Quality Control Data
Resume:
Valliappa LAKSHMANAN
Work Address Home Address
Radar Research and Development Division 612 Trisha Ln.
National Severe Storms Laboratory Norman OK 73072
Ph: 405-***-****
*** ***** *. ***** ****, Norman OK 73072-7327
Ph: 405-***-****
Email: abpnlp@r.postjobfree.com
Updated resume: http://www.cimms.ou.edu/ lakshman/web/
Expertise in machine intelligence R&D for meteorological applications.
Summary
Expertise designing and developing large-scale software systems.
Excellent skills in communicating technical and non-technical material to diverse audiences.
Employment CIMMS - OU & NSSL Norman OK
History 07/95 Present
I am a Research Scientist who has developed several real-time pattern recognition algorithms and
visualization techniques for weather phenomena. I am also the technical lead on several software
projects, including the Warning Decision Support System Integrated Information (WDSS-II).
R ESEARCH I NSTITUTE, C LEVELAND C LINIC F OUNDATION Cleveland OH
04/94 04/95
Developed an image processing and visualization technique to automatically identify 2D cross-
sections of the mitral valve in ultrasound images and to render animated 3D views fast enough to
view interactively.
T HE O HIO S TATE U NIVERSITY M EDICAL S CHOOL Columbus OH
02/94 06/95
Developed a tutorial program to aid medical students in the study of anatomy. This involved image
processing and visualization of MRI images and CT scans of cadavers.
Education T HE U NIVERSITY OF O KLAHOMA Norman OK
01/1999 12/2001
Ph.D in Electrical and Computer Engineering. Research topic was the development of a texture
segmentation algorithm whose outputs are nested partitions. This research enabled the develop-
ment of a multiscale framework for the identi cation and tracking of storms at different scales.
T HE O HIO S TATE U NIVERSITY Columbus OH
08/93 06/95
M.S. in Biomedical Engineering concentrating on image processing and computer vision and their
applications to medicine.
T HE I NDIAN I NSTITUTE O F T ECHNOLOGY Madras, India
08/89 06/93
B.Tech. in Electronics & Communications Eng.
Architect of the Warning Decision Support System Integrated Information (WDSS-II)1, a suite
Research &
Development of multi-sensor machine-intelligence algorithms, tools and displays for research, weather analysis
Experience and severe weather warning decision-making. Uses C++, Java, J2EE, XML, CORBA, OpenGL
and network programming on Linux and Windows (1999-)
1 http://www.wdssii.org
Developed satellite multi-channel nowcasting technique under consideration for possible inclusion
in GOES-R suite of algorithms (2008)
Designed and built an automated storm type classi cation system based on observed radar data
(2007)
Designed and built geographic information systems (GIS) to display, monitor and disseminate
warning and watch polygons (2006)
Designed and built a neural network to quality-control radar data (2003)
Designed and built a 4-D multisource merging process to assimilate information from multiple
sensors. (2002)
Technical lead on team integrating four-dimensional weather data with terrain information. Uses
C++, OpenGL, Geographical Information Systems (2000 2002)
Researched and developed a multiscale framework that enables the identi cation and tracking of
storms at different scales and makes it possible to devise algorithms to detect weather signatures in
speci c (relative) locations within a storm. (1999-2001)
Technical lead on team driving requirements and design to develop a set of interfaces (Common
Operational Development Environment) to streamline the development and incorporation of new
weather detection algorithms in National Weather Service Of ces around the country. Uses C++,
XML. (1999 2000)
Designer and technical leader in developing a system that will pull in data from all the radar in
a region and combine them for visualization and algorithm processing. This project ( OPUP ),
for the Air Force Weather Service, was rst deployed in 1999-2000 and represented a signi cant
advance over current visualization and radar data handling. Uses C++, XML and Oracle. (1998
2000)
Short (less than 6 mo.) projects and work completed before 2000 omitted for space reasons. Please
see publications for a more complete list of R&D work.
Honors 2012 Innovator Award by the University of Oklahoma Of ce of Technology Development for de-
veloping groundbreaking (WDSS-II) software [that] is used worldwide to help predict weather
phenomena including hail, precipitation, mesocyclones, and tornadoes. Used by private companies,
research labs, National and International governments across the globe, this technology provides
users across the world with the information needed to make property and life-saving decisions in
the event of hazardous weather .
