Lightning Studies at Florida State University

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Lightning Studies at Florida State University

• Scott D. Rudlosky
• Ph.D. Candidate, Florida State University
• Henry E. Fuelberg
• Professor of Meteorology, Florida State University

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Prof. Fuelbergs Lightning Team

• Alumni
• Dr. Phillip Shafer
• NWS/MDL - Silver Springs, MD
• Dr. Geoffrey Stano
• ENSCO/SPoRT - Huntsville, AL
• Geoffrey Wagner
• RS Information/NWS-MDL - Silver Springs, MD
• Jessica Stroupe
• NWS WFO - Slidell, LA
• Current Students
• Amanda Hansen Ph.D. Candidate
• Scott Rudlosky Ph.D. Candidate
• Holly Anderson Masters Candidate
• Pete Saunders Masters Candidate

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Outline

• Background
• Past Research
• NWS WFO Tallahassee
• Florida Power and Light Corp.
• Geographic Information System Applications (CSTAR
  and FPL)
• Total Lightning Studies (NASA - Kennedy Space
  Center)
• Current and Future Research
• NASA KSC
• NOAA - COMET
• NOAA/NESDIS

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NWS WFO Tallahassee

• CG climatologies by flow regime
• Jessica Stroupe (M.S.)
• Vector mean wind between 1000 -700 hPa
• For warm season months of May through September.
• First guess for daily convective activity
  associated with the sea breeze.
• Used as input for probability of precipitation
  (PoP) grids.

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FPL Lightning Guidance

• Forecasts for the occurrence of CG Lightning
• Dr. Philip Shafer
• Originally for 13 individual FPL service areas
  (roughly half the size of a county).
• Earliest equations utilized morning rawindsonde
  data
• Focused on populated regions
• FPL shifted focus
• Developed spatial forecast guidance product for
  CG across the entire state of Florida.

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Objectives
FPL Lightning Guidance

• Type A composite sea-level pressure

Type A frequency 1800-2059 Z
Type B composite sea-level pressure
Type B frequency 1800-2059 Z
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FPL Lightning Guidance
June 4, 2004 1800-2059 Z period

• Utilized perfect prognosis (PP) technique
• Create a high-resolution, gridded forecast
  guidance product for warm season CG
• Used data from the Rapid Update Cycle (RUC) model
  and CG data from the National Lightning Detection
  Network (NLDN).
• Most important derived parameters create 3-hourly
  spatial probability forecasts for
• The occurrence of one or more CG flashes.
• Chance of exceeding flash count percentile
  thresholds.
• Binary logistic regression for one or more
  flashes
• Negative binomial (NB) model the amount of
  lightning.

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Positive CG Lightning Study

• Characteristics of positive CG (CG) lighting in
  Florida
• Masters thesis for Scott Rudlosky
• Phase 5 of FPL research
• Climatologies of total CG and CG lightning
  prepared
• Equations to predict percentage of CG created
• Valid within 100 km radius of sounding locations
• Equations for CG had two steps
• Sounding parameters correlated with percentage of
  positive strikes for overall relationship
• Stepwise linear regression then utilized

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Positive CG Lightning Study

• Several conclusions possible
• Percentage Positive increases as...
• Freezing level height increases
• 1000 700 hPa wind speed increases
• Showalter Stability Index increases
• Theta E at 850 hPa increases
• Percentage Positive decreases as
• Mean mixing ratio in lowest 100 hPa increases
• Surface to 1 km shear increases
• Total Totals increases
• Pressure at -10 C increases
• Results agree with the literature in that
  low-level moisture decreases the relative amount
  of CG.

• Low level mean wind speed seems more influential
  than shear magnitude.

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Misclassification of Cloud Pulses

• Prior to the 2002-2003 upgrade, a threshold of
  10 kA was recommended. Afterwards, the
  threshold was changed to 15 kA (Biagi et al
  2007).
• The (small) population of positive discharges
  between 10-20 kA are a mix of CG and cloud
  discharges (Cummins et al. 2006).
• This population is far from small during the warm
  season in Florida.

