supporting paper - Global Severe Weather

Transcription

2015 International Conference on Lightning and Static Electricity (Toulouse, France)
LIGHTNING DETECTION NETWORKS FOR
COMMERCIAL AVIATION – TECHNOLOGY,
METEOROLOGY AND APPLICATIONS
Charlie Liu1, James Anderson2 and Stan Heckman3
1.Vice President, International, Earth Networks, [email protected]
2 Dr. Chief Computer Scientist, Earth Networks, [email protected]
3. Dr. Stan Heckman, Chief Lightning Scientist, Earth Networks, [email protected]
Keywords:
Lightning, In-Cloud,
Severe Weather, Total Lightning
1
Time-of-Arrival,
2
Abstract
The ability to detect and accurately pinpoint areas of existing
and/or early-stage convection is critical to improving
situational awareness and safety in the aviation industry.
Given thunderstorms by definition include lightning and the
vast majority of lightning is typically in-cloud (IC) as
opposed to cloud-to ground (CG), it is necessary to have a
network in place that detects total lightning activity (i.e., both
IC and CG). Total lightning has been demonstrated to
correlate well with storm dynamics and both case studies and
statistical analyses suggest that total lightning is related to the
presence or high likelihood of flight hazards such as hail,
icing and turbulence. Additionally, lightning itself also poses
a direct safety hazard to airborne aircraft as lightning strikes
can cause engine failure, disrupt and damage aircraft
electrical systems, lead to smoke and on-board fires, and
temporarily affect flight crew vision. Total lightning
information is also an important complement to existing
weather radars as it can be highly useful in identifying areas
of convection beyond radar ranges, locations where radar
beams are blocked (i.e., mountainous areas), as well as early
stage convective regions where precipitation returns do not
yet appear significant.
Given that CG activity typically represents a small fraction of
the lightning occurring in the atmosphere, CG detection
networks are not sufficient to meet the mission requirements
of organizations tasked with providing critical information to
aviation interests.
The emergence of total lightning
availability offers significant opportunities to enhance
aviation flight safety. To this end the National Transportation
Safety Board (NTSB) published a safety recommendation
stating that the Federal Aviation Administration should
consider several enhancements to its operations including;
incorporation of total lightning data into weather displays at
both air route traffic control centers and terminal radar
approach control facilities, as well as into products supplied
to pilots in the cockpit [17]. This paper reviews available
total lightning network technology and potential applications
for enhancing aviation safety. Case studies are reviewed and
derivative products, such as proxy radar estimates, VIL and
eco-tops are described.
Introduction – Meteorology
The lifecycle of a thunderstorm convection cell can be
described by the classical tripole model, in which the main
negative charge is located in the center of the cell, the main
positive charge is in the cloud-top ice crystals, and a smaller
positive charge is in the lower section of the cell, below the
negative charge [9]. The initial electrification of the central
and top parts may give rise to cloud flashes with intense
enough charging producing ground lightning [9].
Severe thunderstorms, which may generate lightning, high
wind, hail and tornadoes have certain characteristics in the
lightning flashes, such as high IC flash rates in the storm
formation stage. Severe storms may have either exceptionally
low negative CG flash rates, or have exceptionally high
positive CG flash rates; the greater volume of strong updrafts
during a severe thunderstorm results in more charging overall,
leading to greater numbers of IC flashes and positive CG
flashes [5,10]. Past studies have shown that the CG flash rate
has no correlation with tornadogenesis and that using CG
lightning flash patterns exclusively to detect tornado
formation is not practical [7]. This general finding seems to
carry over to all damaging thunderstorms. There is no system
known that can reliably predict intense thunderstorms using
CG flash data alone.
A study focused on severe thunderstorms in Florida using the
lightning detection and ranging network (LDAR) total
lightning data confirmed a distinguishing feature of severe
storms, i.e., the systematic total lightning and abrupt increase
in total lightning rate precursor to severe weather of all kinds
– wind, hail and tornados [11]. Previous studies on the
Lightning Jump Concept using the Lightning Jump Algorithm
have demonstrated the correlation between severe weather
occurances and the sharp increases in lightning ‘jump’ prior
to and during the weather incidents. Rapid increase in total
lightning activity (both CG and IC) were observed in advance
of severe weather[13, 16]. The results of the study linked the
timing of ‘Lightning Jumps’ to corresponding severe weather
occurances like hail and damaging wind. See Figure 1.