Nominated by National Severe Storms Laboratory for Presidential Early Career Award for Scien-
tists and Engineers (PECASE), 2006
NOAA Tech 2004 Award for Best Presentation in the category of Techology Transfer to Operations:
Real-time Dissemination of WSR-88D Radar Data over Internet2.
Nominated for the National Oceanographic and Atmospheric Administration (NOAA) s Technol-
ogy Transfer Award, 1998.
University Fellow, The Ohio State University, 1993-94.
Third in the IIT, Madras Department of Electrical Engineering (of 75 students: top 5%) in 1989-
1993.
Named among the top 1% of Indian high school graduates in 1989.
Grants Co-PI, $950K National Science Foundation grant, Real-time Data Mining, 2002-05. PIs: Theodore
Trafalis, Michael Richman, myself, S. Lakshmivarahan, Patrick Skubic (NSF ITR 0205628, 2002-
2007).
PI, $900K National Science Foundation grant, Integrating 3D Dynamic Meteorological Data and
Algorithms into a Scalable Geospatial Framework, 2000-04. Collaborators: William Ribarsky
and Nicholas Faust of Georgia Tech Research Institute (NSF-CCF 9982299 for a total amount of
$1.9m, 2000-2004)
PI, $313K Warning Decision Support System Integrated Information Licensing Fees, 2005-2011
PI, $303K Warning Decision Support System Integrated Information Licensing Fees, 2012 to date
PI, $235K NOAA/NWS Meteorological Development Laboratory, Automated Tuning of Au-
toNowCaster, 2010-2012.
Co-PI, $90K NOAA High Performance Computing (HPCC) Initiative, Satellite Data Ingest and
Visualization, 2002. PIs: Robert Rabin (NSSL) and myself
Co-PI, $87K NOAA HPCC, A simple grid computing technique for reanalysis of CONUS radar
data bene tting research and operations, 2008. PIs: Travis Smith, Kevin Kelleher, Stephen Del-
Greco (NCDC)
PI, $80K NOAA/NESDIS GOES-R Algorithms Working Group on Precipitation Nowcasting,
2007.
Co-PI, $67K NOAA High Performance Computing (HPCC) Initiative, Real-time Distribution of
WDSS-II Algorithm Information, 2005. PIs: Russell Schneider (Storm Prediction Center) and
myself
Co-PI, $61K NOAA HPCC, Four-dimensional Real Time CONUS radar products over the WWWW,
2007. PIs: Kurt Hondl (NSSL) and myself
Co-PI, $58K NOAA High Performance Computing (HPCC) Initiative, Visualization of short-term
forecasts of clouds and precipitation: testing the nWave data circuit, 2010. PIs: Bob Rabin and
myself
Co-PI, $57K NOAA High Performance Computing (HPCC) Initiative, Enabling Communication
of WDSS-II Severe Storm Data Over a Network, 2004. PIs: Russell Schneider (Storm Prediction
Center) and myself
Co-PI, $53K NOAA High Performance Computing (HPCC) Initiative, Rapid Access to real-time
and forecast products through a Web Map Service and nWave data circuits, 2011. PIs: Bob Rabin,
Russ Dengel, Dan Lindsey and myself
Co-PI, $37K NOAA HPCC, Visualization of model forecasts as Satellite Visible Imagery, 2008.
PIs: Robert Rabin (NSSL) and myself
PI, $30K USGS, Improving weather radar data processing for biological research applications,
2012.
PI, $35K NOAA/NESDIS GOES-R Algorithms Working Group on Precipitation Nowcasting,
2011.
Co-PI, $30K NESDIS GOES-R Algorithms Working Group on Precipitation Nowcasting, 2007.
PIs: Robert Rabin (NSSL) and myself
PI, $30K USAID Disaster Mitigation and Recovery, 2007: Collaborators: Roy Bhomic (India
Meteorological Department), Kurt Hondl (NSSL), Ming Xue (OU)
Co-PI, $32K GOES-R Risk Reduction, 2009. PIs: Robert Rabin and myself
Co-PI, $30K NOAA National Weather Service, Weather Bug lightning data validation, 2009. PIs:
Don MacGorman and myself
PI, $27K NOAA/NESDIS GOES-R Algorithms Working Group on Precipitation Nowcasting,
2010.
