Cliff Mass: Big Data and Weather Prediction - Seattle Interactive 2015
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The document discusses the integration of big data in weather forecasting, highlighting the challenges and solutions related to data collection, quality control, and uncertainty. It emphasizes the significance of numerical weather prediction and ensemble prediction methods, which enhance forecast accuracy. Furthermore, it suggests leveraging visualization technologies to improve public understanding and communication of weather events.
Cliff Mass: Big Data and Weather Prediction - Seattle Interactive 2015
- 1. Big Data and Weather Forecasting
- 2. Weather prediction has been a big data problem for a long time Petabytes per day
- 3. Big data and weather: the observations
- 4. Weather Satellites: Meteorological Data Everywhere
- 6. NOAA Polar Orbiter Weather Satellite NOAA Polar Orbiter
- 8. Cosmic GPS satellites provide more soundings
- 12. Aircraft
- 13. Weather Radar
- 17. Some of the smartphones/pads with pressure sensors
- 18. 114K Pressures in One Hour Over US From Three Apps
- 19. Big Problem
- 20. Quality Control of Big Weather Data
- 21. The other meteorological big data: forecast models
- 22. The First Numerical Weather Prediction
- 24. Observation Collection Needed to Create the Initialization
- 26. These equations together are a time machine!
- 27. Numerical Weather Prediction National Weather Service Weather Prediction Computer
- 31. 1.33 km resolution available on the UW web site
- 32. Before the models are run there is another step: data assimilation
- 35. Surface analysis of sea level pressure, surface wind, low-atmosphere temperature
- 36. But there is another issue for big data users…meteorological and otherwise.
- 37. Uncertainty
- 38. Big Data and Uncertainty
- 40. The Technology of Uncertainty
- 43. Ensemble Prediction • Can use ensembles to give the probabilities that some weather feature will occur. Not just precipitation. • Ensemble mean is more accurate than any individual member. •When forecasts are similar, forecast skill of the ensemble mean is generally higher.
- 45. The UW has done this: PROBCAST
- 46. Decision Making and Uncertainty
- 48. Big Data and Visualization
- 49. The role of visualization
- 51. And there are some recent non-Xbox examples
- 53. Imagine if we developed the infrastructure for weather animations, SHOWING people what the weather would LOOK like and SOUND like.
- 54. Connect Weather Simulation and Videogame Technologies
- 58. Big weather data can also help in the short-term forecasting
- 59. During the past decade the volume of information on what is occurring now in local areas has increased dramatically
- 60. And we have the perfect delivery tool: Smartphones
- 66. The End