A
pure CG lightning detection system, due to the lack of IC
detection capability, is not adequate for predicting severe
storm development. The convection-cell structure of a
thunderstorm is often visible in a weather radar image, and it
can also be identified in lightning flash clusters when the rates
1
2015 Earth Networks,Inc.
are high enough. But the lightning cells based on CG flashes
can only show the mature stage of a convection cell [8], and
they can’t be used for early severe storm warning.
The Huntsville, Alabama, National Weather Service office
utilizes total lightning information from the North Alabama
Lightning Mapping Array (NALMA) to diagnose convective
trends; this lightning data has led to greater confidence and
lead time in issuing severe thunderstorm and tornado
warnings [3]. In one study, the IC lightning precursor
provided a valuable short-term warning for microburst hazard
at ground level [9]. The lightning cells identified from the
total lightning data would be able to track the whole lifecycle
of a storm. A study based on data from the Lightning
Detection Network in Europe (LINET) achieved an important
step in tracking lightning cells using total lightning data [2].
Figure 1: The graph above shows the results from a
lightning jump concept study demonstrating the Lightning
Jump Algorithhm (LJA) used in the Geostionary Lightning
Mapper (GLM) network.
The top graph details two
instances of lightning jumps recorded during a severe
thunderstorm on May 22, 1997 that reported 1” hail. The
bottom image is a wind differential velocity graph showing
the correlation between the lightning jumps and
corresponding severe weather conditions.
It has also been noted in these and other studies that the
analysis of total lightning can enable more precise location of
the greatest convective activity within a storm, since IC flash
rates increase the most dramatically in the areas of greatest
convection [15].
3
Lightning Detection and Severe Weather
Monitoring Technology
methodology. We focus briefly on surveying these national
scale networks, since they cover the greatest area and in some
cases reliably detect large proportions of the IC flash activity
and are thus more applicable for aviation. There are three
main providers of such systems globally – Earth Networks,
Nowcast and Viasala, Inc. All three detection network
providers have deployed extensive regional or global
networks. Reliable intercomparisons of the performance of
these networks can be difficult to find. Table 1 shows some
basic characteristics regarding each network relevant for
aviation culled from various websites and research reports.
Network
Provider
Sensor
Models
Earth Networks
Deployments
Globally with
dense regional
networks
Dense regional
networks
Method of
Detection
Time of Arrival
Time of Arrival
Frequency
Range of
Detection
IC-CG
Detection
VLF to HF
VLF/LF
Yes, waveform
based
Yes. Various
Altitude of IC
reported
Data
currently
being used
for aviation
applications
Severe
Weather
Nowcasting
Products
Yes
Yes, 3D
detection
based
Yes
Yes
Yes
Yes
Yes – validated
against NWS
alerts and
warnings
Yes
N/A
N/A
Nowcast
(LINET)
N/A
Viasala
Various –
TLS200,
LS7002
Global and
stand alone
regional
networks
Various –
TOA,
Magnetic
Direction
Finding and
Interferometry
VLF/LF and
VHF
N/A
Table 1: Lightning Detection Network Characteristics
There are various studies that provide some indication of
network performance. A full survey of them cannot be
provided here. Studies include those comparing LINET to the
LLDN in Poland [6] and various comparisons of networks to
satellite observations or very local networks [2]. The U.S.
National Weather Service has also done a large scale
comparison of the two network datasets it uses, the ENTLN
and NLDN. A search of the proceedings from the American
Meteorological Society yields a large number of comparisons
[18].
Currently, there are many different types of lightning
detection systems and networks in operation today. These
range from relatively simple and single point sensors which
operate on a variety of principles to sophisticated national or
global scale lightning detection networks, which generally
speaking operate primarily on a time-of-arrival based
2
latitude, longitude and altitude. Strokes are then clustered
into a flash if they are within 700 milliseconds and 10
kilometers. A flash that contains at least one return stroke is
classified as a CG flash. Other Time-of-Arrival based
systems use a similar method for locating lightning pulses
and grouping them into flashes.
