The Geostationary Lightning Mapper (GLM) on the Geostationary Operational Environmental Satellite‐R series of weather satellites provides point geolocations of lightning flashes that are further comprised of a hierarchy of geolocated groups and events. This study describes an open‐source method for reconstruction of imagery from those point detections that retains the quantitative physical measurements made by GLM, restores the spatial footprint of the events, and connects that spatial footprint to the groups and flashes. Meteorological signals are demonstrated to be more apparent in the gridded imagery than in the point detections, leading to their adoption by the United States National Weather Service as the first GLM product available in their real‐time displays. Analysis of a mesoscale convective system over Argentina confirms that there is a class of propagating lightning observed by GLM (distinct from that in storm cores) that can be visualized and quantified using our imagery‐based approach.

Composite analyses of the atmosphere over the central United States during elevated thunderstorms producing heavy rainfall are presented. Composites were created for five National Weather Service County Warning Areas (CWAs) in the region. Events studied occurred during the warm season (April–September) during 1979–2012. These CWAs encompass the region determined previously to experience the greatest frequency of elevated thunderstorms in the United States. Composited events produced rainfall of >50 mm 24 hr−1 within the selected CWA. Composites were generated for the 0–3 hr period prior to the heaviest rainfall, 6–9 hours prior to it, and 12–15 hours prior to it. This paper focuses on the Pleasant Hill, Missouri (EAX) composites, as all CWA results were similar; also these analyses focus on the period 0–3 hours prior to event occurrence. These findings corroborate the findings of previous authors. What is offered here that is unique is (1) a measure of the interquartile range within the composite mean fields, allowing for discrimination between variable fields that provided a strong reliable signal, from those that may appear strong but possess large variability, and (2) composite soundings of two subclasses of elevated thunderstorms. Also, a null case (one that fits the composite but failed to produce significant rainfall) is also examined for comparison.

The empirical Probability of Severe (ProbSevere) model, developed by the National Oceanic and Atmospheric Administration (NOAA) and the Cooperative Institute for Meteorological Satellite Studies (CIMSS), automatically extracts information related to thunderstorm development from several data sources to produce timely, short-term, statistical forecasts of thunderstorm intensity. More specifically, ProbSevere utilizes short-term numerical weather prediction guidance (NWP), geostationary satellite, ground-based radar, and ground-based lightning data to determine the probability that convective storm cells will produce severe weather up to 90 min in the future. ProbSevere guidance, which updates approximately every 2 min, is available to National Weather Service (NWS) Weather Forecast Offices with very short latency. This paper focuses on the integration of ground-based lightning detection data into ProbSevere. In addition, a thorough validation analysis is presented. The validation analysis demonstrates that ProbSevere has slightly less skill compared to NWS severe weather warnings, but can offer greater lead time to initial hazards. Feedback from NWS users has been highly favorable, with most forecasters responding that ProbSevere increases confidence and lead time in numerous warning situations.

With the launch of the Geostationary Operational Environmental Satellite–R (GOES-R) series in 2016, there will be continuity of observations for the current GOES system operating over the Western Hemisphere. The GOES-R Proving Ground was established in 2008 to help prepare satellite user communities for the enhanced capabilities of GOES-R, including new instruments, imagery, and products that will have increased spectral, spatial, and temporal resolution. This is accomplished through demonstration and evaluation of proxy products that use current GOES data, higher-resolution data provided by polar-orbiting satellites, and model-derived synthetic satellite imagery. The GOES-R demonstration products presented here, made available to forecasters in near–real time (within 20 min) via the GOES-R Proving Ground, include the 0–9-h NearCast model, 0–1-h convective initiation probabilities, convective cloud-top cooling, overshooting top detection, and a pseudo–Geostationary Lightning Mapper total lightning tendency diagnostic. These products are designed to assist in identifying areas of increasing convective instability, pre-radar echo cumulus cloud growth preceding thunderstorm formation, storm updraft intensity, and potential storm severity derived from lightning trends. In turn, they provide the warning forecaster with improved situational awareness and short-term predictive information that enhance their ability to monitor atmospheric conditions preceding and associated with the development of deep convection, a time period that typically occurs between the issuance of National Weather Service (NWS) Storm Prediction Center convective watches and convective storm warnings issued by NWS forecast offices. This paper will focus on how this GOES-R satellite convective toolkit could have been used by warning forecasters to enhance near-storm environment analysis and the warning-decision-making process prior to and during the 20 May 2013 Moore, Oklahoma, tornado event.