Lightning and Radar Applications Using WDSSII

1
Lightning and Radar Applications Using WDSS-II

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

2
Outline

• Warning Decision Support System Integrated
  Information (WDSS-II)
• Introduction and Description
• Data Sources
• Lightning, Model-derived, and WSR-88D
• Early WDSS-II research
• Positive lightning study
• Current Techniques
• Storm Type Identification (NSSL)
• Incorporation of Lightning Data (FSU)
• Future Work
• Total Lightning and GOES-R GLM
• Severe vs. Non-Severe Storms

3
WDSS-II

• WDSS-II has three components
• Suite of multi-sensor weather algorithms for
• Combining and interrogating radar data
• Diagnosing hail, lightning and precipitation
• A 3-D display for viewing
  multi-sensor data and algorithm
  output
• Application programming interface
  (API) library in C

WDSS-II related information obtained from
http//www.wdssii.org/
4
WDSS-II

• Real-time Algorithms
• Research system at NSSL/OU
• Several agencies and private companies license
  WDSS-II to run in real-time.
• Algorithm Communication
• Real-time or archived cases
• Algorithm Creator
• Allows users to modify existing, or to create new
  algorithms.

Schematic obtained from http//www.wdssii.org/
5
WDSS-II

• Very complex software
• Steep learning curve
• Extremely advanced starting point
• NSSL discussion forum
• Archived solutions to problems
• Facilitates user feedback
• Becoming more user friendly
• Training modules now available
• Merging of multiple data sources
• Radar, lightning, model-derived, and satellite
  data

6
Data

• Radar Data
• WSR-88D Level II data
• Melbourne, Florida
• Cloud-to-ground (CG) lightning data
• National Lightning Detection Network (NLDN)

• Rapid Update Cycle (RUC) Model Data
• 20 km resolution
• Lightning Detection and Ranging (LDAR)
• Total Lightning Data
• Kennedy Space Center

7
LDAR Data

• Lightning flashes are derived using a spark
  consolidation algorithm
• Murphy et al. (2000), Nelson (2002), and McNamara
  (2002)
• Current forms
• Volume or density of sources
• Flash initiation points
• Counts and/or densities
• Additional Forms
• Flash extent densities
• GOES-R GLM proxy data

Algorithm Derived Flash
15 min LDAR Source Density
8
Previous Approach

• Linking Individual Cells with Lightning
• Storm Cell Identification and Tracking (SCIT)
• Common linking methods
• Predefined radii
• Visual inspection
• Some combination
• No approach is perfect
• Accuracy is time consuming
• Necessitates case study mode
• Our Early Approach
• Define grid or designate box

SCIT Cells
Default grid Grid boxes are used individually or
in combination to track cells and lightning.
9
Previous Approach
Individual Cell
Individual Cell
Group of Cells
Group of Cells
10
WDSS-II Techniques

• SCIT Algorithm
• Intermittent tracking
• Temporally dependent
• Limited storm characteristics
• WDSS-II Algorithms
• Merging of Radar and RUC data
• Near-storm environment information
• Additional radar parameters

3 May 2007 Left Vertically Integrated Liquid
(VIL) Below Precipitation Rate
Reflectivity 0 C
Reflectivity -10 C
Reflectivity -20 C
11
WDSS-II Techniques

• w2segmotion Algorithm
• Hierarchical K-Means clustering method
  (Lakshmanan et al. 2007)
• Identification of storm cells
• Motion estimates
• Create forecasts
• K-Means Clusters
• Features on the scale of 10 km2
• Any gridded field
• e.g., Reflectivity, VIL, LMA, etc.
• Advection of fields

Reflectivity 0.5 Scan
KMeans Cluster of Composite Reflectivity over
Reflectivity 0.5
12
WDSS-II Techniques

