Investigation of Total Lightning and Radar Signatures Within Severe and Non-Severe Storms

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Investigation of Total Lightning and Radar
Signatures Within Severe and Non-Severe Storms

• Scott D. Rudlosky
• Henry E. Fuelberg
• Department of Meteorology
• Florida State University

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Related Research

• Prior studies relating total lightning to severe
  storms suggest that lightning data can help
  forecasters assess the potential for severe
  weather.
• Cloud-to-ground (CG) lightning within severe
  storms
• Polarity, multiplicity, and peak current (e.g.,
  MacGorman and Nielsen 1991, Perez et al. 1997,
  Biggar 2002, and Carey et al. 2003)
• Total lightning and severe weather
• Lightning jumps and holes (e.g., Browning
  1964, Williams et al. 1999, Lang et al. 2004,
  Goodman et al. 2005, and Gatlin 2007)
• Intracloud (IC) vs. CG relationships (e.g., Lang
  et al. 2000)
• Severe vs. non-severe (CG and IC relationships)
• e.g., MacGorman and Morgenstern 1998, Carey and
  Rutledge 2003, and Montanya et al. 2007

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Our Approach

• Our Hypothesis
• Total lightning data, used in conjunction with
  radar data, allow researchers and forecasters to
  gain a better understanding of thunderstorm
  morphology and its relation to severe weather.
  (Steiger et al. 2007)
• Objectives
• Develop algorithms and guidelines to determine
  whether a particular storm is likely to require a
  warning
• Determine statistical relationships between
  radar-derived parameters and total lightning
  characteristics
• Create guidance products that best utilize
  existing and future total lightning data (e.g.,
  GOES-R GLM) in assessing storm severity
• Develop for use in NWS warning assessment

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Course of Action

• Generate lightning and radar products
• NLDN, LDAR/LMA, GLM Proxy
• RUC-derived near-storm environment
• WSR-88D, plus merged parameters
• Identify and track individual storm cells
• Link cells with lightning and then compare the
  radar and lightning fields
• Determine storm type, i.e., isolated supercell,
  line, pulse, or non-severe
• Prepare storm database
• Modify and automate procedures
• Statistical analyses of parameters
• Probabilistic determination of severity

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WDSS-II Approach

• Utilize the Warning Decision Support System
  Integrated Information software (WDSS-II)
• Examine multiple data sources simultaneously
• WDSS-II allows us to synthesize, manipulate, and
  display many types of data

WSR-88D
NLDN
LDAR/LMA
IR Satellite
RUC (20 km)
GLM proxy data
Top Right LDAR source locations, NLDN flash
locations, and QC Reflectivity displaying a
negative bolt from the blue Bottom Right
Example of WDSS-II graphical interface displaying
cross-section and plan-views of QC reflectivity
data
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WDSS-II Processing
Storm Relative Helicity
Composite reflectivity NSE windfield (8 km)
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Merged Radar Products

• Merge WSR-88D and RUC-derived data

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Cell Clustering Data Mining
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LDAR/LMA Parameters

• Within K-Means clusters
• Example parameters
• Level Densities, VILMA, Layer Averages, Height of
  Max LMA, etc.
• Over varying periods of time
• Compute differences, trends,
• lifetime statistics, etc.
• Value added by the 3rd dimension
• Aspect ratio of column
• Evaluate storm life cycles
• Compare with WSR-88D
• Lightning jumps at various levels
• Sudden shifts in the vertical

Above 1 min LDAR vertical profiles between
successive WSR-88D volume scans
Right Dots are 1 min LDAR source locations
Cross Section LDAR source density Plan
View LDAR source density 8 km above ground level
(AGL)
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NLDN Parameters

• Separately evaluate parameters for total,
  negative, and positive CG flashes
• Flash Density
• Percentage Positive
• Peak Current
• Multiplicity

Above Reflectivity at isotherm levels, average
CG multiplicity, and CG density Left Average
CG peak current, density, and percentage positive
Right Dots indicate 1 min LDAR flash initiation
points and and signs represent CG flashes
Cross-Section LDAR source density Plan-view
K-Means cluster and CG Flash Density
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GOES-R Global Lightning Mapper
LDAR profiles at 1 min intervals

• GLM Proxy Parameters
• Currently under development
• Consider LDAR column profile
• Fuzzy logic to weight levels
• Determine total lightning activity
• During 1 min periods
• Amount and temporal distribution
• Compare with original parameters
• GLM Applicability Risk Reduction
• Determine suitability of using GLM data in its
  native form to assess storm severity
• Develop modifications to maximize the benefits of
  utilizing GLM data

Grid Spacing
Top Right 1 min LDAR source densities at 1 km
intervals Bottom Right Idealized clusters at
different times shown to compare the spacing of
the LDAR and NLDN grids with that of the GLM
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Examine Many Storms

• Streamline database development and analysis
• Automate procedures from database creation
  through the visualization of individual storms
• Minimizes manual inspection
• Maximizes accuracy
• Complements case study mode
• Storm query and display procedures
• Determine storm track for comparison with
• Distance from the LDAR/LMA network
• Distance from the WSR-88D
• Storm duration
• Long-lasting, complete life cycle
• Quickly developing features

Scale 0
Scale 1
Scale 2
K-Means clusters of composite reflectivity
defined by areas containing average reflectivity
values greater than 10 dBZ
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Desirable Attributes
13 June 2007 Cell 253 Tracks directly over
the radar during peak lightning production

• Modularity is key (Lang and Rutledge 2008)
• Our scheme must be applicable to
  different geographical regions
• Provides a framework that allows
  continuing improvements
• New technology
• Additional knowledge
• Use currently available parameters to
  make most accurate determination
• If a data source is missing, leverage
  the remaining data to assist during the
  warning decision process
• Level of confidence will be affected

Selected radar-derived parameters overlain with
Top Right Maximum and average
Vertically integrated LMA (sum of entire column)
Bottom Right Maximum and average
cloud-to-ground flash density within the cell
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Develop New Storm Intensity Algorithms

• Statistical Approach Utilize Regression
  Techniques
• Select the optimum parameters
• Parameter combinations are infinite
• Determine best relationships and combinations
• Larger statistical sample than individual case
  studies
• Relate chosen parameters to storm type
• Trends in lightning and radar parameters
• Observe the 3-D development
• Examine many severe and non-severe storms
• Develop probabilistic forecasts of severity
• Improve the lead time for warning severe events
• Incorporate total lightning to quantify storm
  severity at increasing distance from the radar

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Concluding Remarks

• Overriding Theme
• Focus on the decision support process, eventually
  package for dissemination to NWS WFOs
• Help insure that NWS is currently using lightning
  data to best advantage when assessing severe
  weather events
• Help insure that the NWS is fully prepared to
  utilize the upcoming GLM data to aid in
  determining severe weather potential
• Some Perceived Benefits
• Transition away from the case study mode
• Develop robust storm-scale relationships between
  lightning, radar, and severe weather
• Quantify total lightning characteristics for use
  in nowcasting the development of severe weather