Co-PI, $20K NOAA HPCC, Operational Severe Weather Forecast Monitoring using Radar Data
Grids and Web-based Tools, 2007. PIs: Jason Levit (SPC) and myself
PI, $15K NOAA/NESDIS GOES-R Algorithms Working Group on Precipitation Nowcasting,
2009.
Co-PI, $15K NASA Detection of Thunderstorms from Satellite Visible Imagery, 2007. PIs: Robert
Rabin (NSSL) and myself
Software design, architecture, implementation and technical leadership experience using Object-
Computer Skills
Oriented methods.
An expert in C++, C and Java.
Familiar with various technologies including Spring, JEE, JDBC, servlets, XML, CORBA,
Qt, OpenGL; operating systems including UNIX and Windows; concepts including Object-
Oriented Analysis and Design, design patterns and multithreaded programming.
Instructor/author of corporate courses in: Spring Framework, Java security, Java Best Prac-
tices, JEE/J2EE Design Patterns, Java and XML, Object Oriented Design Patterns, Introduc-
tory Java Programming, Advanced C++ Programming and Design, Service Oriented Archi-
tectures and XML Web Services
Member, American Meteorological Society committee on Interactive Information and Pro-
Panels,
Invited Talks cessing Systems, 2010-2012
Organizer, 2008 Arti cial Intelligence Competition2 Jan 2008
Member, American Meteorological Society committee on Arti cial Intelligence3 2004-2009
Reviewer for National Science Foundation Dynamic Data Driven Applications program, J. of
Applied Meteorology, J. of Oceanic and Atmospheric Technology, J. of Weather, Climate and
Society, Weather and Forecasting, IEEE Transactions on Geosciences and Remote Sensing,
IEEE Transactions on Evolutionary Computation, Advances in Atmospheric Sciences
Session Chair/Program Committee Member for:
Arti cial Intelligence Applications, American Meteo. Society (multiple years)
Interactive Information Processing, American Meteo. Society (multiple years)
ACM Knowledge Discovery and Data Mining (2011)
IEEE Conf. on Pattern Analysis and Recognition (2007)
SPIE Optics + Photonics: Satellite Data Compression, Communications and Archiving
III (2007)
One of two NSSL representatives in the working group on the Common Operational Devel-
opment Environment. (1998-2000)
Invited talks:
2 http://nws.met.psu.edu/ai/AMS2008/index.html
3 http://nws.met.psu.edu/ai/
1. To Geneva Center for Security Policy Arti cial Intelligence Implications on Environ-
ment, May. 2011, Geneva, Switzerland
2. To GOES-R GLM AWG/R3 Science Meeting An Algorithm to Identify and Track Objects
on Spatial Grids, Sep. 2009, Huntsville, AL
3. To GOES-R satellite compression working group An Overview of Radar Compression,
Aug. 2007, San Diego, CA
4. Short course on Arti cial Intelligence Machine Learning Techniques on Spatial Grids,
Jan. 2007, Corpus Christi, TX
5. NEXRAD Technical Advisory Committee Meeting (Information Brie ng) Quality Con-
trol Neural Network, Oct. 12, 2005, San Diego, CA.
6. Workshop on Severe Weather Technology for National Weather Service (NWS) Decision
Making, organized by the NWS Meteological Development Laboratory WDSS-II: Aiding
Severe Weather Forecasting, July 13, 2005, Silver Spring, MD
7. Int l Conference on Advances in Pattern Recognition Quality Control of Radar Re ec-
tivity Data Using Texture Features and a Neural Network, Dec. 13, 2003, Kolkota
8. Los Alamos National Laboratory: Fuzzy detection in Radar Images,, Jan 31, 1997
9. University of Tulsa: Electrical Engineering in Meteorological Research,, Oct 29, 1999
Students Graduate Students:
Jianting Zhang (Ph.D, Computer Science, 2002-04) now: Assistant Professor, City University
of New York
Angelyn Kolodziej (MS Meteorology, 2010, U. Oklahoma)
Jennifer Newman (MS Meteorology, U. Oklahoma, in progress)
John Cintineo (MS Meteorology, U. Oklahoma, in progress)
Madison Burnett (MS Meteorology, U. Oklahoma, in progress)
Committee: Shawn McCaroll (MS, ECE, 2008)
Undergraduate research: Becca Mazur (2003), Jennifer Green (2004), Angela Fritz (2005), Eric
Guillot (2007), Chris Wilson (2008), John Cintineo (2008), Travis Visco (2009), Madison Burnett
(2009)
Journal Articles V. Lakshmanan, K. Hondl, C. Potvin, and D. Preignitz, An improved method to compute radar
echo top heights, Wea. Forecasting, vol. 0, no. 0, p. subm, 0.
