1. 10/18/2011 Youngsun Jung and Ming Xue CAPS/OU with help from Tim Supinie

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

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

4. 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. Various covariance inflation methods (Tim Supinie) Parameter estimation Improving microphysical parameterizations

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

7. By Tim Supinie 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

8. By Tim Supinie

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

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

12. Sky: Additive + multiplicative Orange: Adaptive

13. 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. Perfect observation operatorImperfect observation operator Tong and Xue (2008) Jung et al. (2010) √√ √

15. Perfect observation operatorImperfect observation operator Tong and Xue (2008) Jung et al. (2010)

16. Shade: log 10 (N0r) for the ensemble mean of EXP_DM at z = 100 m AGL Contour: Z DR log 10 (8x10 5 ) ≈ 5.9

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

18. 29-30 May 2004 supercell LFO scheme Ensemble mean analysis at z = 2 km and t = 60 min

19. By Tim Supinie

20. (MY)(LIN) excessive size sorting ?

21. z = 2 km No Z DR With Z DR

22. 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. 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.