1. The Need for and Feasibility of a Global Lightning Detection Network
Frederick R. Mosher
NWS/NCEP
Aviation Weather Center

2. Potential Users of Global Convection Information
Aviation (safety and routing)
Initialization of Numerical Models (storms in right places; redistribution of energy and mass)
Precipitation Monitoring (hydrology, agriculture)
General Circulation and Climate Studies (redistribution of mass and energy)

3. SIGMETs
A SIGMET (SIGnificant METeorological Information) advises of weather potentially hazardous to all aircraft. Hazards covered are:
Significant convection
Severe icing
Severe or extreme turbulence
Duststorms and sandstorms lowering visibilities to less than three statute miles
Volcanic Ash

4. US Areas of International SIGMET Responsibilities

5. Convective Hazard Detection
Geostationary satellite infrared images traditionally used to monitor convection over the oceans.
Experimental Vaisala Long Range Lightning Detection Network used to supplement satellite images within km of US/Canada.

6. Methods for Geostationary Monitoring of Convection
Infrared Temperature of Clouds
Multi-channel feature identification
Channel differences (GCD)

7. GCD Identification of Updrafts
For deep convective clouds, updraft brings cloud particles and water vapor to top of cloud. (IR and Water Vapor temperature are the same)
Clouds drifting away from lift will allow cloud particles to fall. Water Vapor will remain at original level. (IR and WV temperatures will be different)

8. GCD Thunderstorm Detection
Tir=Twv
Tir>Twv

9. Algorithm Processing
IR-Water Vapor temperatures differences in original satellite projections.
Temperature differences mapped into 10 km global latlon equidistance projection with parallax correction for 10 km high cloud.
Data limited to within 65 degrees of satellite subpoint.
Most timely data on top for data overlaps.

10. Algorithm Processing (cont)
Temperature difference of 1 degree or less are considered convective.
Lifted index (from AVN) of +1 or less used to eliminate any detection from non convective clouds (such as cirrus from ageostrophic lifting).

11. GCD vs IR <-55C

12. GCD vs Lightning

13. GCD (red) World View

14. GCD Verification
National Convective Weather Diagnostic (NCWD) used as ground truth
NCWD generated from radar and lightning data over the US (see for real time NCWD)
Verification done using FSL Real Time Verification System (RTVS).

15. GCD vs. NCWD 9/7/01 22:15Z

16. Verification Summary GCD PODy=.44 GCD PODn=.99 GCD Bias=2.78
For comparison:
Conv. SIGMET PODy=.44 (0 hr)
Conv. SIGMET PODn=.99
Conv. SIGMET BIAS=1.39

17. FAA Sponsored Ocean Weather Product Development Team (PDT)
Focus on techniques for detecting and forecasting convective hazards to aircraft over the oceans.
Several different satellite techniques have been developed and intercomparison efforts are attempting to determine the strengths and weaknesses of each method.

18. Utilization of NASA TRMM Satellite Information to Evaluate OW Algorithm Products
Lightning Imaging Sensor (LIS) not shown

19. Observations of TRMM PR data
All Ocean Events
Event Type
# of Events
Mean max dBZ at 5 km
Mean max dBZ at 7km
thundercloud 2 43 38
cumulonimbus 7 34 28
All Land Events 5 42 33 3 35 31
thundercloud profile
cumulonimbus cloud profile

20. 10 June 2002 OW Product Performance
Satellite Products
Identification Performance
thunderclouds
cumulonimbus clouds
Cloud Classification (NRL)
16/16 (1.00)
15/16 (0.94)
Global Convective Diagnostic (GCD)
14/16 (0.88)
Cloud Top Height >= 40 kft (NCAR)
6/16 (0.38)
9/16 (0.56)
Cloud Top Height >= 35 kft
11/16 (0.69)
13/16 (0.81)
Cloud Top Height >= 30 kft

21. Summary of 1st Inter-comparison
Distinctions made between thunderstorms and cumulonimbus clouds using TRMM LIS data
Satellite products perform well at identifying both types of clouds but unable to make distinctions between the two

