One component of this project was the development of new deployable lightning mapping array (LMA) instrumentation to enhance MSFC’s ability to monitor severe storms. A total of 5 solar-powered LMA stations were built and deployed in the March-May 2016 time frame in North Alabama. This exercise demonstrated the reliability of a deployable LMA station for future thunderstorm field campaigns that examine the three-dimensional structure of lightning. Also, data from these deployable stations were combined with NASA’s existing nine-station North Alabama LMA and three visiting deployable LMA units from Texas Tech University to demonstrate how LMA sensors operating different VHF channels can be combined for scientific analysis of severe thunderstorms.
A second component to the NSTORM project was the development of an ensemble approach to severe-storm forecasting using the NASA-Unified Weather Research and Forecasting (NU-WRF) model. The model was run over a CONUS domain at 4 km with 56 vertical levels at 24-second time resolution. Three separate configurations were run for the 31 March 2016 severe weather event in the Southeast US to analyze the effect of different land-surface model parameterizations in the location and timing of convective development in the model run. The first set of simulations used 6 Global Ensemble Forecast System (GEFS) members with GEFS surface fields. This set of simulations served as a control run. A second set of simulations used the same 6 GEFS perturbed members with 3-km Land Information System Land Surface Model (LIS LSM) output for the surface fields. A third set of simulations used the same 6 GEFS perturbed members with 3-km LIS LSM data and 4-km Visible Infrared Imaging Radiometer Suite(VIIRS) green vegetation fraction (GVF) data to represent the land surface.
A final component of this project was the development of open-source software tools to facilitate the analysis of severe local storms. These libraries leverage existing software packages, such as the Department of Energy’s Python Atmospheric Radiation Measurement (ARM) Radar Toolkit (Py-ART), to produce customizable workflows for efficient quality control and analysis of arbitrary weather radar data. Unique capabilities being developed under this project include the ability to provide Geographic Information System (GIS) capable radar products in the GeoTIFF format, as well as an open-source package for performing multi-Doppler wind syntheses that are compatible with Py-ART. The latter is a collaboration with Argonne National Lab, NOAA, and the University of Oklahoma.