalman Filter for Atmospheric Data Assimilation. Tellus (in print).

B.R. Hunt, E. Kalnay, E.J. Kostelich, E. Ott, D.J. Patil, T. Sauer, I. Szunyogh, J.A. Yorke, and A.V. Zimin, 2004: Four-Dimensional Ensemble Kalman Filtering. Tellus (in print).

Peña, Malaquías, Cai, Ming, Kalnay, Eugenia. 2004: Life Span of Subseasonal Coupled Anomalies. Journal of Climate: Vol. 17, No. 7, pp. 1597–1604.

Peña, M. and E. Kalnay, 2004: Separating fast and slow modes in coupled chaotic systems. Nonlinear Processes in Geophysics, vol 19, 319-327.

I. Szunyogh, E.J. Kostelich, G. Gyarmati, D.J. Patil, B.R. Hunt, E. Kalnay, E. Ott and J.A. Yorke, 2004: Assessing a Local Ensemble Kalman Filter: Perfect Model Experiments with the NCEP Global Model. Tellus (in print).