V. Lakshmanan, M. Miller, and T. Smith, Quality control of accumulated elds by applying spatial
and temporal constraints, J. Atmos. Ocean. Tech., vol. 0, p. subm, 2012.
A. Zahraei, K. Hsu, S. Sorooshian, J. Gourley, V. Lakshmanan, Y. Hong, and T. Bellerby, Quan-
titative precipitation nowcasting: A lagrangian pixel-based approach, Atmos. Research, vol. 0,
no. 0, p. accepted, 2012.
J. Cintineo, T. Smith, V. Lakshmanan, H. Brooks, and K. Ortega, An objective high-resolution
hail climatology of the contiguous united states, Wea. Forecasting, vol. 1, p. 1, 2012.
J. Kain, M. Coniglio, J. Correia, A. Clark, P. Marsh, C. Ziegler, V. Lakshmanan, S. Miller, S. Dem-
bek, S. Weiss, F. Kong, M. Xue, R. Sobash, R. Dean, I. Jirak, and C. Melick, A feasibility study
for probabilistic convection initiation forecasts based on explicit numerical guidance, Bull. Amer.
Meteor. Soc., vol. 0, no. 0, p. submitted, 2012.
J. Sieglaff, D. Hartung, W. Feltz, L. Cronce, and V. Lakshmanan, Development and application of
a satellite-based convective cloud object-tracking methodology: A multipurpose data fusion tool,
J. Applied Meteorology and Clim., vol. s, p. s, s 2012.
J. Newman, V. Lakshmanan, P. Heinselman, M. Richman, and T. Smith, Range-correcting az-
imuthal shear in dopper radar data, Wea. Forecasting, vol. 0, p. Submitted, 2011.
P. Marsh, J. Kain,, V. Lakshmanan, A. Clark, N. Hitchens, and J. Hardy, A method for calibrating
deterministic forecasts of rare events, Wea. Forecasting, vol. 27, pp. 531 538, 2012.
V. Lakshmanan, J. Crockett, K. Sperrow, M. Ba, and L. Xin, Tuning the auto-nowcaster automat-
ically, Wea. Forecasting, vol. 0, no. 0, p. InPress, 2012.
V. Lakshmanan, R. Rabin, J. Otkin, J. Kain, and S. Dembek, Visualizing model data using a fast
approximation of a radiative transfer model, J. Atmos. Ocean. Tech., vol. 29, pp. 745 754, 2012.
V. Lakshmanan, Image processing of weather radar re ectivity data: Should it be done in z or
dbz?, Elec. J. Severe Storms Meteo., vol. 7, no. 3, pp. 1 4, 2012.
V. Lakshmanan, J. Zhang, K. Hondl, and C. Langston, A statistical approach to mitigating persis-
tent clutter in radar re ectivity data, IEEE J. Selected Topics in Applied Earth Observations and
Remote Sensing, vol. 5, pp. 652 662, 4 2012.
A. Hobson, V. Lakshmanan, T. Smith, and M. Richman, An automated technique to categorize
storm type from radar and near-storm environment data, Atmos. Research, vol. 111, no. 7, pp. 104
113, 2012.
M. Zhu, V. Lakshmanan, P. Zhang, Y. Hong, K. Cheng, and S. Chen, Spatial veri cation using a
true metric, Atmospheric Research, vol. 102, no. 4, pp. 408 419, 2011.
S. McCarroll, M. Yeary, D. Hougen, V. Lakshmanan, and S. Smith, Approaches for compression
of super-resolution WSR-88D data, IEEE Tran. on Geosci. and Remote Sensing Letters, vol. PP,
no. 99, pp. 191 195, 2010.
S. Sen Roy, V. Lakshmanan, S. Roy Bhowmik, and S. Thampi, Doppler weather radar based
nowcasting of cyclone ogni, J. Earth Syst. Sci., vol. 119, no. 2, pp. 183 199, 2010.
V. Lakshmanan and J. Kain, A Gaussian mixture model approach to forecast veri cation, Wea.
Forecasting, vol. 25, no. 3, pp. 908 920, 2010.
