Mesoscale Deterministic and Probabilistic Prediction over the Northwest: An Overview

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. 4 km 1.3

12. 6-hr forecast, 10m wind speed and direction 4 km 1.3 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/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 6th 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 1/8th of normal value

24. Vertical Diffusion cut to 1/4

25. Standard Low 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. “Native” Models/Analyses Available Resolution (~@ 45 N ) Objective Abbreviation/Model/Source Type ComputationalDistributed Analysis avn, Global Forecast System (GFS), Spectral T254 / L64 1.0 / L14 SSI National Centers for Environmental Prediction ~55km ~80km 3D Var cmcg, Global Environmental Multi-scale (GEM), Finite 0.90.9/L28 1.25 / L11 3D Var Canadian Meteorological Centre Diff ~70km ~100km eta, limited-area mesoscale model, Finite 32km / L45 90km / L37 SSI National Centers for Environmental Prediction Diff. 3D Var gasp, Global AnalysiS and Prediction model, Spectral T239 / L29 1.0 / L11 3D Var Australian Bureau of Meteorology ~60km ~80km jma, Global Spectral Model (GSM), Spectral T106 / L21 1.25 / L13OI Japan Meteorological Agency ~135km ~100km ngps, Navy Operational Global Atmos. Pred. System, Spectral T239 / L30 1.0 / L14 OI Fleet Numerical Meteorological & Oceanographic Cntr. ~60km ~80km tcwb, Global Forecast System, Spectral T79 / L18 1.0 / L11 OI Taiwan Central Weather Bureau ~180km ~80km ukmo, Unified Model, Finite 5/65/9/L30 same / L12 3D Var United Kingdom Meteorological Office Diff. ~60km

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 T2 < 0°C BSS: Brier Skill Score Bias Correction Substantially Improves Value of Ensemble Systems

40. BMA

43. BMA • 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