ATM Seminar Series: Overview of the Geostationary Lightning Mapper (GLM)

Dr. Geoffrey Stano is part of NASA’s Short-term Prediction Research and Transition Center (SPoRT). He has been involved with the GOES-R Proving Ground since 2009, and in 2016 began serving as the Satellite Liaison for the Geostationary Lightning Mapper (GLM). His role has been to support the National Weather Service in preparing for the GLM. This has been done through webinars, training sessions, and the development of training modules for the forecasters. This seminar will provide a short background on the NASA SPoRT program as well as the GLM instrument. The remainder of the presentation will focus on real-world applications of the GLM data as it is being integrated into the National Weather Service.

Dr. Geoffrey Stano

Dr. Stano has focused on operational applications research, specifically with lightning observations, for the past 15 years. This has included work with ground-based lightning mapping arrays to the first of its kind Geostationary Lightning Mapper aboard the new GOES-R series. In addition to his role as a lightning expert and trainer with the NASA SPoRT center, he currently serves as the chair for the American Meteorological Society’s Atmospheric Electricity Scientific and Technological Activities Commission.

ATM Seminar Series: Atmospheric Rivers

Atmospheric rivers are relatively long, narrow areas of moisture transport. They’re responsible for approximately 90% of meridional water vapor transport, and also for many high impact rainfall events. This presentation will provide an overview of the structure, climatology, and impacts of atmospheric river events. Predictability and forecast tools will also be discussed.

Dr. Greg West is a Research Associate in the Weather Forecast Research Team at the University of British Columbia in Vancouver, BC. Research projects he is involved with center around improving weather forecasts for clean energy production for the province’s primary electric utility, BC Hydro. This primarily includes improvement of probabilistic forecast post-processing via machine learning methods, evaluation of forecasts, and creation of innovative new forecast tools such as situational awareness forecast indices.