REU Site on Integrated Machine Learning Systems:
Weather Prediction


Amy McGovern

Kelvin K. Droegemeier Jerry Brotzge Roger A. Brown


NOAA Central
       Library; OAR/ERL/National Severe Storms Laboratory (NSSL)

  • Improving the anticipation of severe weather (McGovern, Brotzge, R. A. Brown, Droegemeier). Approximately 25% of all tornadoes are not warned in advance, and nearly 80% of all tornado warnings turn out to be false alarms (when no tornado actually occurs). Too many false alarms can increase public apathy, and tornadoes without advance public warning can cause otherwise preventable deaths and injuries. This pro ject will use data mining and knowledge discovery methods to extract storm type information and attributes from weather radar data and model output to identify common relationships among storm events associated with a false alarm or missed tornado warning. Once these relationships are known, new techniques can be developed to improve our tornado warning process (McGovern et al.,2007; Hiers et al., 2008).

  • Improving the automatic selection of WSR-88D radar scanning strategies (McGovern, R. A. Brown). Within its nationwide network of WSR-88D radars, the National Weather Service uses different radar scanning strategies depending on the type of storms occurring within the coverage region of a given radar. At the present time, forecasters have to monitor the radar display and change the scanning strategy manually. We will investigate how data mining and ML can be used to ob jectively identify storm type and thereby automatically select the appropriate scanning strategy (Gagne II et al., 2008).

  • Establishing the limits of predictability of tornadic outbreaks (Richman, McGovern). At the present, severe storm outbreak type is forecast 24 hours in advance. Through data mining and modeling of the differences between severe weather outbreaks and tornadic outbreaks, it is expected that signatures of tornadic outbreaks can be made at least several days in advance. This project applies ML techniques, such as support vector machines, to classify outbreak type and establish the limits of predictability in both severe weather and tornadic events.

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Last modified: Wed Feb 20 00:04:51 2008