CliffStat.ppt
- 1. Introduction to Weather Forecasting Cliff Mass Department of Atmospheric Sciences University of Washington
- 2. The Stone Age • Prior to approximately 1955, weather forecasting was basically a subjective art, and not very skillful. • The technology of forecasting was basically subjective extrapolation of weather systems, in the latter years using the upper level flow (the jet stream). • Local weather details—which really weren’t understood-- were added subjectively.
- 4. The Development of Numerical Weather Prediction (NWP) Vilhelm Bjerknes in his landmark paper of 1904 suggested that NWP-- objective weather prediction-- was possible. – A closed set of equations existed that could predict the future atmosphere – But it wasn’t practical then because there was no reasonable way to do the computations and a sufficient 3-D description of the atmosphere did not exist.
- 5. Numerical Weather Prediction One such equation is Newton’s Second Law: F = ma Force = mass x acceleration Mass is the amount of matter Acceleration is how velocity changes with time Force is a push or pull on some object (e.g., gravitational force, pressure forces, friction) Using observations we can determine the mass and forces, and thus can calculate the acceleration--giving the future
- 6. NWP Becomes Possible • By the 1940’s an extensive upper air network was in place, plus many more surface observations. Thus, a reasonable 3-D description of the atmosphere was possible. • By the mid to late 1940’s, digital programmable computers were becoming available…the first..the ENIAC
- 7. The Eniac
- 8. 1955-1965: The Advent of Modern Forecasting • Numerical weather prediction became the cornerstone. • New observing technologies also had a huge impact: – Weather satellites – Weather radar
- 9. Satellite and Weather Radars Provides a More Comprehensive View of the Atmosphere
- 11. Weather Prediction Steps • Data collection and quality control • Data assimilation: creating a physically realistic 3-D description of the atmosphere called the initialization. • Model integration. Solving the equations to produce future 3D descriptions of the atmosphere • Model output post-processing using statistical methods • Dissemination and communication
- 12. Using a wide range of weather observations we can create a three-dimensional description of the atmosphere… Initialization
- 13. Numerical Weather Prediction • The observations are interpolated to a 3-D grid where they are integrated into the future using a computer model--the collection of equations and a method for solving them. • As computer speed increased, the number of grid points could be increased. • More (and thus) closer grid points means we can simulate (forecast) smaller and smaller scale features. We call this improved resolution.
- 14. Model Postprocessing in the U.S.: Model Output Statistics (MOS) • Main post-processing approach used by the National Weather Service • Based on linear regression: Y=a0 + a1X1 + a2X2+ a3X3 + … • MOS is available for many parameters and time and greatly improves the quality of most model predictions.
- 15. Prob. Of Precip.– Cool Season (0000/1200 UTC Cycles Combined) 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 Year Brier Score Improvement over Climate Guid POPS 24 hr Local POPS 24 hr Guid POPS 48 hr Local POPS 48 hr
- 17. Major Improvement Weather forecasting skill has substantially improved over the last 50 years. Really.
- 18. Forecast Skill Improvement 15 25 35 45 55 65 75 1950 1960 1970 1980 1990 2000 NCEP operational S1 scores at 36 and 72 hr over North America (500 hPa) P Year "useless forecast" "perfect forecast" S1 score 72 hr forecast 36 hr forecast 10-20 years Forecast Error Year Better National Weather Service
- 19. Why Large Improvement in Weather Forecast Skill? •As computers became faster, were able to solve the equations at higher resolution •Improved physics •New observational assets allowed a better initialization
- 24. A More Basic Problem • There is fundamental uncertainty in weather forecasts that can not be ignored. • This uncertainty has a number of causes: – Uncertainty in initialization – Uncertainty in model physics – Uncertainties in how we solve the equations – Insufficient resolution to properly model atmospheric features.
- 25. The Atmospheric is Chaotic • The work of Lorenz (1963, 1965, 1968) demonstrated that the atmosphere is a chaotic system, in which small differences in the initialization…well within observational error… can have large impacts on the forecasts, particularly for longer forecasts. • Not unlike a pinball game….
- 27. Probabilistic Prediction • Thus, forecasts must be provided in a probabilistic framework, not the deterministic single answer approach that has dominated weather prediction during the last century. • Interestingly…the first public forecasts were probabilistic
- 28. “Ol Probs” Professor Cleveland Abbe, who issued the first public “Weather Synopsis and Probabilities” on February 19, 1871 Cleveland Abbe (“Ol’ Probabilities”), who led the establishment of a weather forecasting division within the U.S. Army Signal Corps. Produced the first known communication of a weather probability to users and the public in 1869.
- 29. Ensemble Prediction • The most prevalent approach for producing probabilistic forecasts and uncertainty information…ensemble prediction. • Instead of making one forecast…make many…each with a slightly different initialization or varied model physics. • Possible to do now with the vastly greater computation resources that are now available.
- 30. The Thanksgiving Forecast 2001 42h forecast (valid Thu 10AM) 13: avn* 11: ngps* 12: cmcg* 10: tcwb* 9: ukmo* 8: eta* Verification 1: cent 7: avn 5: ngps 6: cmcg 4: tcwb 3: ukmo 2: eta - Reveals high uncertainty in storm track and intensity - Indicates low probability of Puget Sound wind event SLP and winds
- 31. Ensemble Prediction •Can use ensembles to provide a new generation of products that give the probabilities that some weather feature will occur. •Can also predict forecast skill. •It appears that when forecasts are similar, forecast skill is higher. •When forecasts differ greatly, forecast skill is less.
- 33. Ensemble Post-Processing • To get the maximum benefits from ensembles, post-processing is needed, such as: – Correction for systematic bias – Optimal weighting of the various ensemble members--e.g., Bayesian Model Averaging
- 34. The UW-MURI Project • Possibility the most advanced ensemble/postprocessing system in the world has been developed at the UW • Includes UW Atmospheric Sciences, Statistics, Psychology, and Applied Physics Lab • Remaining talks will describe some of the research and development completed by this effort.
- 36. But you can have too much of a good thing… Providing forecast uncertainty information is good….
- 37. The END