Total Lightning Visualizations to Enhance Forecast Operations
Stano, Geoffrey T; Carcione, Brian

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). Since then, the NASA Short-term Prediction Research and Transition (SPoRT) program has expanded its efforts to include networks at Kennedy Space Center and Washington D.C. The main product produced for these networs is the lightning source density product. These 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 SPoRT program and evaluated by the National Weather Service office in Huntsville, Alabama and a GOES-R version in the Hazardous Weather Testbed s Spring Program in Norman, Oklahoma. 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. Efforts to unite this product with a rate of change algorithm on a grid cell basis will also be discussed. Lastly, this presentation will briefly look into environmental situations where total lightning has not been effective. In particular, the 21 January 2010 EF-2 tornado in Huntsville, Alabama will be discussed. This last section will aid end users in knowing when total lightning is or is not a viable tool, which will be particularly important as we look ahead to the GOES-R era with the Geostationary Lightning Mapper.