Probabilistic Mesoscale Analyses & Forecasts Progress & Ideas

1. Probabilistic Mesoscale Analyses & ForecastsProgress & Ideas Greg Hakim University of Washington www.atmos.washington.edu/~hakim Collaborators: Brian Ancell, Bonnie Brown, Karin Bumbaco, Sebastien Dirren, Helga Huntley, Rahul Mahajan, Cliff Mass, Guillaume Mauger, Phil Mote, Angie Pendergrass, Chris Snyder, Ryan Torn, & Reid Wolcott.

2. Plan • State estimation & forecasting on the mesoscale. • The UW “pseudo-operational” system. • Ensemble methods for mining & adapting the “data cube.” Analysis & prediction is fundamentally probabilistic!

3. State Estimation • Limitations of observations. • Errors. • Sparse in space & time. • Limited info about unobserved fields & locations. • Not usually on a regular grid. • Limitations of models. • Errors. • Often not cast in terms of observations (e.g. radiances) • Space & time resolution trade off. • Combine strengths of obs & models…

4. One-dimensional Examples

5. Analysis (red) PDF---higher density!

6. More-Accurate Observation

7. Less-Accurate Observation

8. More than one dimension: Covariance • Relationships between variables (spread obs info) • Weight to observations and background • Kalman Filter: propagate the covariance • Ensemble KF: propagate the square root (sample)

9. State-dependent Cov Matrices Cov(Z500,Z500) “3DVAR” EnKF Cov(Z500,U500) EnKF “3DVAR”

10. Mesoscale Example: cov(|V|, qrain)

11. Sampling Error

12. Summary of Ensemble Kalman Filter (EnKF) Algorithm • Ensemble forecast provides background estimate & statistics (Pb) for new analyses. • Ensemble analysis with new observations. (3) Ensemble forecast to arbitrary future time.

13. Real Time Data Assimilation at the University of Washington • Operational since 22 December 2004 • 90-member WRF EnKF • assimilate obs every 6 hours • 36 km grid over NE Pacific and western NOAM • Experimental 12 km grid over Pacific Northwest Transition from research to operations was a direct result of CSTAR support.

14. www.atmos.washington.edu/~enkf

15. System Performance Winds Moisture UW EnKFGFSCMCUKMONOGAPS

16. Applications of Ensemble Data Example: Forecast sensitivity and observation impact • Can rapidly evaluate many metrics & observations • Allows forecasters to do “what if” experiments. • cf. adjoint sensitivity: • new adjoint run for each metric • Also need adjoint of DA system for obs impact.

17. Sensitivity to SLP

18. Analysis difference (no-buoy – buoy), Shift frontal wave to the southeast

19. 6-hour forecast difference

20. 12-hour forecast difference

21. 18-hour forecast difference

22. 24-hour forecast difference Predicted Response: 0.63 hPa Actual Response: 0.60 hPa

23. Observation Impact Example Typhoon Tokage (2004)

24. Compare forecast where only this 250 hPa zonal wind observation is assimilated to forecast with no observation assimilation Observation Impact Squares – rawinsondes Circles – surface obs. Diamonds – ACARS Triangles – cloud winds

25. F00 Forecast Differences Sea-level Pressure 500 hPa Height

26. F24 Forecast Differences Sea-level Pressure 500 hPa Height

27. F48 Forecast Differences Sea-level Pressure 500 hPa Height

28. Short-term mesoscale probabilistic forecasts • ensemble population matters (cf. medium range) • “Hybrid” data assimilation • flow-dependent covariance in 4dvar cost function. • Kalman smoother with strong model constraint. • Observation targeting, thinning, and QC. • “Adaptive” forecast grids & metrics • update forecasts on-the-fly with new observations. • Jim Hansen (NRL) Ensemble Opportunities

29. Summary • Analysis & prediction is fundamentally probabilistic! • Future plans should embrace this fact • Ensembles are not just for prediction & assimilation • Observations: impact; QC; targeting; thinning • Models: calibration and adaptation; forget “plug-n-play” • Data mining: user-defined metrics; “instant updates”