Figure 2: The graph on the left shows the waveforms from
an IC pulse, across multiple sensors in ENTLN. The graph on
the right shows the waveforms from a return stroke (CG).
The Earth Networks Total Lightning Network (ENTLN)
utilizes a wide-band time of arrival based sensor.
The deployment of this sensor network and the improvement
in the detection efficiency, especially in IC flash detection on
a national or continental scale, made it practical to track and
predict severe weather in real-time over large areas.
By combining advanced lightning detection technologies with
modern electronics, an Earth Networks Lightning Sensor
(ENLS) can acquire detailed signals emitted from both IC and
CG flashes and continuously sends information to a central
data processing system. An ENLS is composed of an antenna,
a global positioning system (GPS) receiver, a GPS-based
timing circuit, digital signal processors (DSP), and on-board
storage and internet communication equipment.
The ENLS is unique compared to other existing sensor
technologies, with detection frequency ranging from 1HZ to
12MHZ. The lowest frequencies (below 1 KHz) are used for
long range detection (CG). The middle frequencies (1KHz to
1MHz) are used for locating return strokes. The highest
frequencies (1MHZ to 12 MHz) are used to detect and locate
in-cloud pulses. Overlapping frequency ranges are used at
two Analog to Digital Converters (ADC) to cover several
orders of maginitude in frequency. The sensor records whole
waveforms of each flash and sends them back, in compressed
data packets, to the central server. Instead of using only the
peak pulses, the whole waveforms are used in locating the
flashes and differentiating between IC and CG strokes. The
signal information enhances the detection efficiency and
location accuracy of the system. Sophisticated digital signal
processing technologies are used on the server side to ensure
high-quality detections, to eliminate false locations, and to
reduce noise typically associated with detecting
electromagnetic energy.
When lightning occurs, electromagnetic energy is emitted in
all directions. Every ENTLN sensor that detected the
waveforms records and sends the waveforms to the central
lightning detection server via the Internet. The precise arrival
times are calculated by correlating the waveforms from all
the sensors that detected the strokes of a flash. The waveform
arrival time and signal amplitude can be used to determine
the peak current of the stroke and its exact location including
A high-density network covers the contiguous United States,
Alaska, Hawaii, the Caribbean basin, Australia, parts of
Western Europe, Japan, Brazil and other geographies.
The weather station and lightning sensor at site locations are
plugged into a data logger, which sends independent weather
and lightning data via the Internet to central servers. This is a
unique feature of the ENTLN, where it allows for the
collocation of an automated weather station.
A global lightning network for long-range CG lightning
detection utilizing low frequency data is also deployed but is
not used in this study. An example of IC detection efficiency
is shown in Figure 3 below.
Figure 3: (a) An Earth Networks Lightning Sensor (ENLS) with
other weather instruments mounted on a typical mast; (b) The
Earth Networks Weather Station Network with more than 8,000
surface weather stations; and (c) ENTLN sensors in North
America.
Detection Efficiency and Classification Error
IC is the most common type of lightning representing 5 to 10
times as many flashes than CG. [12] Lightning detection
efficiency is a vital gauge of the total lightning performance.
When lightning occurs, electromagnetic energy is emitted in
all directions; many ENTLN sensors detect and record the
waveforms, and then send the waveforms to a central server
via the Internet. The precise arrival times are calculated by
correlating the waveforms from all the sensors that detected
the strokes of a flash. The waveform arrival time and signal
amplitude are used to determine the stroke type (IC or CG),
polarity, peak current, and exact location including latitude,
longitude, and altitude. Strokes are then clustered into a flash
if they are within 700 milliseconds and 10 kilometres. A
flash that contains at least one return stroke is classified as a
CG flash, otherwise it is classified as an IC flash. [12]
ENTLN has a dense senor network of over 900 total lightning
sensors. The density of the network allows lighting to be
detected by a mesh of sensor and increases the accuracy of
3
detection. ENTLN has a detection efficiency (up to 95%+) in
the US, Midwest and East where most storms occur. ENTLN
classifies IC flashes with > 95% accuracy. See Figure 4. It is
essential to detect a large proportion of IC flashes to
accurately identify and track severe storms.
