Blakeslee, Richard

Goodman, Steven J.

Koshak, William

Mach, Douglas

NASA Technical Reports Server

Steven J. Goodman, Richard Blakeslee, 
Douglas Mach, William Koshak
GOES‐R AWG Lightning Detection Team
The Geostationary 
Lightning Mapper (GLM) 
for the GOES‐R Series of 
Geostationary Satellites
NOAA Science Week
NWS Training Center
Kansas City, MO
30 April-2 May, 2012
Geostationary Lightning 
Mapper (GLM)
https://ntrs.nasa.gov/search.jsp?R=20120015474 2019-08-30T22:43:48+00:00Z
2Lightning Team Members
• Co‐Chairs:
– Steven Goodman (NOAA/NESDIS)
– William Koshak (NASA/MSFC‐AWG, IV&V) 
– Richard Blakeslee (NASA/MSFC‐R3, Val)
• Application Team:
– Douglas Mach (UAH‐AWG) 
– Dennis Buechler (UAH‐AWG, IV&V)
– Larry Carey (UAH‐R3)
– Monte Bateman (USRA‐AWG, IV&V)
– Eugene “Bill” McCaul (USRA‐R3)
– Don MacGorman/Alex Fierro (NSSL‐R3, 
JCSDA FFO)
– Paul Krehbiel (NMTech‐AWG, Val)
– Eric Bruning (TTech‐AWG, PG)
– Rachel Albrecht (CICS/InPE‐R3)
– Scott Rudlosky (NESDIS/STAR‐R3, PG)
– Geoffrey Stano (SPoRT‐Training, PG)
• National Weather Service
• NESDIS Satellite Analysis Branch (SAB)
• Department of Defense (DoD)
– Air Force Weather Agency (AFWA)
– Fleet Numerical Meteorology and Oceanography 
Center (FNMOC)
– Joint Typhoon Warning Center (JTWC)
 NASA
– KSC‐ETR/Launch Commit Criteria
– SMD‐ science (O2R)
 Federal Aviation Administration (FAA)
 USDA‐ Forest Service
 Bureau of Land Management (BLM)
 Department of Energy
 EPA
 NSF‐Universities
 Private Industry
 EUMETSAT (GEO WG)
Stakeholders‐ End Users
Outline
• GLM Instrument
• AWG Lightning Cluster/Filter Algorithm
• GLM Validation
• GLM Applications
– Concept of Operations ‐ Lightning
• GLM Related Posters
• Summary
3
Geostationary Lightning Mapper (GLM)
Specifications
• Continuously maps all (in‐cloud and 
cloud‐to‐ground) lightning events
• Provides early indication of storm 
intensification and severe weather 
events; tornado warning lead time of 
20 minutes or more
• CCD event detector
– 777.4 nm wavelength
– 2 ms frame rate
– 7.7 Mbps downlink rate
• Near uniform spatial resolution
– 8 km (nadir) – 14 km (edge of FOV)
– 70‐90% flash detection
• Product availability < 20 sec
Current Status
• Flight fabrication is underway
• FM1 delivery summer 2013
Optical Assembly
Sensor Unit 
Mechanical 
Support Structure
Metering tube
Primary Contractor: Lockheed Martin Advanced 
Technology Corp of Palo Alto, CA
4
GLM Sensor Unit Overview
Baffle & Door
Solar Rejection 
Filter
Solar Blocking 
Filter
Metering Tube
Focal Plane Array
Assembly (FPAA)
Loop Heat Pipes (FPAA) 
and Thermal Straps (SEB)
Baffle Support
Lens Housing & 
Support Structure
Narrow Band
Filter
Sensor Unit 
Electronics Box (SEB)
5
Variable Pitch 
CCD
