8.1
Improved real-time lightning trend products

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Wednesday, 26 January 2011: 1:30 PM
Improved real-time lightning trend products
602/603 (Washington State Convention Center)
Geoffrey T. Stano, ENSCO/SPoRT, Huntsville, AL; and B. Carcione and C. B. Darden
Manuscript (440.2 kB)

Since 2003, National Weather Service (NWS) forecast offices in and around northern Alabama have been receiving total lightning information from the North Alabama Lightning Mapping Array (NALMA). The lightning source density data have been frequently used to augment the warning decision-making process for a variety of severe thunderstorm threats. In particular, forecasters look for a “lightning jump”, or increase in source densities, in conjunction with other remote sensing data, such as Doppler radar, from the storm of interest to support the decision making process. Recent research has focused on quantifying the magnitude of the lightning jump to determine storm severity.

However, to date, diagnosis of lightning jumps in real-time has required a subjective and somewhat labor-intensive process within the NWS AWIPS (Advanced Weather Interactive Processing System). Efforts are underway to create new trending tools or match total lightning information to existing trending tools, but significant challenges persist in identifying cell centroids and presenting the trends to the user both in real time and within AWIPS.

Rather than focusing on specific cell-oriented trends, an alternative solution is being implemented by the NASA Short-term Prediction Research and Transition (SPoRT) program and NWS office in Huntsville, Alabama. In addition to the existing source density product already provided by SPoRT, a new product displays the maximum source density observed at each 2-kilometer by 2-kilometer grid box over the last 30 minutes. This provides forecasters with a faster way to assess the history of several storms simultaneously further improving the forecasters' situational awareness. This paper will provide some examples of this new maximum density trend, or “lightning track”, product and discuss its operational utility. Additionally, efforts to unite this product with a rate of change algorithm on a grid cell basis will also be discussed.