The 2017 Summer Experiment at the Aviation Weather Testbed has begun, it’s focus centered on mock ups of the operational Traffic Flow Management (TFM) Convective Forecast desk (TCF), and on the Digital Aviation Services ceiling and visibility effort. Yesterday was a spin up day, allowing forecasters to peruse various new datasets as they explored the mock desks.
While the tropics were not in the overall experiment plan, it was difficult not to turn the attention southward to Franklin, particularly when new Geostationary Lightning Mapper (GLM) data was available. The GLM data is optical, meaning that the concept of detecting lightning is much different from typical terrestrial networks. Therefore, developers at the AWC have been exploring various methods of displaying this data as a grid. Below is an example of the GLM groups, a ten minute average estimated every two minutes.
Participants also were able to look at groups as an energy density, also averaged over ten minutes every two.
These grids were designed for the AWC’s N-AWIPS systems, both created on an eighth of a degree grid over the entirety of the GOES-16 domain. When zoomed in, the grid points are noted to be pretty coarse. However, these particular displays were designed with AWC tropical and global forecasters in mind, folks whose perspective is broader. In their eyes, this coarse resolution isn’t a big issue. For others, such as TCF and Convective SIGMET, it may be that a similar display with higher resolution will need to be created. Additionally, gridded plots of the group energy density are also being added to the selection for later in the experiment.
Why gridded plots? As mentioned, GLM is an optical sensor, collecting lightning data in a very different manner than ground based networks… i.e. point data from that optical sensor does not equate to point data taken from a ground-based sensor calibrated to pick up a specific frequency. Therefore, it is important for forecasters to examine data with a different perspective than they have with other lightning data in the past in order to glean it’s value. For that reason, AWC has started the exploration with grids.
Over the next two weeks, we plan to take a cautious approach, keeping the known data quality issues in mind while collecting feedback and exposing forecasters to this new exciting data.