Lightning Cluster/Filter Algorithm (LCFA)
• Algorithm Has Not Been 
Modified
• Based on Analysis of 
Over a Decade of Data 
from LIS/OTD
• Only QA Components 
Have Changed (evolution 
of QA data based on L1B 
code development)
• Harris/AER Implemented 
LCFA/ATBD… (March 
2012)
6
GLM Code Speed Tests
• Tests Indicate Code Can Meet 
Latency Requirements
– Proxy data tests on single thread GLM code on 
target system indicate that the code can process 
20,000 to 25,000 events in less than 1 second of 
computer time
– Current estimates of maximum GLM data rates are 
around 40,000 events/sec (mean rates nearer 150 
events/sec)
– We can process a nominal second of GLM event 
data in less than 1 second of computer time and well 
less than the 4 second latency requirement
Max Event Rate Test
Vaisala NLDN used to scale max expected flash rate 7
AWG Lightning Detection 
Harris/AER Interactions
• Series of TIMs (Technical Interchange Meetings)
• Series of Document  Interchanges 
(Questions/Answers/Clarifications/Resolutions)
• Interactions Allowed Harris/AER to Fully Understand 
the LCFA and the Expected Inputs and Outputs
• Resulted in Contractor Code Meeting or Exceeding 
Requirements
– Specifications: 0.95
– Achieved 0.99 (or better)
8
GLM Validation
9
GLM: Lightning Optical Pulse Detector
• GLM Detects Optical Pulses From CG 
and IC Flashes Over Nearly the Full 
GOES‐R FOV
• NLDN Detects VLF From Mostly CG 
Flashes Over Mostly the CONUS & 
Canada (up to 90% of lightning is IC)
• LMA Detects VHF From CG and IC 
Flashes Over a Limited Range (at 
location of network)
10
Proxy Data/Validation Posters:
Integrated GOES‐R GLM/ABI approaches for the detection and 
forecasting of convectively induced turbulence (CIT) ‐ Carey, Feltz, 
Bedka, Monette, Wang, Rogers, Scott
GOES‐R AWG GLM Val Tool Development ‐ Bateman, Mach, Goodman, 
Blakeslee, Koshak
Intercomparison of Lightning Location Systems during CHUVA‐
GLM field campaign and thunderstorm characteristics ‐ Albrecht, 
Morales, Goodman, Blakeslee, Bailey, Carey, Mach, Hall, Bateman, Rudlosky, 
Holler, Betz, Mattos, Nag, Said, Lojou, Heckman, Pinto Jr., Naccarato, Saraiva, 
Saba, Holzworth, Anderson, Collins
Validation Data
• Ground Truth Datasets: 
– Short-Medium Range Lightning
• LMA (North Alabama (NASA-NOAA), DC (NASA-NOAA), Oklahoma (OU 
CIMMS-NSSL), West Texas (TTU), NMTECH, Camp Blanding (UF-
DARPA), Colorado Front Range (CSU), Houston (TAMU), NASA-KSC and 
Wallops, Atlanta (GTRI), St. Louis (SLU-Cardinals baseball)
• HAMMA/Delta E Array (North Alabama) 
• High Speed Video Cameras
• KSC Field Mills (KSC Florida)
• NLDN  (CONUS)
– Long Range Lightning
• GLD360
• WWLLN
• ENTLN
LIS optical
outline
LMA
VHF
Building the GLM Proxy
11
Validation Data (cont.)
