The Application of Total Lightning Data in the Warning Decision Making Process
Priscilla V. Bridenstine, NOAA/NWS, Huntsville, AL; and C. B. Darden, J. Burks, and S. J. Goodman
The National Weather Service Forecast Office in Huntsville, Alabama is co-located with the University of Alabama in Huntsville and scientists from NASA. The collaboration between NASA atmospheric scientists and NWS meteorologists have provided forecasters several unique datasets to utilize during forecast and warning operations. The North Alabama Lightning Mapping Array (LMA) is one such dataset that became operational in November 2001 and has proven beneficial for the short-term forecasting of severe and hazardous weather.
The North Alabama LMA, developed by NASA scientists and centered in Huntsville, Alabama, has allowed NWS offices across the region the opportunity to view in-cloud, cloud-to-cloud and cloud-to-air lightning in near real-time. This is in addition to cloud-to-ground lightning data readily available to forecasters through the NWS Advanced Weather Interactive Processing System (AWIPS). Total lightning displays available through AWIPS have been used numerous times since becoming operational in May 2003. Access to total lightning data has enhanced situational awareness in the local forecast offices and has added additional confidence to the warning decision making process.
This study will focus on several cases in which total lightning information was successfully utilized with radar data to enhance warning operations during severe weather events. The added operational benefits of this data during critical weather will be illustrated by comparing radar signatures to the time rate-of-change of total lightning. This lightning trend information can potentially give the warning forecaster additional time to pinpoint severe storm evolution and distinguish between intensifying and weakening storms. Effectively using the LMA data will allow forecasters the ability to focus attention on the greatest severe weather threat, which can lead to improved warning lead times.
Extended Abstract (216K)
Poster Session 1, Poster Session 1
Monday, 10 January 2005, 2:30 PM-4:00 PM
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