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Weather Forecasts: From Tragedy to Triumph Cliff Mass University of Washington

Weather Forecasts: From Tragedy to Triumph Cliff Mass University of Washington. 2012: Hurricane Sandy. 125 dead, 60+ billion dollars damage. Well predicted over a week ahead of time. ECMWF Forecast of Sea Level Pressure. 1938 Hurricane: Similar in Strength to Sandy. Nearly a thousand died.

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Weather Forecasts: From Tragedy to Triumph Cliff Mass University of Washington

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  1. Weather Forecasts: From Tragedy to TriumphCliff MassUniversity of Washington

  2. 2012: Hurricane Sandy 125 dead, 60+ billion dollars damage

  3. Well predicted over a week ahead of time ECMWF Forecast of Sea Level Pressure

  4. 1938 Hurricane: Similar in Strength to Sandy Nearly a thousand died

  5. Not forecast the day before

  6. 1962 Columbus Day Storm

  7. Not forecast the day before either Seattle Times

  8. Jan 1993: Inauguration Day Storm: Near Perfect Forecast

  9. Something Has ChangedBefore 1990 the National Weather Service got virtually every major storm wrong, even the day before.After 1990, they gave good warnings for nearly all.

  10. Forecast Skill Improvement National Weather Service Forecast Error Better Year

  11. Skill Improvements (ECMWF) Major improvements, mainly due to satellite data and improved models

  12. The Revolution in Weather Prediction Technology

  13. The Key Technology of Modern Weather Forecasting is Numerical Weather Prediction

  14. Numerical Weather Prediction • The basic idea is that if you can determine the current state of the atmosphere (known as the initialization) , you can predict the future using the equations that describe the physics of the atmosphere. • These equations can be solved on a three-dimensional grid.

  15. The “Primitive” Equations

  16. Numerical Weather Prediction • Numerical weather prediction is limited by the available computer resources. • As computer speed increases, the number of grid points can be increased. • More (and thus) closer grid points means we can simulate (forecast) smaller scale features. National Weather Service Weather Prediction Computer

  17. NGM, 80 km,1995

  18. 2007-2008 4-km MM5 Real-time

  19. 1.33 km resolution available on the UW web site

  20. But just as important has been the weather data revolution, with satellites giving us three dimension data over the entire planet

  21. Example: The Pacific Data Void No Longer Exists

  22. Cloud Track Winds

  23. Better than Star Trek!

  24. NOAA Polar Orbiter Weather Satellite

  25. Satellite Sensors Provide Thousands of High Quality Vertical Soundings Daily over the Pacific

  26. Cosmic GPS Satellites Provide More Soundings!

  27. We are now starting to see frequent examples of forecast skill past one week: Hurricane Sandy is only one example

  28. Observed 180 hr (7.5 days)

  29. Forecast Skill Will Continue to Extend Further in Time…with limits (about 2 weeks) • More satellite assets will provide a far better description of the atmosphere. • Better models and higher resolution • Better data assimilation: how we use the observations to produce an initialization for our models.

  30. Increasing Resolution and Better Models Will Not Be EnoughThe Next Major Revolution in Numerical Weather Prediction Will Come Elsewhere

  31. The Transition from Deterministic to Probabilistic Prediction

  32. A Fundamental Problem • The way we have been forecasting has been essentially flawed. • 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….

  33. A Fundamental Problem • Similarly, uncertainty in our model physics (e.g., clouds and precipitation processes) also produces uncertainty in forecasts. • Thus, all forecasts have some uncertainty. • The uncertainty generally increases in time.

  34. This is Ridiculous!

  35. Forecast Probabilistically • We should be using probabilities for all our forecasts or at least providing the range of possibilities. • There is an approach to handling this issue that is being explored by the forecasting community…ensemble forecasts

  36. Ensemble Prediction • Instead of making one forecast…make many…each with a slightly different initialization or different model physics. • Possible to do this now with the vastly greater computation resources that are available.

  37. Ensemble Prediction • Can use ensembles to give the probabilities that some weather feature will occur. • Ensemble mean is more accurate than any individual member. • Can also predict forecast skill! • When forecasts are similar, forecast skill is generally higher. • When forecasts differ greatly, forecast skill is less.

  38. Prediction! • The meteorological profession is rapidly gaining the ability to produce high-resolution probabilistic weather forecasts AND analyses. • Probabilistic forecasts and analyses will be available for a wide range of weather parameters.

  39. The Nowcasting Revolution

  40. AMS Nowcasting Definition A description of current weather and a short-term forecast varying from minutes to a few hours; typically shorter than most operational short-range forecasts. American Meteorological Society’s Glossary of Weather and Climate

  41. During the past decade or so the geographical and temporal detail the weather profession can provide has greatly increased. • High resolution forecasting, NWS forecasts on a 2.5 km grid, radar data, satellite imagery, huge numbers of surface stations, and now probabilistic prediction!

  42. Example: The Pacific Northwest Based on 72 different networks 3000-4000 observations per hour over WA and OR

  43. Traditional Approaches of Weather Information Dissemination Are Incapable of Delivering the Specificity and Detail Meteorologists Can Provide Typical TV weathercasters have only 2.5 minutes!

  44. Many of us worried about this problem in the 90’s but now the solution is literally at hand

  45. Smartphones are Ideal for Weather Data Delivery! • Lots of bandwidth • They know where they are, so forecast information can be tailored to the user • Substantial computational capacity.

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