• Additional Ground-Based Systems for Field Campaigns
– CHUVA SOS- Sao Jose dos Campos (Nov. 2011-March 2012)
• SPLMA (Sao Paulo Brazil), ENTLN, RINDAT (CG)
– DC3 (May-June, 2012)
• FCLMA (Fort Collins Colorado), OKLMA, NALMA
– HyMeX  (SOP-1 Sept-Oct 2012)
• Deployed LMA in March 2012 (Mediterranean region)
• Bill Rison (VSP, NMTech) to participate in HyMeX SOP-1 (Sep-Oct 2012)
• GOES-R MOU with HyMeX allows full access to their data and NWP
1
GLM CHUVA Campaign‐ Nov 2011‐ Mar 2012
2
TRMM Overpass
LIS and VIRS
• Excellent Cross‐Network Inter‐
Comparisons and Performance 
Assessments
• Improved Understanding of 
– Convective Initiation
– Thunderstorm Physics
– Nowcasting Applications
• Generation of GLM Proxy Data
• Concurrent SEVIRI and LIS Data
• Better Understanding of 
Lightning/Storm Interactions
See: Intercomparison of Lightning Location Systems during 
CHUVA‐GLM field campaign and thunderstorm 
characteristics ‐ Albrecht, Morales, Goodman, Blakeslee, Bailey, 
Carey, Mach, Hall, Bateman, Rudlosky, Holler, Betz, Mattos, Nag, 
Said, Lojou, Heckman, Pinto Jr., Naccarato, Saraiva, Saba, Holzworth, 
Anderson, Collins
TRMM Overpass
LIS and VIRS
Validation Tools
• Best Proxy GLM is LIS
• LIS Data Not Always Available 
• Tool Uses LMA as “Seed“
• Output a Statistical Match for LIS/GLM Data
• Tools Developed to Assess LCFA software
• By Comparing LFCA Output with “Seed" Data, Can Test Clustering
• Results Indicate LFCA is Performing Exactly as Should
• Tools Developed to Help Visualize the Many Data Streams Needed to Assess 
Performance of GLM (cal/val)
3
See: GOES‐R AWG GLM Val Tool Development ‐ Bateman, Mach, Goodman, Blakeslee, Koshak
LIS
LMA
4LIS
LMA
SPLMA, WWLN TRMM/LIS Overpass
February 10, 2012 GLM CHUVA Campaign
Courtesy, Monte Bateman
5SPLMA, WWLN TRMM/LIS Overpass
February 10, 2012 GLM CHUVA Campaign
Courtesy, Rachel Albrecht
Validation Data (cont.)
• Airborne GLM Simulator
– Build an airborne detection system that will make high resolution 
optical measurements as a GLM simulator.
– Deploy on aircraft (e.g., ER2, Global Hawk) to observe cloud-top 
lightning pulses (target DC3, HS3, other field campaigns).
• Satellite Observations
– LIS 
• GLM proxy data development
• Pre-launch validation simulations (including val tool  testing)
• Pursue opportunity to a LIS on International Space Station
• TRMM Extended Mission ? (next slide)
– TARANIS (Tool for the Analysis of RAdiation from lightNIng and Sprites)
• Launch 2015, CNES/France; nadir staring (2 cameras, 4 photometers) 
• Directly compare with GLM data
– Cross-Calibration Between GLM and MTG LI  (2017) (at 777.4 nm)
Credit: CNES/Ill. D. Ducros
1
•12/201
3
•02/16
•End of 
Fuel = 
12/2013 •En
d of 
Ops
•02/15
•0 km
•
L
a
u
n
c
h
•Prime 
Mission (350km)
•Extended Mission 
(402.5 km)
•11/1997 •01/1998 •08/2001
•07/2011
TRMM Notional Timeline
•Passivation
•402.5 km
•350 
km
•04/2015 •03/201
8
•03/2019
•End of Fuel = 
4/2015
•End of 
Ops
•335 km
Note: Timeline not 
drawn to scale
GLM Applications
• GLM – Lightning mapping, alerts
• GLM + ABI – Severe and high impact weather
• GLM + ABI + GPM‐ Precipitation
• GLM + NWP – Data Assimilation
• GLM Fused
– CI, Severe Storms, WoF, Tropical Cyclones, Multi‐
sensor Precipitation, Aviation Weather, Fire 
Weather, NOx, Volcanic Eruptions, Extreme 
Weather and Climate Variability
– with Radar, Ground‐Based Lightning (CG, IC), NWP
3
Concept of Operations and Operational 
Requirements:
*Lightning Data Program (05‐033)
• Current State of Lightning Operations
– NLDN for CONUS and surrounding areas
• 1‐minute product
• CG data
• Some IC data
– GLD360 for worldwide lightning (known uneven coverage)
