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Investigation of Total Lightning and Radar Signatures Within Severe and NonSevere Storms

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Title: Investigation of Total Lightning and Radar Signatures Within Severe and NonSevere Storms


1
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

2
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

3
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

4
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

5
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
6
WDSS-II Processing
Storm Relative Helicity
Composite reflectivity NSE windfield (8 km)
7
Merged Radar Products
  • Merge WSR-88D and RUC-derived data

8
Cell Clustering Data Mining
9
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)
10
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
11
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
12
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
13
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
14
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

15
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
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