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Panel 3: New Science Issues and Possibilities in Data Assimilation

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

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Panel 3: New Science Issues and Possibilities 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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