• Not displayed in AWIPS
• Hurricane data (Pacific & Atlantic)
• Proposed Concept of Lightning Operations
– NLDN for CONUS and surrounding areas (same as Current)
– GLM total lightning
• Total lightning over GLM FOV
– Use AWIPS‐II
• Plot total lightning data
• Monitor total lightning trends
• Use data to obtain more detailed information (database query)
• Archive lightning data
4*Source:NWS/Sergio Marsh
NWS Concept of Operations and 
Operational Requirements
Lightning Data Program (05‐033)
• GLM Will Add Major Total Lightning Data 
Source to the Operational Pallet of Resources
5
6
From Wakimoto and Bringi, 1988; Photos, K. Knupp
Added GLM Value for 
Severe Storm Opeations and Decision Aids
• Small Air Mass Thunderstorm
– Huntsville “Monrovia” Microburst, 
20 July 1986
– Pulse air mass storm, 65 dBZ max Z
– Pea size hail, 40 kt outflow
– 110 total lightning , 6 CG strikes
1830
1914
1900
1930
2000 2030
Cloud top temperatures continue 
cooling after reaching the mature 
stage as cirrus anvil fills imager fov
1907 1911 19161913
Lightning Connection to Storm 
Updraft, Storm Growth and Decay
• Total Lightning —responds to updraft velocity 
and concentration, phase, type of 
hydrometeors, integrated flux of particles 
• Dual-Pol WX Radar — responds to 
concentration, size, phase, and type of 
hydrometeors- integrated over small volumes
Air Mass Storm
20 July 1986
1Adapted from Goodman et al, GRL, 1988; Wakimoto and Bringi, MWR, 1988; Kingsmill and Wakimoto, MWR,1991, Zeng et al., 2001, Gatlin and Goodman, JTECH, 2010
Glaciated, Zdr<0
1912 19181906
Hail Shaft
Growth, Zdr>0
w, dBZ, HID
Raindrops, T<0C
OK Tornado Outbreak 3 May 1999
NEXRAD Reflectivity NEXRAD Velocity
Active lightning region in tornadic supercell … correlates with radar hook echo and velocity couplet 
•Hook •Doppler  
shear 
couplet
2
3GLM and ABI Combined (with radar) characterizes storm intensification and decay)
Total Lightning Dominates During OK Tornado: 3 May 1999
4National Average for Tornado warning lead‐time is only 14 minutes
Lightning Detection
Operational demonstration underway beginning in April 2012 of the total lightning 
algorithm at the Hazardous Weather Testbed (at request of NWS)
See: Lightning Jump Algorithm and Relation to Thunderstorm Cell Tracking, GLM Proxy and 
other Meteorological Measurements ‐ Schultz, Carey, Cecil, Bateman
Total Lightning Demonstrations
at the GOES‐R Proving Ground
• Pseudo‐GLM
– Data from ground‐based total lightning 
detection networks
• Huntsville, AL; Washington, DC; 
Melbourne, FL; and Norman, OK
– Raw data sorted into flashes and 
interpolated to an 8km grid
– Running 2‐minute average
• Simulated lightning threat
– Implemented in NSSL‐WRF, OU/CAPS 
ensemble, and High Resolution Rapid 
Refresh (HRRR)
– Estimates total lightning from vertical 
ice content and flux within cloud 
objects (see McCaul et al., 2009)
See: The WRF Lightning Forecast Algorithm for GLM: Refinement and 
Incorporation into Convection‐allowing Ensemble Forecasts ‐ Bill McCaul
A lightning data assimilation scheme for the WRF‐ARW model at cloud‐
resolving scales: Case studies ‐ Alex Fierro
• “The total lightning data is an excellent tool for monitoring convection, I see much 
promise for such data in the future…”
• “I utilized it as a situational awareness product …the PGLM data gave me more 
confidence in my warning.”
• “Total lightning data preceded the CG network (NLDN) anywhere from 10-40 
minutes. I was able to quickly determine when flash rate was significantly 
increasing, and then compare with satellite and updraft/downdraft parameters for a 
nice big picture.”