V. Lakshmanan and T. Smith, An objective method of evaluating and devising storm tracking
algorithms, Wea. Forecasting, vol. 25, no. 2, pp. 721 729, 2010.
V. Lakshmanan, K. Elmore, and M. Richman, Reaching scienti c consensus through a competi-
tion, Bull. of Amer. Meteo. Soc., vol. 91, pp. 1423 1427, 2010.
T. Smith and V. Lakshmanan, Real-time, rapidly updating severe weather products for virtual
globes, Computers and Geosci., 2009. DOI: 10.1016/j.cageo.2010.03.023.
V. Lakshmanan, J. Zhang, and K. Howard, A technique to censor biological echoes in radar re-
ectivity data, J. Applied Meteorology, vol. 49, pp. 435 462, 3 2010.
V. Lakshmanan and T. Smith, Data mining storm attributes from spatial grids, J. Ocea. and
Atmos. Tech., vol. 26, no. 11, pp. 2353 2365, 2009.
V. Lakshmanan, K. Hondl, and R. Rabin, An ef cient, general-purpose technique for identifying
storm cells in geospatial images, J. Ocean. Atmos. Tech., vol. 26, no. 3, pp. 523 37, 2009.
I. Adrianto, T. Trafalis, and V. Lakshmanan, Support vector machines for spatiotemporal tornado
prediction, Int l J. of General Systems, vol. 38, no. 7, pp. 759 776, 2009. DOI:10.1080/03081070601068629.
V. Lakshmanan, T. Smith, G. J. Stumpf, and K. Hondl, The warning decision support system
integrated information, Wea. Forecasting, vol. 22, no. 3, pp. 596 612, 2007.
V. Lakshmanan, A. Fritz, T. Smith, K. Hondl, and G. J. Stumpf, An automated technique to quality
control radar re ectivity data, J. Applied Meteorology, vol. 46, pp. 288 305, Mar 2007.
N. Pal, A. Mandal, S. Pal, J. Das, and V. Lakshmanan, Fuzzy rule-based approach for detection of
bounded weak-echo regions in radar images, J. Appl. Meteo. and Clim., vol. 45, no. 9, pp. 1304
1312, 2006.
V. Lakshmanan, T. Smith, K. Hondl, G. J. Stumpf, and A. Witt, A real-time, three dimensional,
rapidly updating, heterogeneous radar merger technique for re ectivity, velocity and derived prod-
ucts, Wea. Forecasting, vol. 21, no. 5, pp. 802 823, 2006.
V. Lakshmanan, A separable lter for directional smoothing, IEEE Geosci. Remote Sensing
Letters, vol. 1, pp. 192 195, 7 2004.
V. Lakshmanan, R. Rabin, and V. DeBrunner, Multiscale storm identi cation and forecast, J.
Atm. Res., vol. 67, pp. 367 380, July 2003.
V. Lakshmanan, Speeding up a large scale lter, J. of Oc. and Atm. Tech., vol. 17, pp. 468 473,
April 2000.
V. Lakshmanan, Using a genetic algorithm to tune a bounded weak echo region detection algo-
rithm, J. of Applied Meteorology, vol. 39, pp. 222 230, 2 2000.
C. Marzban and V. Lakshmanan, On the uniqueness of gandin and murphy s equitable perfor-
mance measures, Monthly Wea. Review, vol. 127, pp. 1134 1136, June 1999.
V. Lakshmanan and A. Witt, A fuzzy logic approach to detecting severe updrafts, AI Appl.,
vol. 11, pp. 1 12, May 1997.
Conferences V. Lakshmanan and J. Kain, Detecting convective initiation using radar images, in Proc. of 7th
European Conf. on Radar in Meteo. and Hydro., (Toulousse, France), p. P198, ERAD, 2012.
J. Kain, M. Coniglio, J. Correia, A. Clark, P. Marsh, C. Ziegler, V. Lakshmanan, S. Miller, S. Dem-
bek, S. Weiss, F. Kong, M. Xue, R. Sobash, R. Dean, I. Jirak, and C. Melick, A feasibility study
for probabilistic convection initiation forecasts based on explicit numerical guidance, Bull. Amer.
Meteor. Soc., vol. 0, no. 0, p. submitted, 2012.
V. Lakshmanan, Should image processing of weather radar re ectivity data be done in z or in
dbz?, in Proc. of 7th European Conf. on Radar in Meteo. and Hydro., (Toulousse, France),
p. P197, ERAD, 2012.
