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Future Applications of Ensemble Kalman Filtering in Data Assimilation

Explore the advancements and possibilities of Ensemble Kalman Filtering in data assimilation models, with a focus on model deficiencies, ocean reanalysis, and real-time precipitation assimilation. Discover new ideas and future plans for improved accuracy and efficiency in scientific data assimilation. This discussion encompasses Square Root Ensemble Kalman Filtering, model error detection, and innovative techniques for better observational analysis.

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Future Applications of Ensemble Kalman Filtering in Data Assimilation

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  1. Panel 3: New Science Issues and Possibilities in Data Assimilation • Ensemble Kalman Filtering: • Square root filters, Local Ensemble Kalman Filtering • 4D-Var: should we consider it? • Surface data reanalysis (pre-1948) • Model deficiencies: detection and correction • Ocean reanalysis/Coupled reanalysis • Assimilation of “observed” precip: MERRA New ideas: how mature are they? Ready now? In 2 years? In 5 years?

  2. First some speakers, then open discussion • Square root Ensemble Kalman Filtering, etc • Kalnay (LEKF) (5-10 min) • Anderson (5-10 min) • Whitaker et al (no raobs) (5-10 min) • vandenDool (no model) (5 min) • Kanamitsu (no raobs) (5 min) • Ocean reanalysis: Carton (5-10 min) • Budget balance (Ichiro Fukumori) (5 min) • Model error discussion: Tribbia (5 m) • Open discussion (months and months)

  3. Local Ensemble Kalman FilterSzunyogh, Kostelich et al (2003), Ott et al (2003) • Completely parallel (each grid point analysis done locally and independently) • Computationally efficient (LEKF 80 member global NCEP model T62 model runs on a 40-processor cluster of PCs) • 10 minutes to assimilate 1.7 million observations • Model independent (code developed for the Lorenz 40 variable model and ported to the NCEP global model) • No need for TLM or adjoint models

  4. Some results • Tested it comparing it with Rebecca Morss QG 3D-Var with fantastic results • Tested it on the Lorenz 40 variable model with fantastic results • The same FORTRAN code developed for the Lorenz 40 model was coupled with the NCEP global model (T62/28layers)! • It would be simpler with a grid-point model

  5. Some results with the NCEP global model (Szunyogh et al, 2003) • So far we have done only simulations with a “nature” run, observing temperature and winds • We used 1K random errors in the temperature and 1m/sec in the wind • Tested the impact of number of observations and number of ensemble members

  6. 40 members, 2000 obs 80 members, 2000 obs 80 members, 18048 obs Observational error …went down to 5% of the observations with the same error: the background error covariance knows about the “errors of the day”

  7. Near future plans • Take local cubes, not local columns. This should improve boundary layer analysis, increase the efficiency and reduce the number of members • Test the system with rawinsondes (Whitaker et al data/experiment) • Add moisture analysis • Add incremental 4D-Var (no adjoint): allows assimilation at the right time at no extra cost • Kalman smoother (use future data)

  8. The future • LEKF is accurate and efficient and completely feasible with today’s supercomputers • Provides good estimates of the background error, the error growth and the analysis error covariance • Creates optimal ensemble perturbations • Ideal for adaptive observations (e.g., use high resolution where errors are growing) • Could be used interactively on board satellites, with instruments dwelling where errors are large or growing fast • Ready in two years…

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