Figure 4: IC Detection Efficiency over North America
Lightning Cell Tracking and Dangerous
Thunderstorm Alert
Lightning detection networks that reliably detect a large
proportion of IC flashes can be utilized to predict, detect and
track areas of severe weather that can adversely impact
aviation operation. Earth Networks has developed and tested
a variety of methods for doing this.
A lightning cell is a cluster of flashes with a boundary as a
polygon determined by the flash density value for a given
period. The color of the polygon represents a predefined
number lightning flashes per minute thresholds that are used
to help determine the severity of a storm cell. The polygon is
calculated every minute with a six-minute data window. The
cell tracks and directions can be determined by correlating the
cell polygons over a period of time. By counting the flashes
in the cell, it is possible to estimate the lightning flash rate
(flashes/min), cell speed and direction and total cell area. See
Figure 5.
Figure 5: Lightning Cell Track. Figure A is a red lightning cell
track polygon. Red indicates that >50 flashes per minute are
being detected. Figure B is the cell track detail derived from
algorithms that measure flash count. Flash Density polygon
Storm Cell and Track.
The flash data is streamed from a lightning manager service
to the cell tracker as soon as a flash is located. The cell
tracker keeps flashes in a moving time window of six
minutes. Two gridding processes are executed every minute,
using a snapshot of the flash data in that time window. The
first gridding is on a coarse grid to quickly locate areas of
interest and the second gridding is operated on a much finer
grid using density functions to find the closed contours. To
simplify the calculations, a convex polygon, which is the cell
polygon at the time, is generated from each of the closed
contours. In most cases, the cell polygon is similar to the
previous minute polygon, so the correlation between the two
polygons is straightforward. But in the case of sharp rise of
the flash rate, or cell split or merger, the correlation of
subsequent cells is not obvious. Special care is taken to link
the cell polygons and produce a reasonable path of the
moving cells. When a storm cell regroups after weakening,
based on the trajectory of the cell and the time-distance of
two polygons, a continuous cell path may be maintained.
Once a lightning cell is located and tracked, the total flash
rates, including IC and CG, are calculated. By monitoring
the flash rates and the rate changes, the severe storm cells or
4
the ones to potentially become severe, can be identified. An
alert polygon called a Dangerous Thunderstorm Alert (DTA)
is issued when lightning exceeds a 25 flashes /per minute
threshold for an identified cell. The alert indicates an
increased threat of heavy rain, lightning, hail, convective
winds and tornadic activity. Figure 6 shows an example DTA
being issued for a storm cell with a total flash rate of 108.3
flash/per minute.
can be issued at ti. The threshold of total lightning rate may
vary in different regions or different type of storms.
To simplify the study, a threshold of 25 flashes per minute
was chosen. Combining the information from the cells, such
as the moving speed and direction and size of the cell, a
warning area ahead of the storm cell can be determined. The
cell may reenergize and repeat the process again and trigger
more alerts. Some cells may disappear quickly and some may
keep going for hours. Some storms may contain mostly CG
flashes, although they are not usually severe in terms of high
wind, hail or tornadoes. CG strokes can cause serious
property damages and be threats to people.
The alert polygon covers the distance that a cell will travel in
45 minutes with the speed demonstrated at the moment when
the alert is generated. The alert polygon is updated every 15
minutes to reflect the updated path of the cell. Sufficient
surface weather station density is needed to provide wind
gust and rain rate data in real-time along the storm cell path.
R-time weather data provides additional information for the
dangerous thunderstorm alerts.
Figure 6: An alert polygon can be created for the area 45
minutes ahead of the moving cell.
Figure 7 shows the schematic cell history, the total lightning
rate has a sudden jump at t o and the severe weather follows at
ts after the rate peaks at t p. In a microburst, the pattern may
show up once, while in a super cell thunderstorm the pattern
can repeat many times during the lifetime of the cell.
