Disaster Prediction and Warning

Disaster Types

Disasters are classified as natural or technological. Natural hazards include floods, earthquakes, hurricanes, storm surges, tornadoes, wildfires, landslides, tsunamis, volcanic eruptions, severe winter storms, droughts, extreme heat, coastal erosion, thunderstorms, hailstorms, snow avalanches, land subsidence, expansive soils, and dam failures. Technological hazards include fires, hazardous material accidents, nuclear plant accidents, terrorism, and biological or chemical weapons. Not all disasters are equally predictable. Hurricanes can be predicted several days in advance, whereas tornadoes and earthquakes often take communities by surprise. Warnings therefore tend to be much more successful for hurricanes in comparison with tornadoes. Earthquakes are virtually unpredictable, and therefore, society cannot be warned with much success.

Prediction Technologies

Disaster warning systems and the communications technologies through which warnings reach [Page 760]communities remain limited without improvements in prediction technology. One goal of improving prediction technologies is to increase the lead time. Lead time is the amount of time between when a warning or alert is issued and when the hazard for which the alert was issued strikes the community. If, for instance, a warning was issued for a tornado at 10:00 a.m. and the tornado struck at 10:10 a.m., the warning generated only 10 minutes of lead time. Short lead times (the result of poor predictive technology) give communities little time to prepare, which, in turn, leads to increases in the number of fatalities and injuries. Tornadoes are one disaster for which prediction technologies are rapidly evolving.

The Warning Decision Support System (WDSS) is an example of a new technology that is also communication friendly. WDSS applies automated algorithms through the WSR88-D Doppler weather radar system to automatically detect and predict severe weather, revealing weather conditions using the color display system that is now widely known to the public and used by weather forecasters in the communication process. The Automated Weather Interactive Processing System (AWIPS) serves to integrate the data that come from Doppler, ASOS (Automated Surface Observing Systems), GOES-8 and G0ES-10 (Geostationary Operational Environmental Satellites), wind profilers, and other observing systems.

A tsunami buoy on the National Oceanic and Atmospheric Administration ship Ronald H. Brown in the Pacific Ocean. It is part of an image that will provide an early warning in the event of a tsunami.

Source: NOAA.

Recently three-dimensional (3D) numerical modeling studies of tornadoes have been carried out. These models show a tornado's structure and wind field. Beyond providing information on the formation of a tornado, the Doppler system has increased the lead time for tornado warnings. These systems can be set so that an automatic alert goes off in the forecast office whenever a storm, within that station's radar umbrella, exceeds a given value of reflectivity. The challenge lies in adjusting the 120 geographically dispersed Doppler systems to local climatologies and developing new algorithms for tornado detection and warnings. In addition, prediction can be improved with 3D numerical prediction improvements.

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