1. Kelvin K. Droegemeier School of Meteorology Center for Analysis and Prediction of Storms University of Oklahoma National Press Foundation Program Understanding Violent Weather 26 October 2005 Advances in the Observation and Computer Prediction of Severe Storms

2. Copyright © 2003 WGN-TV Everyone is Familiar With This Person!!

3. Copyright © 2003 WGN-TV Computer Models are the Primary Source of Information for All Weather Forecasts

4. Numerical Weather Prediction The use of computer models of the atmosphere to predict the weather given a set of current observations

5. According to Webster… n pre·dict: To state, tell about, or make known in advance, especially on the basis of special knowledge.

6. According to Meteorologists… n pre·dict: To state, tell about, or make known in advance, trying not to lie and always keeping the coin concealed from curious onlookers.

7. The Prediction Process Analyze Results Compare and Verify Observe the Atmosphere Identify and Apply Physical Laws Create a Mathematical Model Create and Run a Computer Model

8. The Prediction Process Analyze Results Compare and Verify Observe the Atmosphere Identify and Apply Physical Laws Create a Mathematical Model Create and Run a Computer Model

9. Observe the Atmosphere Upper-AirBalloons Satellites NEXRADDopplerRadar Commercial Aircraft AutomatedSurfaceNetworks

10. The Prediction Process Analyze Results Compare and Verify Identify and Apply Physical Laws Create a Mathematical Model Create and Run a Computer Model Observe the Atmosphere

11. Identify & Apply Physical Laws F=ma

12. The Prediction Process Analyze Results Compare and Verify Create a Mathematical Model Create and Run a Computer Model Observe the Atmosphere Identify and Apply Physical Laws

13. Create a Mathematical Model

14. The Prediction Process Analyze Results Compare and Verify Create and Run a Computer Model Observe the Atmosphere Identify and Apply Physical Laws Create a Mathematical Model

15. Create Computer Model

17. n Solve highly nonlinear partial differential equations n East/West Wind n North/South Wind n Vertical Wind n Temperature n Water Vapor n Cloud Water n Precipitating Water n Cloud Ice n Graupel n Hail n Surface Temperature n Surface Moisture n Soil Temperature n Soil Moisture n Sub-Grid Turbulence Run the Computer Model

18. n Over the course of a single forecast, the computer model solves billions of equations n Requires the fastest supercomputers in the world -- capable of performing trillions of calculations each second Run the Computer Model

19. n Finer resolution allows the model to capture more detail n Requires more computer power –doubling the number of grid boxes in 3-D increases the computer requirements by a factor of 16! More Power!!!

20. The Prediction Process Analyze Results Compare and Verify Observe the Atmosphere Identify and Apply Physical Laws Create a Mathematical Model Create and Run a Computer Model

21. Analyze the Results/Compare/Verify

22. In the Beginning… ENIAC

23. ENIAC Versus Today n Weighed 30 tons n Had 18,000 vacuum tubes, 1,500 relays thousands of resistors, capacitors, inductors n Peak speed of 5000 adds/second and 300 multiplies/sec n A 1.2 GHz Pentium IV processor is 500,000 times faster than the ENIAC n A desktop PC with 1 Gbyte of RAM can store 5 million times as much data as the ENIAC

24. 1950: The First Computer Weather Forecast Model 450 Miles

25. Todays Models

26. A Typical Forecast From Todays Models

27. What Causes the Major Problems?

28. Why the Lack of Detail in the Model? This Thunderstorm Falls Through the Cracks

29. Why the Lack of Detail in the Model?

30. A Foundational Question Can computer forecast technology...... explicitly predict this type of weather?

31. Would This Capability Be Useful? n Intense local weather causes economic losses in the US that average $300 M per week n Over 30% of the $10 trillion US economy is impacted each year n Commercial aviation loses $1-2 B per year due to diversions, delays, and cancellations (one diverted flight costs $150K) n Agriculture losses exceed $10 B/year

32. Dutton (2002)

34. Actual Losses – Extreme Events Pielke and Carbone (2002)

35. n Cargo shipping –Most expeditious route can save $40,000 per voyage – thousands of ships travel continuously! –Examples n High temperature and humidity can cause grain to germinate in cargo holds n Ships affected differently by wavelength of ocean swells n Commercial aviation –Single diversion averages $10,000 per domestic flight –Not unusual for one carrier to have 70 diversions at a hub for a single weather event (1-2 hours) –Cost is $700,000 per event –Industry loses $1-2 B per year due to weather Specific Examples Source: Weathernews, Inc.