One concern in previous studies [3] is the issue when
artificial trends in lightning data are strictly related to
efficiency or range issues. For such reason, flash data instead
of stroke data are used in the cell tracking; the latter may be
affected more by the detection efficiency. The thresholds can
be adjusted when the detection efficiency becomes known for
different regions. Further study will be conducted in this area.
Subsequently, the performance of DTA’s were validated
against National Weather Service (NWS) Warnings and
Local Store Reports (LSR’s). The study finding show that in
comparison to official alerts and ground truth from LSR’s the
DTA’s provide similar alert quality, measured with false
alarm rates and other metrics, as the NWS alerts, while also
providing greater warning lead time [18]. LINET also
highlights severe weather alerting capabilities, however no
validation studies were found.
4
Figure 7: Total lightning rate graph with to = jump time; tp =
peak total lightning rate time; ts = time of severe weather; ti =
issuing time of Dangerous Thunderstorm Alert (DTA); ts – ti
represents the lead time of the alert
When a cell is identified and the total lightning rate jumps
passing the threshold, a dangerous thunderstorm alert (DTA)
Case Studies and Applications in Aviation
Studies have shown a presence and increase in-cloud
lightning and ‘Lightning Jump’ prior to convective events.
[11, 13, 14] These confirm a consistent correlation between
severe weather events, total lightning detection and advance
warning to such severe weather.
The U.S. National
Transportation and Safety Board (NTSB) used ENTLN data
to analyse multiple cases of inflight convective events
(turbulence, thunderstorms, etc.) between 2010 to 2012. It
was determined during the study that each flight case had
substantial amounts of lightning detected by ENTLN prior to
each of the flight incidents. See Figure 8. After a thorough
review, the NTSB recommended in its May 2012 formal
Safety Recommendation to the FAA the utilization of incloud and cloud-to-ground (total) lighting detection in
weather displays at air route traffic- and terminal radar
5
control approach centers, as well as in technology used in the
cockpit [17].
Figure 8: Case study highlights from the NTSB May
2012 Safety Recommendation
Another case, which shows the value of in-cloud lightning
detection, was an event at the Baltimore/Washington
International Thurgood Marshall Airport, in Hanover,
Maryland, USA, on September 12, 2013. Early that
afternoon, a storm cell formed with heavy lightning, resulting
in a lightning strike to the control tower that injured an
employee. ENTLN detected the first in-cloud lightning 47
minutes prior to the reported time of the strike. The National
Aviation Weather Program (NAWP) released its 10-year
accident reduction report for 2010. It defines specific flight
hazard categories and weather factor that effect both general
aviation and commercial. The largest of the hazard category
is ‘turbulence and convection’. See Figure 9.
Figure 9: NAWP 10 year accident reduction report
hazard category and weather factors assessment
matrix
Government aviation agencies are also integrating total
lighting into their operations. Most notable is the United
States Air Force Weather Agency, which utilizes data from
the ENTLN as well as StreamerRTSM a weather visualization
tool, to monitor total lightning and track severe weather in
real time, supporting Air Force operations around the world.
Total lightning data and visualization tools are also used at
NASA Goddard Space Flight Center’s Wallops Flight Facility
to aid personnel in decision making and helping to ensure
range safety during rocket launches and aircraft operations.
Numerous airports and airlines also utilize these services or
similar ones provided by other commercial weather
companies and lightning detection network data providers.
6
On February 8, 2015, TAM Airlines flight JJ3307 took off
from Rio de Janiero airport. Shortly after take-off the flight
encountered hail associated with known thunderstorms in and
around the fight path. See Figure 10. The flight was forced
to make an emergency landing. The plane sustained
significant damage to its radome and cockpit windshields.
Flight Operations was unaware of the hail that caused the
damaged and triggered the emergency landing.
