TJ4.3 Lightning Observations and Research: Integration with and Feedback from Operational Meteorology

Monday, 23 January 2017: 4:30 PM
Conference Center: Tahoma 1 (Washington State Convention Center )
Kristin Calhoun, OU/CIMMS, NOAA/NSSL, Norman, OK

Since 2010, the Hazardous Weather Testbed (HWT) has been successfully utilized to provide forecasters with a first-hand look at the latest research concepts and products integrating total lightning data while also educating lightning research scientists on the challenges, needs, and constraints of National Weather Service (NWS) warning forecasters.  With the launch of GOES-R and the Geostationary Lightning Mapper, in addition to multiple ground-based detection systems already available to forecasters, lightning data has the potential to provide information for convective storms across large territories, including typically data sparse regions such as offshore and within mountainous terrain. 

During live HWT experiments, one to five-minute grids of total lightning density and subsequent lightning-derived algorithms, such as the lightning jump, have been incorporated by NWS forecasters within their real-time warning-decision process for various storm modes over multiple regions of the United States. Additionally in spring of 2016, as part of the Prototype Probabilistic Hazard Information (PHI) experiment, forecasters were asked to use a new Multi-Radar/Multi-Sensor (MRMS) cloud-to-ground (CG) lightning probability guidance to create rapidly updating probability grids and warnings for the threat of CG lightning for 0-60 minutes.  This was the first time NWS forecasters were asked to formally issue CG lightning warnings and hazard information.

Both formal and informal research protocols have been used to collect observations, data, and feedback in the HWT; these included online surveys, live blogging, individual interviews, and group discussions.   In their evaluations, forecasters have noted that total lightning data and algorithms could be an incredibly useful situational awareness tool and may be able to provide additional guidance during a warning decision. NWS forecasters were also receptive to the probabilistic CG lightning algorithms and guidance.  Emergency managers participating in the experiment were also particularly interested of CG lightning products created by the NWS forecasters, noting a lack of current hazard guidance from the NWS.  However, forecasters have commonly noted a current lack of training and understanding in regards to various types of lightning data available and for forecasting CG lightning.  This presentation will visit the recent history of total and CG lightning use in warning operations as well as current and future plans for implementation, use, and training.

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