• “Coming into the day, I wasn't quite sure when or where to or why to use the data, 
but after using it.  I really think it has a lot of functionality and is useful in warning 
operations.  I look forward to it as a product from the GOES-R.”
“We saw several instances where the total lightning was 
picking up on storms before the AWIPS lightning 
[NLDN] program picked up on them. One could see the 
utility of this in the future, bringing with it a potential for 
lightning statements and potentially lightning based 
warnings.” 
-Pat Spoden (SOO, NWSFO Paducah, KY)
Proving Ground Forecaster Feedback:
Lightning Detection
6
See: Lightning at HWT (Spring Experiment) –
Kristin Kuhlman, Chris Siewert
7SPLMA, WWLLN Flash During TRMM 
Overpass: CHUVA 10 Feb 2012
8Currently Available Training
• VISIT CONUS Lightning
• VISIT Lightning Meteorology I
• VISIT Lightning Meteorology II
• COMET Intro to Tropical Met
• COMET Fire Weather Climatology
• COMET GOES-R Benefits
• VISIT GOES-R 101
• SPoRT Lightning Mapping Array
• SPoRT PGLM Training
• VISIT Use of GOES/RSO Imagery w/Remote
Sensor Data for Diagnosing Severe Wx
Source, NWS/Brian Motta
GLM Posters
• Algorithm Performance
– The Ground Flash Fraction Retrieval Algorithm Employing Differential Evolution: 
Simulations and Applications ‐ Koshak, Solakiewicz
– Lightning Jump Algorithm and Relation to Thunderstorm Cell Tracking, GLM Proxy and 
other Meteorological Measurements ‐ Schultz, Carey, Cecil, Bateman
– GOES‐R AWG GLM Val Tool Development ‐ Bateman, Mach, Goodman, Blakeslee, 
Koshak
– The WRF Lightning Forecast Algorithm for GLM: Refinement and Incorporation into 
Convection‐allowing Ensemble Forecasts ‐ McCaul, Case, Goodman, Dembek, Kong
• Intercomparisions
– Intercomparison of Lightning Location Systems during CHUVA‐GLM field campaign and 
thunderstorm characteristics ‐ Albrecht, Morales, Goodman, Blakeslee, Bailey, Carey, 
Mach, Hall, Bateman, Rudlosky, Holler, Betz, Mattos, Nag, Said, Lojou, Heckman, Pinto 
Jr., Naccarato, Saraiva, Saba, Holzworth, Anderson, Collins
9
GLM Posters
10
• Assimilations
– A cloud‐scale lightning data assimilation technique and the explicitly forecast of 
lightning with full charging/discharge physics implemented within the WRF‐ARW 
model ‐ Fierro, Mansell, Allen, Ziegler, MacGorman
– Evaluating storm intensity using coupled TRMM Lightning Imaging Sensor and 
Meteosat Second Generation in preparation for GOES‐R ‐ Jewett, Leroy, Mecikalski, 
Walker
– Utility of GOES‐R GLM observations using hybrid variational‐ensemble data 
assimilation in regional applications – Zupanski
– Assimilation of lightning data in the rapid Refresh model and evaluation of lightning 
diagnostics from nested HRRR runs ‐ Hu, Alexander, Weygandt, Benjamin, Lin
– Integrated GOES‐R GLM/ABI approaches for the detection and forecasting of 
convectively induced turbulence (CIT) ‐ Carey, Feltz, Bedka, Monette, Wang, Rogers, 
Scott
– Combining GLM and ABI for Enhanced GOES‐R Rainfall Estimates ‐ Adler, Xu, Wang
Summary
• GLM Instrument Development on Track‐ FM1 2013
• Code Development on Track & Within 
Specifications
• Proxy data and Cal/Val tools in development for 
monitoring GLM performance
• GLM Data will enhance future NWS Products
• Training content and module development 
coordination with NWS, COMET, VISIT
• For more details, please visit  GLM posters
11


The Geostationary Lightning Mapper (GLM) for the GOES-R Series of Geostationary Satellites

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