ENTLN data and lightning detection data sets from other
providers are also available as a data feed for easy integration
into aviation support applications. For example, Earth
Networks provides MeteoStar with global lightning
information for integration into its Flight Explorer system
(Figure 12) product, an environmental analysis and display
system. The integration of total lightning data enables
MeteoStar to provide its clients enhanced visibility into
dangerous lightning and severe storm events for improved
situational awareness. The bottom line:
Aviation
professionals must consider integrating total lightning
information into aviation operations for improved safety
inflight and on the ground.
Managing Convective Events
Figure 10: TAM flight JJ3307 flight into thunderstorms.
February 8, 2015.
In commercial aviation, Orlando International Airport in
central Florida (USA) is the travel hub for many of the largest
vacation destinations in the US. The climate in Florida is
particularly conducive to convective events. Airport officials
and airline staff implemented a complete early warning
system consisting of an automated weather station equipped
with a lightning detection sensor, visualization and an indoor
alerting tool.
The system is available to provide personnel over 20 airlines
with insight into weather and lightning information for
informed decision making and improved on-time
performance. Numerous other airports in multiple countries
have implemented the same technology, including Miami
International Airport, Madrid International Airport Barajas,
and Dirección Nacional de Aeronáutica Civil in Paraguay.
Case study evidence warrants the use of total lightning
information, particularly in-cloud lightning, as an effective
tool for improved visibility and increased lead-time in the
prediction of convective events as recommended by the
NTSB to the FAA. Utilizing real-time total lightning and
precise storm cell location and tracking information has
proven to improve safety in the air and on the ground, as well
as enable efficient scheduling and operational continuity
during severe weather events.
Figure 12: ENTLN data integrated into MeteoStar’s Flight
Explorer system provides enhanced visibility for routing of
aircraft out of harm’s way
5
Figure 11: StreamerRT image showing total lightning,
cell tracks and DTAs (purple polygons) over Florida's
Gulf coast
Conclusions
Reductions in weather related aviation incident have
marginally improved overall aviation safety over the last
couple of decades. Improved weather forecasting and
observational data has aided this effort. The use of total
lightning information, particularly in-cloud lightning, can
further improve visibility and increased lead time in the
prediction of severe weather events as recommended by the
NTSB to the FAA. The Earth Networks Total Lightning
Network (ENTLN) enables total lightning detection used for
real-time severe weather nowcasting and alerting applicable
to all aspects of aviation operations. LINET and Viasala also
7
provide lightning detection network data that detects IC
lightning to varying degrees.
This study provided further facts about the relationship
between the total lightning rate and severe weather; CG flash
rate in a storm does not have a clear correlation with the
severe weather activities, but IC flash rate and the rate jumps
can provide early indicators of severe thunderstorms capable
of producing hail, high wind or tornadoes. Most severe
convective storms can generate high IC flash rate and high
IC/CG flash-rate ratios. By tracking the lightning cells in a
storm and monitoring the total lightning flash rates, it is
possible to issue Dangerous Thunderstorm Alerts by Earth
Networks (DTAs) with a lead time of up to 30 minutes before
ground-level severe weather develops. ENTLN is a total
lightning detection network that can detect both CG and IC
flashes efficiently, and can be used to provide advance
warning of severe weather. The cell tracking and Dangerous
Thunderstorm Alerts (DTAs) can be used as an automated
severe storm prediction tool, which can be used to augment
radar, computer model data and observations to issue reliable
severe weather warnings. Further study is needed to
determine the appropriate Dangerous Thunderstorm Alert
(DTA) threshold for various storm characteristics and relative
geographic differences in ENTLN detection efficiency.
Further study is also needed to correlate the Dangerous
Thunderstorm Alert by Earth Networks (DTA) to NWS
severe thunderstorm warning to evaluate lead time and
accuracy.
6
Acknowledgements
The authors would like to thank members of the Earth
Networks management team, Robert S. Marshall and
Christopher D. Sloop, for initiating the lightning cell tracking
project and providing key inputs. We would also like to thank
additional members of the Earth Networks team, Benjamin
Beroukhim, Mark Hoekzema, Steve Prinzivalli and James
West for using the cell tracking system and providing
valuable feedback.
8
7
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
References
Betz, H. D., and Schmidt, K., Oettinger, W. P., and
Montag, B., 2008: Cell-tracking with lightning data
from LINET. Adv. Geosci., 17: 55–61.
