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Model error issues: microphysics errors

Model error issues: microphysics errors. 10/18/2011 Youngsun Jung and Ming Xue CAPS/OU with help from Tim Supinie. Source of errors . Observation error: Non- Gaussianity , inaccurate observations error variance, none-zero observation error correlation, etc. Observation operator error

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Model error issues: microphysics errors

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  1. Model error issues: microphysics errors 10/18/2011 Youngsun Jung and Ming Xue CAPS/OU with help from Tim Supinie

  2. Source of errors • Observation error: Non-Gaussianity, inaccurate observations error variance, none-zero observation error correlation, etc. • Observation operator error • Model error

  3. Example: Observation operator error http://www.radar.mcgill.ca/science/ex-phenomenon/ex-melting-layers.html

  4. Background • In imperfect model experiments, it is observed that model error dominates the error growth in data assimilation cycles. • Despite this, the characteristics of model error are little known and its statistical properties are poorly understood (Dee 1995; Houtekamer et al. 2005). • For convective-scale NWP, microphysics scheme represents one of the most important physical processes.

  5. Outline • Various covariance inflation methods (Tim Supinie) • Parameter estimation • Improving microphysical parameterizations

  6. Inflation methods • Multiplicative inflation (Anderson and Anderson, 1999) • Relaxation (Zhang et al., 2004) • Adaptive inflation (Whitaker and Hamill, 2010) • Additive noise (Mitchell and Houtekamer, 2000) Sensitive to the inflation factor/size of noise a

  7. Inflation factor • Perfect model scenario • Multiplicative: 1.09 • Relaxation: 0.44 • Adaptive: 0.43 • Imperfect model scenario • Multiplicative: 1.12 -> filter divergence • Relaxation: 0.5 -> filter divergence • Adaptive: 0.8 By Tim Supinie

  8. Change in ensemble spread By Tim Supinie

  9. Change in ensemble spread By Tim Supinie

  10. Additive vs. Adaptive t = 1500 sec W z=7km MAX: 30.88 Min: -34.56 MAX: 31.17 Min: -27.37 Adaptive Additive noise corr(Z, qr) z=2km

  11. Additive vs. Adaptive t = 3600 sec efmean Adaptive Additive noise enmean MAX: 37.12 Min: -20.20 MAX: 25.68 Min: -27.68

  12. Additive (0.5 to u, v, T) vs. Adaptive (0.85) Sky: Additive + multiplicative Orange: Adaptive

  13. Parameter estimation • Certain DSD parameters such as the bulk densities and the intercept parameters of hydrometeors greatly influence the evolution of storm through microphysical processes. • Significant uncertainties exist in those parameters. • Several studies have shown that the EnKF method is capable of successfully identifying parameter values during assimilation process and, therefore, may help improve forecast (Annan et al. 2005a,b; Annan and Hargreaves 2004; Hacker and Snyder 2005; Aksoy et al. 2006a,b; Tong and Xue 2008a,b).

  14. Parameter estimation (single-parameter) Perfect observation operator Imperfect observation operator √ √ √ Jung et al. (2010) Tong and Xue (2008)

  15. Parameter estimation (three-parameter) Perfect observation operator Imperfect observation operator Jung et al. (2010) Tong and Xue (2008)

  16. Parameter estimation Shade: log10(N0r) for the ensemble mean of EXP_DM at z = 100 m AGL Contour: ZDR log10(8x105) ≈ 5.9

  17. Example of high hail bias • 29-30 May 2004 supercell • Milbrandt and Yau SM scheme 0.1 0.1 Ensemble mean analysis at z = 100 m and t = 60 min

  18. Example of high hail bias • 29-30 May 2004 supercell • LFO scheme Ensemble mean analysis at z = 2 km and t = 60 min

  19. Error in the microphysics scheme By Tim Supinie

  20. Analyzed polarimetric variables vs. observed (LIN) (MY) excessive size sorting ?

  21. Assimilating ZDR using a SM scheme No ZDR With ZDR z = 2 km

  22. Summary • Model error becomes a huge issue for real-data cases. • Various covariance inflation methods are found to be helpful but each method has its own limitations. Understanding strength and weaknesses of each method can help make better use of them. • Additional observations can help only if the observations carries information that the model can handle.

  23. Summary • Certain microphysics bias is very hard to treat and can be further deteriorated during data assimilation when the problem is seriously under-constrained by observations. • Observation operator errors can significantly influence the quality of analysis for storm scale DA. • Therefore, there should be continuous efforts to improve the model and the observation operator.

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