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Mesoscale Probabilistic Prediction over the Northwest: An Overview

Mesoscale Probabilistic Prediction over the Northwest: An Overview

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Mesoscale Probabilistic Prediction over the Northwest: An Overview

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

  2. Mesoscale Probabilistic Prediction • By the late 1990’s, we had a good idea of the benefits of high resolution. • It was clear that initial condition and physics uncertainty was large. • We were also sitting on an unusual asset due to our work evaluating major NWP centers: real-time initializations and forecasts from NWP centers around the world. • Also, inexpensive UNIX clusters became available.

  3. “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

  4. “Ensemblers” Eric Grimit (r ) and Tony Eckel (l) are besides themselves over the acquisition of the new 20 processor athelon cluster

  5. UWME • Core : 8 members, 00 and 12Z • Each uses different synoptic scale initial and boundary conditions • All use same physics • Physics : 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

  6. Ensemble-Based Probabilistic Products

  7. The MURI Project • In 2000, Statistic Professor Adrian Raftery came to me with a wild idea: submit a proposal to bring together a strong interdisciplinary team to deal with mesoscale probabilistic prediction. • Include atmospheric sciences, psychologists, statisticians, web display and human factors experts.

  8. The Muri I didn’t think it had a chance. I was wrong. It was funded and very successful.

  9. The MURI • Over five years substantial progress was made: • Successful development of Bayesian Model Averaging (BMA) postprocessing for temperature and precipitation • Development of both global and local BMA • Development of grid-based bias correction • Completion of several studies on how people use probabilistic information • Development of new probabilistic icons.

  10. Raw 12-h Forecast Bias-Corrected Forecast

  11. *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

  12. Calibration Example-Max 2-m Tempeature(all stations in 12 km domain)

  13. Verification The Thanksgiving Forecast 2001 42h forecast (valid Thu 10AM) SLP and winds • Reveals high uncertainty in storm track and intensity • Indicates low probability of Puget Sound wind event 1: cent 5: ngps 11: ngps* 8: eta* 2: eta 3: ukmo 6: cmcg 9: ukmo* 12: cmcg* 4: tcwb 7: avn 13: avn* 10: tcwb*

  14. Ensemble-Based Probabilistic Products

  15. Probability Density Function at one point Ensemble-Based Probabilistic Products

  16. Providing forecast uncertainty information is good…. But you can have too much of a good thing…

  17. MURI • Improvements and extensions of UWME ensembles to multi-physics • Development of BMA and probcast web sites for communication of probabilistic information. • Extensive verification and publication of a large collection of papers. • And plenty more…

  18. Before Probcast: The BMA Site



  21. The JEFS Phase • Joint AF and Navy project (at least it was supposed to be this way). UW and NCAR main contractors. • Provided support to continue development of basic parameters. • Joint project with NCAR to build a complete mesoscale forecasting system for the Air Force. • For the first few years was centered on North Korea, then SW Asia, and now the U.S.

  22. JEFS Highlights • Under JEFS the post-processed BMA fields has been extended to wind speed and direction. Local BMA for precipitation. • Development of EMOS, a regression-based approach that produces results nearly as good as BMA. • Next steps: derived parameters (e.g., ceiling, visibility)

  23. NSF Project • Currently supporting extensive series of human-subjects studies to determine how people interpret uncertainty information. • Further work on icons • Further work on probcast.

  24. Ensemble Kalman Filter Project • Much more this afternoon. • 80-member synoptic ensemble (36 km-12 km or 36 km) • Uses WRF model • Six-hour assimilation steps. • Experimenting with 12 and 4 km to determine value for mesoscale data assimilation-AOR in 3D.

  25. Big Picture • The U.S. is not where it should be regarding probabilistic prediction on the mesoscale. • Current NCEP SREF is inadequate and uncalibrated. • Substantial challenges in data poor areas for calibration and for fields like visibility that the models don’t simulate at all or simulate poorly. • A nationally organized effort to push rapidly to 4-D probabilistic capabilities is required.

  26. Opinion • Creating sharp, reliable PDFs is only half the battle. • The hardest part is the human side, making the output accessible, useful, and compelling. We NEED the social scientists. • Probabilistic forecast information has the potential for great societal economic benefit.

  27. The END

  28. Brief History • Local high-resolution mesoscale NWP in the Northwest began in the mid-1990s after a period of experimentation showed the substantial potential of small grid spacing (12 to 4 km) over terrain. • At that time NCEP was running 32-48km grid spacing and the Eta model clearly had difficulties in terrain.

  29. The Northwest Environmental Prediction System • Beginning in 1995, a team at the University of Washington, with the help of colleagues at Washington State University and others have built the most extensive regional weather/environmental prediction system in the U.S. • It represents a different model of how weather and environmental prediction can be accomplished.

  30. Pacific Northwest Regional Prediction: Major Components • Real-time, operational mesoscale environmental prediction • MM5/WRF atmospheric model • DHSVM distributed hydrological model • Calgrid Air Quality Model • A variety of application models (e.g., road surface) • Real-time collection and quality control of regional observations.

  31. Why Probcast? • We are rapidly gaining the ability to produce useful probabilistic guidance-- forecasts that are reasonably reliable and sharp. • Believe it or not, this is the easy part of the problem. • What we have not done is to design interfaces that allow users to make effective user of probabilistic output … or even convince users that they should “go probabilistic”. • The recent NRC Report on Probabilistic Prediction highlights this issue.

  32. UW Uncertainty MURI • The DOD-sponsored UW Uncertainty MURI was designed to consider both sides of the problem: • Generation of probabilistic information--ensembles and post-processing • Display and human interface issues. • Includes UW Atmospheric Sciences, Statistics, Psychology, and Applied Physics Lab

  33. UW Probabilistic Prediction • UW Ensemble System is based on using varying initialization and boundary conditions from differing operational analyses. • Also includes varying model physics and surface properties (e.g., SST). • Have developed sophisticated post-processing: grid-based bias correction and Bayesian Model Averaging (BMA)--both global and local

  34. Before Probcast: The BMA Site

  35. UW MURI • 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.