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Exploring Balance Metrics in Assimilation Systems: Algorithm Insights and Model Considerations

This discussion delves into the importance of balance in assimilation systems, exploring various metrics and types of imbalance in models. It covers algorithm details, balance issues concerning moisture and tropical cyclones, unbalanced flow scenarios, and the use of toy models. The analysis also questions the significance of balance in background initialization and error covariances estimation. Further, it investigates the impact of control variable choices in Var systems and the variability of kinetic energy spectrum with height. Forecast and observational error variances are examined, emphasizing the significance of balance considerations in assimilation processes.

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Exploring Balance Metrics in Assimilation Systems: Algorithm Insights and Model Considerations

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  1. General order for discussion • Does balance matter? • What kinds of metric should be used to measure imbalance? • Algorithm details • Other types of balance • What about unbalanced flow? • Use of “toy models”. • Anything else?

  2. Metrics • What kind of metric should be used to measure imbalance? • Separate by scale?

  3. Measuring gravitational “noise”

  4. Does balance matter? • Does a balanced analysis matter, or can we just initialise? • Does a balanced background matter? • Used to estimate background error covariances

  5. Algorithm details • Are some flavours of EnKF inherently better for balance? • Perturbed obs, single vs double, ETKF vs EnSRF vs EAKF, sequential vs all-at-once, … • Are some control variable choices better for balance in Var systems?

  6. Other types of balance • Balance issues concerning moisture • Balances on the mesoscale • Tropical cyclones

  7. Convection and Gravity-Wave Generation

  8. What about unbalanced flow? • Models can simulate several regimes, e.g • Mesosphere • Mesoscale • Tropics

  9. Kinetic energy spectrum changes with height Rot KE Div KE troposphere stratosphere mesosphere RotKE = DivKE around n=20 RotKE = DivKE around n=10 Koshyk et al. (1999)

  10. Toy models • What are the desirable properties of a toy model for studying balance in an assimilation system?

  11. Anything else?

  12. Forecast and obs error variances increase with height CMAM ens-based T forecast error std T profiles over one night from lidar 42.9 N, 81.4 W R.J. Sica (U Western Ontario) http://pcl.physics.uwo.ca/science/temperature/ Nezlin et al. (2008)

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