Description
Lightning measurements generate hierarchically clustered, time-tagged, georeferenced datasets, which is rare in the Earth sciences. Therefore, practical knowledge concerning lightning data formats, services, and visualization approaches is underdeveloped. Using data from new instruments and recent field campaigns we will show the utility of xarray and other Python tools for traversing and visualizing lighting data alongside other meteorological datasets, with a view toward reference implementations that speed use of lightning data by the wider scientific and operational forecasting community.