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NASA NTRS · 20160014743

Preparations for Integrating Space-Based Total Lightning Observations into Forecast Operations

Abstract

NASA's Short-term Prediction Research and Transition (SPoRT) Center has been a leader in collaborating with the United States National Weather Service (NWS) offices to integrate ground-based total lightning (intra-cloud and cloud-to-ground) observations into the real-time operational environment. For much of these collaborations, the emphasis has been on training, dissemination of data to the NWS AWIPS system, and focusing on the utility of these data in the warning decision support process. A shift away from this paradigm has occurred more recently for several reasons. For one, SPoRT's collaborations have expanded to new partners, including emergency managers and the aviation community. Additionally, and most importantly, is the impending launch of the GOES-R Geostationary Lightning Mapper (GLM). This has led to collaborative efforts to focus on additional forecast needs, new data displays, develop training for GLM uses based on the lessons learned from ground-based lightning mapping arrays, and ways to better relate total lightning data to other meteorological parameters. This presentation will focus on these efforts to prepare the operational end user community for GLM with an eye towards sharing lessons learned as EUMETSAT prepares for the Meteosat Third Generation Lightning Imager. This will focus on both software and training needs. In particular, SPoRT has worked closely with the Meteorological Development Laboratory to create the total lightning tracking tool. This software allows for NWS forecasters to manually track storms of interest and display a time series trend of observations. This tool also has been expanded to work on any gridded data set allowing for easy visual comparisons of multiple parameters in addition to total lightning. A new web display has been developed for the ground-based observations that can be easily extended to satellite observations. This paves the way for new collaborations outside of the NWS, both domestically and internationally, as the web display will be functional on PCs and mobile devices. Furthermore, SPoRT has helped developed the software plug-in to visualize GLM data. Examples using the official GLM proxy product will be used to provide a glimpse as to what real-time GLM and likely MTG-LI data will be in the near future.

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BibTeXRIS

Stano, Geoffrey T., Fuell, Kevin K., Molthan, Andrew L.. 2016-09-26. Preparations for Integrating Space-Based Total Lightning Observations into Forecast Operations. https://ntrs.nasa.gov/citations/20160014743

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A Radar Investigation of Precipitation Properties During Discrepancies Between GOES-16 GLM and LMA Observed Flash Rates in the Skyline Alabama Supercell of 22 April 2017

Conclusions: GLM (Geostationary Lightning Mapper) flash rates were 2 to 5 times lower than LMA (Lightning Mapping Array) in an Alabama supercell that was tracked using a combination of GLM flash initiation density and VIL (Vertically Integrated Liquid); Since most lightning was initiating at 8-9 kilometers (and not at low levels) according to LMA, flash height does not appear to be a primary factor in low GLM flash rates; When (LMA-GLM) flash rate differences were largest, the LMA observed flash areas were relatively small (and vice versa); Flash size may be a primary factor in low GLM flash rates due to detectability and/or flash clustering issues with small flashes within the coarse 8 kilometers by 8 kilometers resolution; High cloud liquid water droplet concentrations were inferred indirectly from riming necessary for large radar MESH (Maximum Expected Size of Hail), VIL and hail/graupel volumes. High cloud water droplet concentrations in supercells may decrease GLM detection efficiency due to optical extinction of near IR (near Infrared) emitted by lightning as it moves through cloud; Despite large flash rate differences, GLM & LMA lightning jumps during robust supercell generally agreed with each other and radar trends in HID (Radar Reflectivity and Hydrometeor Identification), MESH and VIL.However, more LMA jumps (than GLM) in developing supercell and more GLM jumps (than LMA) in weak to decaying supercell. Future work: improve GLM tracking.

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