1. Kelvin K. Droegemeier University of Oklahoma NCAR 50th Anniversary Special Symposium The Future of Weather Forecasting and Potential Roles to be Played by NCAR

3. In the Beginning…

4. Predictions by Hand 25 point mesh! One Level Grid Spacing = 250 km

5. n Done on ENIAC: 2.5 million times slower than my laptop n Numerically integrated one equation at one altitude n 736 km grid spacing n 24 hour forecast took 24 hours to compute! n Forecast below up due to lack of smoothing of data – but rerun today, it was ok! 450 Miles 1950: The First Computer Weather Forecast Model

6. …And Things Have Been Improving Ever Since

7. Where Are We Today? Operations 60 Years After ENIAC

8. Where Are We Today? Communications

10. KFWS 02Z 30 Nov 98 ARPS 6 h Forecast CREF (9 km) Valid 02Z 30 Nov 98 1998 Science and Technology

11. Where Are We Today? R&D 1 km grid WRF, 9-hour Forecast

12. 21 hr, 4 km WRF Ensemble Forecasts Prob Ref > 35 dBZSpaghetti Observed 2 km Grid Xue et al. (2008)

13. Centers of On-Demand Forecast Grids Launched at NCSA During 2007 HWT Spring Experiment Launched automatically in response to hazardous weather messages (tornado watches, mesoscale discussions) Launched based on forecaster guidance Graphic Courtesy Jay Alameda and Al Rossi, NCSA

14. The Value of Dynamic Adaptation: Forecaster- Initiated Predictions on 7 June 2007 Brewster et al. (2008) Radar Observations Standard 20-hr Forecast 5 hr LEAD Dynamic Forecast

15. Lili Ju (Univ. South Carolina) Max Gunzberger (FSU) Todd Ringler (LANL) (Michael Duda, NCAR) NCAR Model for Prediction Across Scales (MPAS) A Model of Innovation

16. New Observations

17. Advanced Data Assimilation

18. Computational Power: Innovation?

19. We’ve Been Wishing Wishing…

20. Past Emphasis n Our biggest challenges in weather forecasting have been principally in the physical science and engineering domains –Efficient and accurate numerical solvers –Good model physics –Sufficiently powerful computers/fine grids –Meaningful and plentiful observations –Methods for assimilating data –Fast communications networks –Statistical measures of skill

21. Limitations Have Been Our Friend n We’ve benefitted from an ability to –Separate scales –Not explicitly predict unobservable phenomena –Verify what we can observe –Predict weather in relative isolation from other physical systems (e.g., ecosystems) –Consider weather and climate as mostly distinct –Forecast weather reasonably well without a unified theory of predictability

22. The Future Will be QUITE Different! n Weather forecasts of the future will be one component of a risk assessment and decision process for people, businesses, nations n Understanding what to convey, and how to convey it, will require trans-disciplinary collaboration with the social, behavioral, economic, and other sciences n Statistical skill will be overshadowed by measures of value and cost benefit

23. The Future Will be QUITE Different! n The distinction between weather and climate will become much less clear n Weather prediction will become much broader and will encompass ecosystems and other biological domains n Fixed prediction schedules and geographic domains will give way to dynamically adaptive approaches in which the entire prediction system adjusts automatically to the situation at hand

24. The Future Will be QUITE Different! n Computers with millions of cores and 100- 1000 core nodes will require entirely new approaches to solving the equations n Warnings will be issued based upon fine- scale weather forecasts, fundamentally changing human interpretation and response n We will be forced to revisit the fundamental tenets of predictability as we now understand them

25. Numerical Prediction with Radar Data Assimilation

26. The Future Will be QUITE Different! n In other words, many of the key challenges for the future will reside not in physical science and engineering domains alone, but also in the social, behavioral, health, policy and economic sciences –Urban living –Environmental sustainability –Energy –Ecosystem productivity –Transportation

27. Potential Roles for NCAR n To continue serving as a disciplinary change agent and integrator –Basic and applied research –Innovation in observations (COSMIC) –Community models (WRF  MPAS) –Deployable research observing platforms –Linking research and operations –High performance computing & related research –Integrating other physical science domains –Environment for nurturing new talent –  Enabling the academic community

28. Potential Roles for NCAR n To attack the social, behavioral, and economic science aspects of weather forecasting through new partnerships with academia that leverage capabilities at NCAR n Universities have entire departments/centers –Sociology –Psychology –Economics –Finance –Risk –Life science –Anthropology –Political Science –Public Policy –Medicine –Public Health –Ecology/biology

29. A Good Example

30. Warn on Explicit Forecast?

31. Getting What We Wish For! Warn-on-Forecast Will… n Completely change the notion of a hazardous weather warning (and of forecasting) –Information content, communication –Implications for other domains such as homeland security n Completely change how people respond? –Relevance of flash flood warnings, hurricane warnings n Require integration of economics n Require quantification of uncertainty – could be the downfall or the reason for success n Require understanding of predictability

32. NCAR Going Forward n NCAR and the UCAR community will be vitally important for ushering in this new era of “weather forecasting” –Consistent with its strategic plan n The Blue Book vision in reverse n We must work together to keep NCAR strong and vibrant, engaging all relevant programs at NSF and other agencies n Greater role of the private sector at NCAR in “weather forecasting?”