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Geographic Information System

• Geographic Information System (GIS) software
  provides new approaches to analyze CG lightning
  data.
• GIS has many applications for lightning research
• Visualization of data
• Computation of cell statistics
• Locating/Analyzing strikes of interest
• Incorporation of elevation data sets
• Interrogation of multiple data sets within a
  common framework
• GIS Advantages
• Avoid complex Fortran gridding programs.
• Data analysis and visualization are in same
  software.

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GIS Applications

• U.S. Park Service gridding tool is used to
  produce a grid with the desired cell size and
  spacing.
• Individual strikes are joined to the grid (i.e.,
  each flash is assigned a grid cell), and are then
  summed and combined to produce flash densities.

22 km Grid
Individual CG Flashes
Sum of CG Flashes
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GIS Applications

• GIS files are easily manipulated for flash
  densities and cell statistics.
• GIS allows comparisons with other data sets
  (i.e., precipitation, elevation, wildfires,
  etc.).
• Extremely fine grid spacing allows identification
  of several grid cells with greater than 25
  flashes km-2 year-1.

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GIS Applications

• Forecast equations for thunderstorm development
• By Masters Student Geoffrey Wagner
• For West Texas and New Mexico regions
• Utilize sounding, reanalysis, elevation and NLDN
  data
• This research differs from our previous studies
  in that

• Topography is the dominant forcing mechanism.
• GIS computes the aspect ratio and slope for each
  grid cell.
• Study attempts to predict the initiation of
  thunderstorms (i.e., the first CG flash) during a
  specified time period.

Data from the United States Geological Survey
(USGS) National Elevation Dataset (NED)
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GIS Applications

• Binary Logistic Regression (BLR) techniques
  developed statistical relations between lightning
  initiation and candidate predictors.
• Equations developed and employed to produce
  deterministic and probabilistic forecasts for
  thunderstorm initiation.

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GIS Applications

• Scott Rudlosky (Ph.D. Candidate) related
  wildfires with CG flashes and precipitation
  estimates.
• Database contains the location of each wildfire,
  statistics on CG within 2 km, and a 24 h
  precipitation estimate.
• Over 70 of all reported lightning initiated
  wildfires during May, June, and July 2007 were
  physically linked to CG lightning.
• Positive lightning was associated with less than
  7 of all lightning induced wildfires.

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Total Lightning Studies

• Our total lightning studies utilized the
  Lightning Detection and Ranging (LDAR) network at
  Kennedy Space Center (KSC).

• Dr. Geoffrey Stano employed a flash consolidation
  algorithm to combine individual sparks into
  flashes. (Murphy et al. 2000 McNamara 2002
  Nelson 2002)

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Total Lightning Studies

• KSC is one of the few regions in the U.S. that
  issues lightning advisories.
• The U.S. Air Forces 45th Weather Squadron (45WS)
  has a good record of forecasting the initiation
  of lightning.
• When should an advisory be canceled?
• Dr. Geoffrey Stano examined LDAR, CG, sounding,
  and WSR-88D data to produce lightning cessation
  guidelines.

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Current Research
2052 UTC 6 June 2007

• Continuing cessation studies
• Masters student Holly Anderson
• New techniques utilizing WDSS-II

Reflectivity at 0 C
30 dBZ Cluster
Reflectivity at -20 C
Reflectivity at -10 C
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Current Research

• Expanding CG forecast techniques to new locations
• Pete Saunders (Masters Student)
• Through COMET
• NWS WFO Pueblo, CO
• NWS WFO Pendleton, OR
• NWS WFO Sterling, VA
• Combining and applying earlier techniques
• Dr. Phil Shafer for Florida
• Geoffrey Wagner for West Texas New Mexico

Images courtesy of Steven Hodanish
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Current Research

• Ph.D. Candidate Scott Rudlosky is working on
  Optimizing the Use of Lightning Data in Severe
  Storm Warning Assessment.
• Project relies heavily on newly developed WDSS
  techniques and algorithms which have yet to be
  created.
• Data will include LMA/LDAR total lightning data,
  NLDN CG lightning data, RUC-derived model data,
  WSR-88D data, and new data sets as they become
  available.
• Our goal is procedures and/or guidelines to
  optimally utilize total lightning data from the
  GOES-R Global Lightning Mapper (GLM), thereby
  leading to more accurate severe storm warnings.