V. Lakshmanan, Image processing of weather radar re ectivity data: Should it be done in z or
dbz?, Elec. J. Severe Storms Meteo., vol. 7, no. 3, pp. 1 4, 2012.
V. Lakshmanan, J. Zhang, K. Hondl, and C. Langston, Objective method of creating a clutter
bypass map, in Proc. of 7th European Conf. on Radar in Meteo. and Hydro., (Toulousse, France),
p. P196, ERAD, 2012.
V. Lakshmanan, J. Zhang, K. Hondl, and C. Langston, A statistical approach to mitigating persis-
tent clutter in radar re ectivity data, IEEE J. Selected Topics in Applied Earth Observations and
Remote Sensing, vol. 5, pp. 652 662, 4 2012.
V. Lakshmanan, R. Rabin, J. Otkin, and J. Kain, Visualizing a model using synthetic visible
imagery, in International Symposium on Earth-science Challenges: 2nd Summit between the Uni-
versity of Oklahoma and Kyoto University, (Norman, OK), p. 14, Sep 2011.
V. Lakshmanan, R. Rabin, J. Otkin, and J. Kain, Approximating radiative transfer with a neural
network, in 10th Conf. on Arti cial Intelligence App. to Env. Sci., (New Orleans), p. TJ14.3, Jan
2012.
V. Lakshmanan, R. Rabin, J. Otkin, J. Kain, and S. Dembek, Visualizing model data using a fast
approximation of a radiative transfer model, J. Atmos. Ocean. Tech., vol. 29, pp. 745 754, 2012.
V. Lakshmanan, Detecting convective inititation from radar, in International Symposium on
Earth-science Challenges: 2nd Summit between the University of Oklahoma and Kyoto Univer-
sity, (Norman, OK), p. 13, Sep 2011.
V. Lakshmanan, Extrapolating radar images using a gaussian mixture model, in Preprints, Sixth
European Conf. on Radar in Meteorology and Hydrology, (Sibiu), National Meteorological Ad-
ministration, Romania, Sep 2010.
V. Lakshmanan and J. Kain, Model veri cation using gaussian mixture models, in 20th Conf. on
Probability and Statistics in the Atmospheric Sciences, (Atlanta, GA), p. 6.4, Amer. Meteor. Soc.,
Jan 2010.
V. Lakshmanan and J. Kain, A Gaussian mixture model approach to forecast veri cation, Wea.
Forecasting, vol. 25, no. 3, pp. 908 920, 2010.
V. Lakshmanan and J. Zhang, Censoring biological echoes in wea. radar images, in 6th Interna-
tional Conf. on Fuzzy Systems and Knowledge Discovery, (Tianjin, China), IEEE, IEEE Computer
Press, Aug. 2009.
V. Lakshmanan and T. Smith, Evaluating a storm tracking algorithm, in 26th Conf. on IIPS for
Meteo., Ocean. and Hydr., (Atlanta, GA), p. 8.2, Amer. Meteor. Soc., Jan 2010.
V. Lakshmanan and T. Smith, An objective method of evaluating and devising storm tracking
algorithms, Wea. Forecasting, vol. 25, no. 2, pp. 721 729, 2010.
V. Lakshmanan, Predicting turbulence using partial least squares regression and an arti cial neural
network, in 7th Conf. on Arti cial Applications to the Environmental Sciences, (Atlanta, GA),
p. 3.3, Amer. Meteor. Soc., Jan 2010.
V. Lakshmanan and T. Smith, Data mining storm attributes from spatial grids, J. Ocea. and
Atmos. Tech., vol. 26, no. 11, pp. 2353 2365, 2009.
V. Lakshmanan, Tuning an algorithm for identifying and tracking cells, in Southern Thunder
2009 Workshop, (Cocoa Beach, FL), p. CD, Nat. Aero. Space Admin., July 2009.
V. Lakshmanan and T. Smith, Lighting warning and prediction using observations and models, in
4th Conf. on the Meteorological Applications of Lightning Data, (Phoenix), p. 6.4, Amer. Meteor.
Soc., Jan 2009.
V. Lakshmanan, An overview of radar data compression, in SPIE Optics + Photonics: Satellite
Data Compression, Communications and Archiving III, no. 07 in 6683, (San Diego, CA), SPIE,
Aug. 2007.
