Implications of Varying Time Steps within Operational Total Lightning Information

Over the last 10 to 15 years, real-time, operational use of total lightning information has continued to grow as new total lightning detection networks are brought online. As of summer 2014, National Weather Service forecast offices have access to data from ten different networks across the continental United States. Despite using nearly identical technology, the data from these networks are provided with minute differences in time step and resolution. For example, data from the North Alabama Lightning Mapping Array provided to Mid-South NWS offices are accumulated over a 2-minute time period on a 2 km by 2 km grid, while data from the Kennedy Space Center Lightning Detection And Ranging network were summed over a 1-minute period on a 1 km by 1 km grid.

While these kinds of variations may seem insignificant, experience with NWS offices suggests that the impact to operational forecasters is not. Higher refresh rates provide data on a more rapid basis for forecasters, but may also force them to sift through additional data. More importantly, it also divides total lightning trends into smaller bins; while this may be desirable under certain conditions, it may obscure meaningful changes, and might overwhelm forecasters unaccustomed to such rapid data assimilation and interpretation. As surprising as it may seem, it is possible that data could indeed update too quickly.

Time step considerations are all the more important as the community proceeds toward the launch of the Geostationary Lightning Mapper (GLM) instrument. While GLM will have an 8 km horizontal resolution at nadir (and unlike the ground-based networks, that will not change), it also promises to offer data latency of just 20 seconds.

This presentation will explore the impacts of different time steps as seen with existing ground-based networks and NWS offices, and extrapolate these issues forward to the use of GLM. It will also offer potential solutions in the form of new visualizations or data types.