The CSPP Geo Team is excited to announce the initial beta release of the LightningCast software package, which predicts the probability of a Geostationary Lightning Mapper (GLM) observation of lightning occurring in the next hour within a region of interest. The underlying algorithm uses a deep learning model trained on data from the GLM instrument on-board the GOES-16 satellite. However, the only input that is required for near real-time processing is imager data from the GOES-16, GOES-18 or Himawari-9 satellite.
The main capabilities offered in this beta release are:
- Generation of lightning predictions in several data formats (GeoJSON, NetCDF, GR placefiles)
- Generation of single band and RGB imagery with lightning probability contours overlaid
- Optional over-plotting of GLM Flash Extent Density (as generated by the CSPP Geo Gridded GLM package)
- User-definable regions of interest
- Optional parallax correction
- Optional AWIPS-compatible output
This package is based on science software that was developed by NOAA and CIMSS scientists John Cintineo, Mike Pavolonis, Justin Sieglaff and Levi Pfantz. For more information on the algorithm, refer to the 2022 paper ProbSevere LightningCast: A Deep-Learning Model for Satellite-Based Lightning Nowcasting. Another useful resource is the LightningCast Quick Guide.
CSPP Geo software is developed at CIMSS at the University of Wisconsin under NOAA / GOES-R funding.
Since this is a beta release, please bear in mind that it has not been as thoroughly tested as a production release. In addition, users should expect that functionality and interfaces may be changed for the v1 production release, which is planned in the next few months.
To obtain the software, system requirements, test data and documentation, please visit the CSPP Geo website (free registration required for downloads). The software package is self-contained; installation of additional third-party software is not required. Refer to the CSPP Geo LightningCast Software Users' Guide for instructions on installing and running the software, as well as a description of features and usage examples.
We are very interested in hearing feedback from beta testers (Is this useful to you? Did you run into any problems? What other capabilities would you like to see?). Please provide feedback to csppgeo.issues@ssec.wisc.edu.