The GOES-R GeoStationary Lightning Mapper (GLM)

Abstract The Geostationary Operational Environmental Satellite R-series (GOES-R) is the next block of four satellites to follow the existing GOES constellation currently operating over the Western Hemisphere. Advanced spacecraft and instrument technology will support expanded detection of environmental phenomena, resulting in more timely and accurate forecasts and warnings. Advancements over current GOES capabilities include a new capability for total lightning detection (cloud and cloud-to-ground flashes) from the Geostationary Lightning Mapper (GLM), and improved cloud and moisture imagery with the 16-channel Advanced Baseline Imager (ABI). The GLM will map total lightning activity continuously day and night with near-uniform storm-scale spatial resolution of 8 km with a product refresh rate of less than 20 s over the Americas and adjacent oceanic regions in the western hemisphere. This will aid in forecasting severe storms and tornado activity, and convective weather impacts on aviation safety and efficiency. In parallel with the instrument development, an Algorithm Working Group (AWG) Lightning Detection Science and Applications Team developed the Level 2 (stroke and flash) algorithms from the Level 1 lightning event (pixel level) data. Proxy data sets used to develop the GLM operational algorithms as well as cal/val performance monitoring tools were derived from the NASA Lightning Imaging Sensor (LIS) and Optical Transient Detector (OTD) instruments in low Earth orbit, and from ground-based lightning networks and intensive prelaunch field campaigns. The GLM will produce the same or similar lightning flash attributes provided by the LIS and OTD, and thus extend their combined climatology over the western hemisphere into the coming decades. Science and application development along with preoperational product demonstrations and evaluations at NWS forecast offices and NOAA testbeds will prepare the forecasters to use GLM as soon as possible after the planned launch and checkout of GOES-R in late 2015. New applications will use GLM alone, in combination with the ABI, or integrated (fused) with other available tools (weather radar and ground strike networks, nowcasting systems, mesoscale analysis, and numerical weather prediction models) in the hands of the forecaster responsible for issuing more timely and accurate forecasts and warnings.

[1]  Conrad L. Ziegler,et al.  The Implementation of an Explicit Charging and Discharge Lightning Scheme within the WRF-ARW Model: Benchmark Simulations of a Continental Squall Line, a Tropical Cyclone, and a Winter Storm , 2013 .

[2]  P. Gatlin,et al.  A Total Lightning Trending Algorithm to Identify Severe Thunderstorms , 2010 .

[3]  Lawrence D. Carey,et al.  Regional Comparison of GOES Cloud-Top Properties and Radar Characteristics in Advance of First-Flash Lightning Initiation , 2013 .

[4]  W. David Rust,et al.  A Comparison of the Optical Pulse Characteristics of Intracloud and Cloud-to-Ground Lightning as Observed above Clouds. , 1988 .

[5]  S. Goodman,et al.  Forecasting Lightning Threat Using Cloud-Resolving Model Simulations , 2009 .

[6]  K. Cummins,et al.  Combined Satellite- and Surface-Based Estimation of the Intracloud Cloud-to-Ground Lightning Ratio over the Continental United States , 2001 .

[7]  Osmar Pinto,et al.  Maximum cloud-to-ground lightning flash densities observed by lightning location systems in the tropical region: A review , 2007 .

[8]  Richard J. Blakeslee,et al.  Global lightning activity from the ENSO perspective , 2008 .

[9]  John M. Hall,et al.  The North Alabama Lightning Mapping Array: Recent Severe Storm Observations and Future Prospects , 2005 .

[10]  E. Anagnostou,et al.  Improving Convective Precipitation Forecasting through Assimilation of Regional Lightning Measurements in a Mesoscale Model , 2003 .

[11]  Michael D. King,et al.  Our changing planet : the view from space , 2007 .

[12]  Stanley G. Benjamin,et al.  CONVECTIVE-SCALE WARN-ON-FORECAST SYSTEM: A vision for 2020 , 2009 .

[13]  Steven D. Miller,et al.  The GOES-R Proving Ground: Accelerating User Readiness for the Next-Generation Geostationary Environmental Satellite System , 2012 .

[14]  Philip A. Durkee,et al.  The Definition of GOES Infrared Lightning Initiation Interest Fields , 2010 .

[15]  M. Llasat,et al.  Predicting the potential for lightning activity in Mediterranean storms based on the Weather Research and Forecasting (WRF) model dynamic and microphysical fields , 2010 .

[16]  A. Clark,et al.  Predicting Cloud-to-Ground and Intracloud Lightning in Weather Forecast Models , 2012 .