V. Lakshmanan and K. Ortega, A technique for creating probabilistic spatio-temporal forecasts,
in 8th Int l Conf. on Adv. in Patt. Recogn., (Kolkota), IEEE, Jan 2007.
V. Lakshmanan, I. Adrianto, T. Smith, and G. Stumpf, A spatiotemporal approach to tornado
prediction, in Int l Joint Conf. on Neural Networks, (Montreal), p. CDROM 1072, July 2005.
V. Lakshmanan, K. Hondl, G. Stumpf, and T. Smith, Quality control of wea. radar data using
texture features and a neural network, in 5th Int l Conf. on Adv. in Patt. Recogn., (Kolkota),
IEEE, Dec 2003.
V. Lakshmanan, V. DeBrunner, and R. Rabin, Nested partitions using texture segmentation, in
Southwest Symposium on Image Analysis and Interpretation, (Santa Fe, New Mexico), IEEE, IEEE
Computer Press, Apr. 2002.
V. Lakshmanan, V. DeBrunner, and R. Rabin, Texture-based segmentation of satellite weather
imagery, in Int l Conf. on Image Processing, (Vancouver), pp. 732 735, Sept. 2000.
V. Lakshmanan and A. Witt, Detecting rare signatures, in Arti cial Neural Networks in Engi-
neering ANNIE 97, (St. Louis, MO), pp. 521 526, ASME Press, 1997.
V. Lakshmanan and A. Witt, A fuzzy logic classi er for the detection of bounded weak echo
regions in meteorological images, in Arti cial Neural Networks in Engineering ANNIE 96, (St.
Louis, MO), pp. 513 518, ASME Press, 1996.
V. Lakshmanan and A. Witt, Detection of bounded weak echo regions in meteorological images,
in 13th Int l Conf. on Pattern Recognition, (Vienna), pp. 895 899, International Association of
Pattern Recognition, 1996.
V. Lakshmanan and A. Witt, A fuzzy logic scheme for the detection of bounded weak echo re-
gions in meteorological images, in IAPR Workshop on Machine Perception Applications, (Graz,
Austria), pp. 185 198, International Association of Pattern Recognition, 1996.
V. Lakshmanan and A. Witt, Classi cation of skewed distributions: Detecting severe updrafts, in
Arti cial Intelligence and Soft Computing, (Banff, Canada), pp. 37 40, International Association
of Science and Technology for Development, 1997.
V. Lakshmanan, The simpler the better, in 6th Conf. on Arti cial Applications to the Environ-
mental Sciences, (Phoenix, AZ), p. 3.5, Amer. Meteor. Soc., Feb 2009.
V. Lakshmanan, J. Gourley, Z. Flamig, and S. Giangrande, A simple data-driven model for stream-
ow prediction, in 6th Conf. on Arti cial Applications to the Environmental Sciences, (Phoenix,
AZ), p. J6.2, Amer. Meteor. Soc., Jan 2009.
V. Lakshmanan, T. Smith, and R. Rabin, Automated real-time extraction of storm properties from
gridded elds, in Preprints, Fifth European Conf. on Radar in Meteorology and Hydrology,
(Helsinki), Finnish Meteorological Institute, June 2008.
V. Lakshmanan, J. Zhang, and C. Langston, Quality control of canadian radar re ectivity data,
in Preprints, Fifth European Conf. on Radar in Meteorology and Hydrology, (Helsinki), Finnish
Meteorological Institute, June 2008.
V. Lakshmanan, E. Ebert, and S. Haupt, The 2008 arti cial intelligence competition, in 6th Conf.
on Arti cial Intelligence Applications to Environmental Science, (New Orleans), p. 2.1, Amer.
Meteor. Soc., Jan 2008.
V. Lakshmanan and R. Rabin, Preprints, nowcasting of thunderstorms from GOES infrared and
visible imagery, in 5th GOES Users Conf., (New Orleans), p. P1.73, Amer. Meteor. Soc., Jan
2008.
V. Lakshmanan and K. Hondl, A polar-coordinate real-time three-dimensional rapidly updating
merger technique for phased array radar scanning strategies, in 33rd Conf. on Radar Meteorology,
(Cairns, Australia), p. 7.4, Amer. Meteor. Soc., Aug. 2007.
V. Lakshmanan, K. Ortega, and T. Smith, Creating spatio-temporal tornado probability forecasts
using fuzzy logic and motion variability, in 5th Conf. on Arti cial Intelligence Appl. to Environ.