Betz, H. D., and Schmidt, K., Oettinger, W. P., and
Wuerl, A.: Ground Truth from Lightning Detection
Network LINET in Europe for the Interpretation of
Thunderstorm-Related Data Collection by Space Born
Sensors. EUMETSAT, 2013.
file:///C:/Users/JAnderson/Downloads/PDF_CONF_
P_S5_30_BETZ_V.pdf.
Darden, Christopher B., and Nadler, David J., Carcione,
Brian C., Blakeslee, Richard J., Stano, Geoffrey T., and
Buechler, Dennis E., 2010: Utilizing total lightning
information to diagnose convective trends. Bulletin of
the American Meteorological Society, 91: 167–175.
Lang, T. J., and Rutledge, S. A., 2002: Relationships
between convective storm kinematics, precipitation, and
lightning. Mon. Wea. Rev., 130: 2492–2506.
Lang, T. J., and Rutledge, S.A., Dye, J. E., Venticinque,
M., Laroche, P., and Defer, E., 2000: Anomalously low
negative cloud-to-ground lightning flash rates in intense
convective storms observed during STERAO-A. Mon.
Wea. Rev., 128: 160–173.
Loboda, M., Betz, H. D., Baranski, P., Wiszniowski,
J., and Dziewit, Z. New Lightning Detection
Networks in Poland – LINET and LLDN. The Open
Atmospheric Science Journal, 2009, 3, 29-38.
Perez, A. H., and Wicker, Louis J., and Orville, Richard
E., 1997: Characteristics of Cloud-to-Ground lightning
associated with violent tornadoes. Weather and
Forecasting, 12: 428–437.
Tuomi, T. J., and Larjavaara, M., 2005: Identification
and analysis of lightning in thunderstorms. Meteorol.
Soc., 131: 1191-1214
Williams, E. R., 1989: The tripole structure of
thunderstorms, J. Geophys. Res., 94(D11), 13: 151–
13,167.
Williams, E. R., and Weber, M.E., and Orville, R.E.,
1989: The relationship between lightning type and
convective state of thunderclouds. J. Geophys. Res.,
94(D11), 13213–13220.
Williams, E.R., and Boldi, B., Matlin, A., Weber M.,
Hodanish, S., Sharp, D., Goodman, S., Raghavan, R., and
Buechler, D., 1999: The behavior of total lightning
activity in severe Florida thunderstorms. Atmospheric
Research, 51: 245–265.
http://www.nssl.noaa.gov/education/svrwx101/lightn
ing/types, accessed June 2015
Christopher J. Schultz, Walter A Peterson, Lawrence
D. Carey, ‘Preliminary Development and Evaluation
of Lightning Jump Algorithms for Real-Time
Detection of Severe Weather’, available online from:
http://journals.ametsoc.org/doi/pdf/10.1175/2009JA
MC2237.1/12/2009
[15]
[16]
[17]
[18]
Scott Rudlosky, Ph.D. ‘Evaluating Ground-Based
Lightning Using TRMM/LIS Observations’,
NOAA/NESDIS/STAR, United states, 2012, pp. 4
Alberto Tapia, James A. Smith, and Michael Dixon,
‘estimation of Convective Rainfall from Lightning
Observations’, available online from:
http://journals.ametsoc.org/doi/pdf/10.1175/15200450%281998%29037%3C1497%3AEOCRFL%3E
2.0.CO%3B2 [27/01/1997]
Steven J Goodman, Patrick N. Gatlin, ‘A Total
Lightning Trending Algorithm to Identify Severe
Thunderstorms’, University of Alabam, in
Huntville, NOAA/NESDIS, available online from:
http://journals.ametsoc.org/doi/pdf/10.1175/2009JTE
CHA1286.1 [07/14/2009]
http://earthnetworks.com/Portals/0/NTSB_Safety_R
ecommendation.pdf, accessed July 2015
https://ams.confex.com/ams/94Annual/webprogram/
Paper240041.html, accessed July 2015
9

supporting paper - Global Severe Weather

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