1 / 23

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

lamis
Download Presentation

Model error issues: microphysics errors

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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.

More Related