1. Mesoscale Deterministic and Probabilistic Prediction over the Northwest: An Overview Cliff Mass University of Washington

2. University of Washington Mesoscale Prediction Effort An attempt to create an end-to-end deterministic and probabilistic prediction system. On the deterministic side, examine the benefits of high resolution Identify major issues with physics parameterizations

3. Deterministic Prediction WRF ARW Core run at 36, 12, 4, and now 4/3 km grid spacing Extensive verification Variety of applications running off the deterministic forecasts.

4. Major Elements Two mesoscale ensembles systems UWME (15 members) and EnKF (60 members, 36 and 4 km grid spacing). Sophisticated post-processing to reduce model bias and enhance reliability and sharpness of resulting probability density functions (PDFs) for UWME. Stand-alone bias correction Bayesian Model Averaging (BMA) Ensemble MOS (EMOS)

5. Major Elements Psychological research to determine the best approaches for presenting uncertainty information. Creation of next-generation display products providing probabilistic information to a lay audience. Example: probcast.

6. Inexpensive Commodity Clusters This effort has demonstrated the viability of doing such work on inexpensive Linux clusters. Proven to be highly reliable

7. The Summary

8. Verification

9. Precip Verification

10. High Resolution Attempt to answer questions: – What is the payoff in getting the land-water boundaries and smaller scale terrain much better – Does ultra high resolution improve objective verification or subjective structures? – Do physics problems get better or worse?

11. 1.3 4 km

12. 6-hr forecast, 10m wind speed and direction 1.3 km 4 km

13. Boundary Layer Physics: A Current Achilles Heel of Mesoscale NWP Well known issues: –Winds too strong and geostrophic near surface –Excessive low-level mixing –Inability to maintain shallow cold PBL

14. During the past few months we have continued our testing program of various PBL schemes, vertical diffusion options, etc. A test case has been one in which the 4 and 1.3 km created unrealistic roll circulations.

15. http://www.atmos.washington.edu/~ovens/wrf _1.33km_striations/

16. 1 km visible

18. Problem Instead of getting open cellular convection, there are these period cloud streets. Look like roll circulation, but of too large a scale (if you look at sat pics you can see hints of them). Sometimes apparent (but less so) in 4-km. Occurs only in unstable, post-frontal conditions.

19. Through the kitchen sink at it and consulted heavily with Dave Stauffer at Penn State Tried a range of PBL schemes (YSU, QNSE, ACM2, MYNN, MYJ, MYJ with Stauffer mods) Added 6 th order diffusion and played with diffusion coefficent. Fully, interactive nesting Upper level diffusion and gravity wave drag Monotonic advection Varying vertical diffusion, both more and less

20. Results ACM2 (Pleim PBL and LSM) was the only thing that helped reduce the rolls. It created this stratiform cloud mass that wasn’t very realistic.

21. Excessive Geostrophy and Mixing at Low Levels Tried every PBL option in ARW…not the solution! Trying other things: decreasing model diffusion and increasing surface drag by increasing ustar.

23. Example: Cut vertical diffusion 10 1/8 th of normal value

24. Vertical Diffusion cut to 1/4

25. StandardLow Diffusion

26. Mesoscale Ensembles at the UW

27. UWME Core Members 8 members, 00 and 12Z Each uses different synoptic scale initial and boundary conditions from major international centers All use same physics MM5 model, will be switching to WRF. 72-h forecasts

28. Resolution ( ~ @ 45  N ) Objective Abbreviation/Model/Source Type Computational Distributed Analysis avn, Global Forecast System (GFS), SpectralT254 / L641.0  / L14 SSI National Centers for Environmental Prediction~55 km~80 km3D Var cmcg, Global Environmental Multi-scale (GEM), Finite0.9  0.9  /L281.25  / L113D Var Canadian Meteorological Centre Diff ~70 km ~100 km eta, limited-area mesoscale model, Finite32 km / L45 90 km / L37SSI National Centers for Environmental Prediction Diff.3D Var gasp, Global AnalysiS and Prediction model,SpectralT239 / L291.0  / L11 3D Var Australian Bureau of Meteorology~60 km~80 km jma, Global Spectral Model (GSM),SpectralT106 / L211.25  / L13OI Japan Meteorological Agency~135 km~100 km ngps, Navy Operational Global Atmos. Pred. System,SpectralT239 / L301.0  / L14OI Fleet Numerical Meteorological & Oceanographic Cntr. ~60 km~80 km tcwb, Global Forecast System,SpectralT79 / L181.0  / L11 OI Taiwan Central Weather Bureau~180 km~80 km ukmo, Unified Model, Finite5/6  5/9  /L30same / L123D Var United Kingdom Meteorological Office Diff.~60 km “Native” Models/Analyses Available

29. UWME –Physics Members 8 members, 00Z only Each uses different synoptic scale initial and boundary conditions Each uses different physics Each uses different SST perturbations Each uses different land surface characteristic perturbations –Centroid, 00 and 12Z Average of 8 core members used for initial and boundary conditions

30. 36 and 12-km domains

34. Post-Processing Post-processing is a critical and necessary step to get useful PDFs from ensemble systems. The UW has spent and is spending a great deal of effort to perfect various approaches that are applicable on the mesoscale.

35. Post-Processing Major Efforts Include –Development of grid-based bias correction –Successful development of Bayesian Model Averaging (BMA) postprocessing for temperature, precipitation, and wind –Development of both global and local BMA –Development of ensemble MOS (EMOS)

36. Grid-Based Bias Correction Use previous observations, land-use categories, elevation, and distance to determine and reduce bias in forecasts

39. *UW Basic Ensemble with bias correction UW Basic Ensemble, no bias correction *UW Enhanced Ensemble with bias cor. UW Enhanced Ensemble without bias cor Skill for Probability of T 2 < 0°C BSS: Brier Skill Score Bias Correction Substantially Improves Value of Ensemble Systems

40. BMA

43. Testing both global BMA (same weights over entire domain) and local BMA (ensemble weights vary spatially).

44. EMOS

45. EMOS Test

46. EMOS Verification

47. Communication and Display Considerable work by Susan Joslyn and others in psychology and APL to examine how forecasters and others process forecast information and particularly probabilistic information. One example has been their study of the interpretation of weather forecast icons.

50. The Winner

51. PROBCAST

53. UW EnKF System Can we produce a superior 3D analysis? Can we use it to produce good short-term predictions…a major missing element in most systems? Can we use a significant proportion of the numerous observations that are now available?

54. UW EnKF Data Assimilation Now using a 36-4 km system with 3hr update Previously, used 36-12 km and 6h update Future: move to 1 hr update

55. EnKF 12km Surface Observations

56. EnKF 12-km vs. GFS, NAM, RUC RMS analysis errors GFS2.38 m/s 2.28 K NAM RUC EnKF 12km WindTemperatur e 2.30 m/s 2.13 m/s 1.85 m/s 2.54 K 2.35 K 1.67 K

57. The END

58. Future Evaluation Improving PBL and surface drag may preferentially help 1.3 km (more later) Using 1.3 km as a testbed (some problems are more acute at higher resolution)

59. A Major Issue Has Been Excessive Wind Speeds Over Land and Excessive Geostrophy at the Surface –either too much mixing in vertical or not enough drag. Winds over land and water too similar No magic bullet in PBL tests. Recently, we tried something that really looks like it has potential to help…increasing the friction velocity….ustar. Essentially adds drag, without messing other things up. Perhaps it is realistic, mimicking the effects of hills.