22. 2nd Inter-comparison: 26-31 March 2003
Pacific sector
20N – 0N, 150W – 120W
(area: 6.94x106 km2)
21:00 – 03:00 UTC
South American sector
10N – 20S, 80W – 50W
(area: 10.91x106 km2)
18:00 – 00:00 UTC
Sectors selected based on high likelihood of air mass thunderstorms over land and deep convection in ITCZ
Analysis performed during TRMM intersects – Pacific (14) South America (8)

23. Overall Assessment of Inter-comparisons
In terms of satellite product performance:
All perform well at identifying continental convection but exaggerate presence of maritime convection
All perform well at identifying the clouds posing the greatest hazard to aviation, i.e. thunderstorms
NRL cloud class more responsive at detecting convective cells in early developmental stage
False alarm/hit ratios are dramatically higher over ocean vs. land
Reflectivity profiles of oceanic events indicate that most are weakly developed in the mixed phase region

24. Implications of Intercomparisons
Satellites image techniques can not distinguish between hazardous thunderstorms and non hazardous convective clouds.
Lightning detection data is needed to identify thunderstorm hazards over the oceans.

25. Thunderstorms Require Charge Separation
Thunderstorm charge separation typically requires existence of graupel.
Graupel formation requires an updraft speed of 7-10 m/sec.
Thunderstorm Project observed aircraft severe turbulence for updrafts > 10m/sec
Existence of lightning can be used distinguish convective updrafts >7-10m/sec.

26. Vaisala Maritime Data

27. Long Range Lightning Detection
Vaisala VLF/LF sensors (IMPACT and LPATS) are very responsive to ionospherically-propagated electromagnetic signals, in the VLF frequency range. Thus, they are able to detect remote lightning events that occur at great distances.
During the daytime, the long-range lightning data is generally available out to NM ( km) from the sensors with a DE of 10-20%.
At night the range increases to NM ( km) and the DE to 10-30%.
VLF and LF pulses can travel a substantial distance while still retaining sufficient signal form to facilitate detection.
These signals will bounce off the ionosphere as they travel outward from their source. This the key to long range detection.
Ionosphere is strongest at night for about a 6-hour period.
In essence, the method used to facilitate such long range detection and location is to reverse engineer the signal path to its point of origin.
Enough is known about the frequency components and character of lightning for this approach to be a viable and proven method.
When using sensors with this technological approach, polarity or the amount of current [Kamps] within an event cannot be defined.

28. PacNet Configuration
Employs Five (5) IMPACT ESP Long Range VLF Sensors:
Hawaiian Islands [Kauai and Midway Islands]
Dutch Harbor, Alaska
Kwajalein Atoll
Other Pacific islands
Also Employs Long-Range Data Detected by the:
National Lightning Detection Network
Canadian Lightning Detection Network
Data Processed at Vaisala-GAI Network Control Center-Tucson
Will begin operations early 2004.
Primary communications is TCP/IP based.
Raw data managed by Vaisala-GAI.
Processed data redistributed by Vaisala-GAI to participants.
Vaisala-GAI has rights to commercialization of data and products.

29. Median Detection Efficiency - NALDN + PacNet
Projection Based on Long-range Configuration

30. Median Location Accuracy NALDN + PacNet
Reason for distortion is due to the decision process involved.
Specifically, which sensors [NALDN/PacNet], or combination thereof, would be used to produce the solution.
Projection Based on Long-range Configuration

31. MINIMUM CARIBNET DE W/7 VLF SENSORS

32. Potential Network- NORTH ATLANTIC

33. Global Coverage
VLF sensors would be needed for global coverage.
Projected total cost of CARIBNET was $400K/yr for 7 sensor network.
Extrapolated cost of global network would be $5M per year.
Comparable in extend and cost to NLDN for US coverage.

34. Complimentary data source: NASA Lightning Mapper May Fly on upcoming GOES-R (2012)

35. Conclusions
Advanced satellite thunderstorm detection algorithms have not been able to demonstrate the ability to distinguish thunderstorms from weaker convection over the oceans.
Lightning data is needed over the oceans to identify thunderstorms hazardous to aviation.
Technology exists that could be utilized for global lightning detection if funding sources could be identified ($5M/yr) .