• NSSL Storm Type Identification
• Clusters defined by 30 dBZ mean composite
  reflectivity
• Clusters track additional fields
• i.e., VIL, MESH, POSH, SHI, echo top, and low
  level shear
• Algorithm computes statistics
• i.e., maximum, minimum, average, count, standard
  deviation, and time delta
• Decision tree determines storm type

13
WDSS-II Techniques

• Storm Type Example (NSSL)
• Determined using the
• Size, speed, aspect ratio, low-level shear, max
  VIL, MESH, mean reflectivity, and orientation
• Storm types include
• Isolated supercell, line, pulse, and non-severe

14
WDSS-II Techniques

• FSU Algorithm Modification
• Storm type algorithm
• Storm type is dependent on season and location
• Algorithm can be trained
• User created algorithms
• Clusters can be based on different fields
• Incorporation of lightning data

10 June 2007 Cross-section of composite
reflectivity, LDAR sparks during 1 min period
following scan.
15
WDSS-II Techniques

• Temporal Resolution
• Segmotion attempts to fill the gaps
• Lightning data are more frequent
• Currently creating new algorithms
• FSU Algorithm Development
• With clusters based on LDAR
• 1-min resolution
• Modification of storm type algorithm
• Consider lightning parameters
• Short-term forecasts
• Total and cloud-to-ground
• Combination of above
• Severe vs. Non-severe
  determination

10 June 2007
Cross-section of Composite Reflectivity, 1 min of
LDAR sparks between 3 and 4 min after the
volume scan.
16
Continuing Research

• Optimizing the Use of Lightning Data in Severe
  Storm Warning Assessment
• Research Objectives
• Total lightning data
• Regional LDAR/LMA networks
• Few NWS WFOs have real-time access to data
• Determine relationship between total lightning
  and radar
• GOES-R Global lightning mapper (GLM)
• Goodman et al. (2008)
• Total lightning for all NWS WFOs
• Apply LMA relationships to GLM
• Sterling, VA Lightning mapping array (LMA)
• Examination and/or development of proxy data

17
Continuing Research

• Optimizing the Use of Lightning Data in Severe
  Storm Warning Assessment
• Approach
• Automate dataset preparation
• Minimize manual inspection
• Maximize accuracy
• Examine many cases
• Transition out of case study mode
• Statistical Software
• Determine relationships
• Develop scheme to determine severity
• WDSS-II Algorithm
• Consider parameters and trends

18
Summary

• WDSS-II
• Capabilities
• FSU Algorithm Modification
• Transition from case study mode
• Total Lightning Approach
• Relate total lightning to radar
• CG (NLDN) and IC (LMA/LDAR)
• GOES-R GLM
• Development/Evaluation of proxy data
• Can data be used in a similar fashion to
  LDAR/LMA?
• Severe vs. Non-Severe
• Develop algorithms

19
Acknowledgements

• Many thanks to
• Irv Watson (SOO)
• NWS WFO Tallahassee
• Valliappa Lakshmanan
• NSSL/OU
• Steve Goodman
• NESDIS/ORA

Any Questions?
20
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.
• ___, R. Rabin, and V. DeBrunner, 2007
  Multi-scale storm identification and forecast.
  Online at http//cimms.ou.edu/lakshman/Papers/kme
  ans_motion.pdf
• Goodman, S. J., R. J. Blakeslee and W. Koshak,
  2007 Geostationary Lightning Mapper for GOES-R.
• Goodman, S. J., R. J. Blakeslee, W. Koshak, W.
  Petersen, D. E. Buechler, P. R. Krehbiel, P.
  Gatlin, and S. Zubrick, 2008 Pre-launch
  algorithms and risk reduction in support of the
  Geostationary Lightning Mapper for GOES-R and
  beyond. Paper 3.3 , Third Conference on
  Meteorological Applications of Lightning Data,
  New Orleans, Amer. Meteor. Soc.
•  
•  

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., 2007 Characteristics of
positive cloud-to-ground lightning. M.S. thesis,
Florida State University, Tallahassee,
Florida.