Science, (San Antonio, TX), Amer. Meteor. Soc., 2007.
V. Lakshmanan, T. Smith, K. Cooper, J. Levit, K. Hondl, G. Stumpf, and D. Bright, High-
resolution radar data and products over the continental united states, in Preprints, 22th Int l Conf.
on Inter. Inf. Proc. Sys. (IIPS) for Meteor., Ocean., and Hydr., (Atlanta), Amer. Meteor. Soc., Feb
2006.
V. Lakshmanan and G. Stumpf, A real-time learning technique to predict cloud-to-ground light-
ning, in Preprints, Fourth Conf. on Arti cial Intelligence Applications to Environmental Science,
(San Diego), p. J5.6, Amer. Meteor. Soc., Jan 2005.
V. Lakshmanan, G. Stumpf, and A. Witt, A neural network for detecting and diagnosing tor-
nadic circulations using the mesocyclone detection and near storm environment algorithms, in
Preprints,21st Int l Conf. on Information Processing Systems, (San Diego), p. J5.2, Amer. Meteor.
Soc., Jan 2005.
V. Lakshmanan, K. Hondl, D. MacGorman, and G. Stumpf, Preprints, the use of lightning map-
ping array data in WDSS-II, in 22nd Conf. on Severe Local Storms, (Hyannis, MA), p. P14.3,
Amer. Meteor. Soc., 2004.
V. Lakshmanan and M. Valente, Quality control of radar re ectivity data using satellite data and
surface observations, in 20th Int l Conf. on Inter. Inf. Proc. Sys. (IIPS) for Meteor., Ocean., and
Hydr., (Seattle), p. 12.2, Amer. Meteor. Soc., Jan 2004.
V. Lakshmanan, K. Hondl, G. Stumpf, and T. Smith, Quality control of WSR-88D data, in 31st
Radar Conf., (Seattle), pp. 522 525, Amer. Meteor. Soc., Aug 2003.
V. Lakshmanan, Motion estimator based on hierarchical clusters, in 19th Int l Conf. on Inter. Inf.
Proc. Sys. (IIPS) for Meteor., Ocean., and Hydr., (Long Beach, CA), Amer. Meteor. Soc., Feb.
2003.
V. Lakshmanan, An extensible, multi-source meteorological algorithm development interface, in
21st Conf. on Severe Local Storms, (San Antonio, TX), Amer. Meteor. Soc., 2002.
V. Lakshmanan, Statistical clustering for hierarchical storm identi cation, in 21st Conf. on Severe
Local Storms, (San Antonio, TX), Amer. Meteor. Soc., 2002.
R. Lynn and V. Lakshmanan, Virtual radar volumes: Creation, algorithm access and visualization,
in 21st Conf. on Severe Local Storms, (San Antonio, TX), Amer. Meteor. Soc., 2002.
V. Lakshmanan, Real-time merging of multisource data, in 21st Conf. on Severe Local Storms,
(San Antonio, TX), Amer. Meteor. Soc., 2002.
V. Lakshmanan, Real-time merging of multi-source data, in 19th Int l Conf. on Inter. Inf. Proc.
Sys. (IIPS) for Meteor., Ocean., and Hydr., (Long Beach, CA), Amer. Meteor. Soc., Feb. 2003.
V. Lakshmanan, R. Rabin, and V. DeBrunner, Multiscale storm identi cation and forecast, J.
Atm. Res., vol. 67, pp. 367 380, July 2003.
V. Lakshmanan, Lossless coding and compression of radar re ectivity data, in 30th International
Conf. on Radar Meteorology, (Munich), pp. 50 52, American Meteorological Society, July 2001.
V. Lakshmanan, R. Rabin, and V. DeBrunner, Segmenting radar re ectivity data using texture, in
30th International Conf. on Radar Meteorology, (Munich), pp. 50 52, American Meteorological
Society, July 2001.
V. Lakshmanan, R. Rabin, and V. DeBrunner, Identifying and tracking storms in satellite images,
in Second Arti cial Intelligence Conf., (Long Beach, CA), pp. 90 95, American Meteorological
Society, 2000.
V. Lakshmanan and A. Witt, Detecting bounded weak echo regions, in 28th International Conf.
on Radar Meteorology, (Austin, TX), American Meteorological Society, 1997.
Non-refereed presentations as second author and higher omitted.
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