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Probabilistic Mesoscale Analyses & Forecasts Progress & Ideas

This presentation by Greg Hakim from the University of Washington discusses the progress and ideas surrounding probabilistic mesoscale analyses and forecasts. It covers state estimation, limitations of observations and models, ensemble methods, and real-time data assimilation. The talk also explores applications of ensemble data, forecast sensitivity and observation impact, and short-term mesoscale probabilistic forecasts.

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Probabilistic Mesoscale Analyses & Forecasts Progress & Ideas

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  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”

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