36. About 50% of the loss is deemed preventable with better forecasts!

37. n 876 deaths annually due to severe weather n 7000+ weather-related traffic fatalities n 450,000 weather-related traffic injuries A Great Toll in Human Life

38. Model Types Global (2 weeks)

39. Model Types Global (2 weeks) Continental (few days)

40. Model Types Global (2 weeks) Continental (few days) Special

41. Model Types Global (2 weeks) Continental (few days) Special Operational

42. Model Types Global (2 weeks) Continental (few days) Regional (day) Local (few hours) Special Experimental

43. Increasing Skill Trends in Large-Scale Forecast Skill

44. 10 km1 km Crossing the Divide n The next quantum leap in NWP will come when we start resolving explicitly the most energetic weather features, e.g., individual convective storms in 3-D n For global models, the predictability increases for all resolvable scales as the spatial resolution increases –The improvement is bounded –Going finer than a few 10s of km in grid spacing gives little payoff 60 km30 km 10 km

45. Importance of Finer Grid Spacing in Models Courtesy NCAR 512 km

46. 256 km

47. 128 km

48. 64 km

49. 32 km

50. 16 km

51. 8 km

52. Each improvement requires 10X computer resources, total increase of 10,000,000! 4 km

53. Where Are We Today? n Tremendous advances are being made in the computer-based prediction of high-impact local weather, such as thunderstorms, owing to –Increases in computer power and networking capacity –Affordability of computers –Availability of fine-scale observations (NEXRAD Doppler radar) –Improved understanding of the atmosphere –Societal need, especially that of weather impacted industries (aviation, energy, recreation, defense)

54. Example : March 28, 2000 Fort Worth Tornadic Storms

55. Tornado

56. NWS 12-hr Computer Forecast Valid at 6 pm CDT No Explicit Evidence of Precipitation in North Texas

57. 6 pm 7 pm8 pm Radar Hourly Radar Observations (Fort Worth Shown by the Pink Star)

58. 6 pm 7 pm8 pm Radar Computer Forecast 2 hr 3 hr 4 hr

59. As a Forecaster Worried About This Reality… 7 pm

60. As a Forecaster Worried About This Reality… How Much Trust Would You Place in This Model Forecast? 3 hr 7 pm

61. Uncertainty n We never know the complete state of the atmosphere everywhere, with perfect accuracy n Small observation errors can grow with time in a forecast (chaos) n Rather than run a single forecast from one estimate of the current conditions, we run several based upon equally plausible initial conditions to account for observational uncertainty n This is ensemble forecasting

62. Actual Radar

63. Forecast #1 Forecast #2 Forecast #3 Forecast #5 Forecast #4 Actual Radar

64. Probability of Intense Precipitation Model Forecast Radar Observations

65. MUCH MORE Computing Power is Required!! Forecast #1 Forecast #50 Forecast #2 Forecast #3

66. Recent Real Time Experimental Forecasts Run by OU for the National Weather Service Actual Radar Observations

67. Recent Real Time Experimental Forecasts Run by OU for the National Weather Service 24 Hour Forecast Actual Radar Observations

68. A Foundational Question Can computer forecast technology...... explicitly predict this type of weather? The Answer Appears to be Yes, But New Methodologies May Be Needed…

69. Predicting Storms and Anticipating Tornadoes Requires Fine-Scale Observations NEXRAD Doppler Radar Network

70. Current Operational Radar System in US NEXRAD Doppler Radar Network

71. #2. Earths curvature prevents 72% of the atmosphere below 1 km from being observed #1. Operates largely independent of the prevailing weather conditions #3. Operates entirely independent from the models and algorithms that use its data The Limitations of NEXRAD

72. Source: NWS Office of Science and Technology NEXRAD The Consequence

73. New NSF Engineering Research Center for Adaptive Sensing of the Atmosphere (CASA) n UMass/Amherst, OU, CSU, UPRM n Concept: inexpensive, phased array Doppler radars on cell towers and buildings n Dynamically adaptive dynamic sensing of multiple targets while simultaneously meeting multiple end-user needs

75. Oklahoma Test Bed: Spring 2006

76. The $1M Question: Will Numerical Models Ever Be Able to Predict Tornadoes?

77. Schematic Diagram of a Supercell Storm (C. Doswell)

79. The Future n The National Weather Service will begin running models to explicitly predict thunderstorms n Private companies will play a major role in providing customized numerical forecasts for weather-sensitive industries, especially energy and aviation

80. The Future n Human forecasters will continue to be essential, though with changing roles

81. Contact Information Kelvin K. Droegemeier University of Oklahoma Sarkeys Energy Center, Suite 1110 100 East Boyd Street Norman, OK 73019 Email: kkd@ou.edu Phone: 405-325-0453 Fax: 405-325-7614 Mobile: 405-413-7847