[17]  Richard J. Blakeslee,et al.  The detection of lightning from geostationary orbit , 1989 .

[18]  Dennis E. Buechler,et al.  THE BEHAVIOR OF TOTAL LIGHTNING ACTMTY IN SEVERE FLORIDA THUNDERSTORMS , 2022 .

[19]  W. J. Koshak,et al.  Optical Characteristics of OTD Flashes and the Implications for Flash-Type Discrimination , 2010 .

[20]  John A. Knaff,et al.  Tropical Cyclone Lightning and Rapid Intensity Change , 2012 .

[21]  W. J. Koshak,et al.  Retrieving the Fraction of Ground Flashes from Satellite Lightning Imager Data Using CONUS-Based Optical Statistics , 2011 .

[22]  Richard J. Blakeslee,et al.  Gridded lightning climatology from TRMM-LIS and OTD: Dataset description , 2014 .

[23]  William J. Koshak,et al.  Assessing the performance of the Lightning Imaging Sensor (LIS) using Deep Convective Clouds , 2014 .

[24]  Christopher J. Schultz,et al.  Preliminary Development and Evaluation of Lightning Jump Algorithms for the Real-Time Detection of Severe Weather , 2009 .

[25]  Conrad L. Ziegler,et al.  A Lightning Data Assimilation Technique for Mesoscale Forecast Models , 2007 .

[26]  K.L. Cummins,et al.  An Overview of Lightning Locating Systems: History, Techniques, and Data Uses, With an In-Depth Look at the U.S. NLDN , 2009, IEEE Transactions on Electromagnetic Compatibility.

[27]  Paul Krehbiel,et al.  A GPS‐based three‐dimensional lightning mapping system: Initial observations in central New Mexico , 1999 .

[28]  Christopher J. Schultz,et al.  Lightning and Severe Weather: A Comparison between Total and Cloud-to-Ground Lightning Trends , 2011 .

[29]  Steven J. Goodman,et al.  Comparison of ground‐based 3‐dimensional lightning mapping observations with satellite‐based LIS observations in Oklahoma , 2000 .

[30]  Steven Businger,et al.  The Impact of Lightning Data Assimilation on a Winter Storm Simulation over the North Pacific Ocean , 2009 .

[31]  Dennis E. Buechler,et al.  The 1997–98 El Nino event and related wintertime lightning variations in the southeastern United States , 2000 .

[32]  Steven J. Goodman,et al.  Three Years of TRMM Precipitation Features. Part I: Radar, Radiometric, and Lightning Characteristics , 2005 .

[33]  David P. Yorty,et al.  WHERE ARE THE MOST INTENSE THUNDERSTORMS ON EARTH , 2006 .

[34]  W. Deierling,et al.  Estimation of total lightning from various storm parameters: A cloud-resolving model study , 2010 .

[35]  William S. Olson,et al.  The effect of spaceborne microwave and ground-based continuous lightning measurements on forecasts of the 1998 groundhog day storm , 2001 .

[36]  William M. Farrell,et al.  Lightning optical pulse statistics from storm overflights during the Altus Cumulus Electrification Study , 2005 .

[37]  Osmar Pinto,et al.  The intracloud/cloud-to-ground lightning ratio in Southeastern Brazil , 2009 .

[38]  H. J. Christian,et al.  Optical Observations of Lightning from a High-Altitude Airplane , 1987 .

[39]  Richard J. Blakeslee,et al.  The 13 years of TRMM Lightning Imaging Sensor: From individual flash characteristics to decadal tendencies , 2011 .

[40]  Nai-Yu Wang,et al.  Improving Geostationary Satellite Rainfall Estimates Using Lightning Observations: Underlying Lightning–Rainfall–Cloud Relationships , 2013 .

[41]  Richard J. Blakeslee,et al.  Performance Assessment of the Optical Transient Detector and Lightning Imaging Sensor. Part 2; Clustering Algorithm , 2007 .

[42]  W. J. Koshak,et al.  A Mixed Exponential Distribution Model for Retrieving Ground Flash Fraction from Satellite Lightning Imager Data , 2011 .

[43]  A. Fierro,et al.  Application of a Lightning Data Assimilation Technique in the WRF-ARW Model at Cloud-Resolving Scales for the Tornado Outbreak of 24 May 2011 , 2012 .

[44]  W. Paul Menzel,et al.  INTRODUCING THE NEXT-GENERATION ADVANCED BASELINE IMAGER ON GOES-R , 2005 .