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Conclusions

• The Fuelberg Lightning Group at Florida State
  University continues to focus on operational
  lightning research.
• This approach has benefited both the public (NWS,
  NASA, KSC, FDOF) and private (FPL) sectors.
• Our research fits into four general categories
• Forecasting CG lightning in the 0-24 h time range
• Forecasting lightning cessation
• Analyzing the relationship between environmental
  conditions and lightning patterns within severe
  and non-severe storms
• Relating lightning and precipitation to the
  initiation of wildfires

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Conclusions

• Several key aspects include
• Newly developed GIS techniques
• Algorithm creation within the WDSS-II software
• Collaborations with the public and private
  sectors
• Innovative tools and procedures
• Preparing for new datasets to help ease
  transition
• Looking ahead to the GLM and polarimetric radar
• Will have the tools necessary to make optimal use
  of these data

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References

• Lakshmanan, V., T. Smith, G. J. Stumpf, and K.
  Hondl, 2007 The warning decision support system
  - integrated information (WDSS-II). Weather and
  Forecasting, 22, No. 3, 592-608.
•  
• Lericos, T. P., H. E. Fuelberg, A. I. Watson, and
  R. L. Holle, 2002 Warm season lightning
  distributions over the Florida peninsula as
  related to synoptic patterns. Wea. Forecasting,
  17, 83-98.
•  
• McNamara, T. M., 2002 The horizontal extent of
  cloud-to-ground lightning over the Kennedy Space
  Center, M.S. Thesis, Air Force Institute of
  Technology, 114 pp.
• Murphy, M. J., K. L. Cummins, and L. M. Maier,
  2000 The analysis and interpretation of
  three-dimensional lightning flash information.
  16th Int. Conf. on IIPS for Meteorology,
  Oceanography, and Hydrology, Long Beach, CA,
  Amer. Meteorol. Soc., 102-105.
• Nelson, L. A., 2002 Synthesis of 3-dimensional
  lightning data and radar to determine the
  distance that naturally occurring lightning
  travels from thunderstorms, M.S. Thesis, Air
  Force Institute of Technology, 85 pp.
• Rudlosky, S.D., and H. E. Fuelberg, 2007
  Characteristics of positive cloud-to-ground
  lightning. M.S. Thesis, Florida State University.
•  
• ___, and ___, 2007 The relation between
  lightning and wildfires in Florida, Third
  Conference on Meteorological Applications of
  Lightning Data, American Meteorological Society
  (AMS), New Orleans, Louisiana, Jan 21 Jan 23,
  2008, P2.6.
•  

Shafer, P. E., and H. E. Fuelberg, 2006 A
statistical procedure to forecast warm season
lightning over portions of the Florida peninsula.
Wea. Forecasting, 21, 851-868.   ___, and ___,
2007 A perfect prognosis scheme for forecasting
warm season lightning over Florida. Accepted by
Mon. Wea. Rev.   Smith, J. R., H. E. Fuelberg,
and A. I. Watson, 2005 Warm season lightning
distributions over the northern Gulf of Mexico
coast and their relation to synoptic-scale and
mesoscale environments. Wea. Forecasting, 20,
415-438.   Stano, G. T., 2007 Developing
empirical lightning forecast guidance for the
Kennedy Space Center. Ph.D. Dissertation,
Florida State University.   Wagner, G. A., H. E.
Fuelberg, D. Kann, R. Wynn, and S. Cobb, 2005 A
GIS-based approach to lightning studies for west
Texas and New Mexico. Second Conference on
Meteorological Applications of Lightning Data,
American Meteorological Society (AMS), Atlanta,
Georgia, Jan 30 Feb 1, 2006.   Wolf, P., 2006
Anticipating the initiation, cessation, and
frequency of cloud-to-ground lightning utilizing
WSR-88D reflectivity data, available from the
author at NWS Jacksonville FL.
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• Lightning Parameterization Module
• WRF CHEM
• Accurately specify NOx produced by lightning
• Tropospheric ozone forecasts
• WDSS-II
• Relate storm parameters to lightning