Thunderstorm Observation by Radar (ThOR): An Algorithm to Develop a Climatology of Thunderstorms

Adam L. Houston * Department of Earth and Atmospheric Sciences, University of Nebraska–Lincoln, Lincoln, Nebraska

Search for other papers by Adam L. Houston in
Current site
Google Scholar
PubMed
Close
,
Noah A. Lock Weather Decision Technologies, Norman, Oklahoma

Search for other papers by Noah A. Lock in
Current site
Google Scholar
PubMed
Close
,
Jamie Lahowetz High Plains Regional Climate Center, Lincoln, Nebraska

Search for other papers by Jamie Lahowetz in
Current site
Google Scholar
PubMed
Close
,
Brian L. Barjenbruch National Weather Service, Topeka, Kansas

Search for other papers by Brian L. Barjenbruch in
Current site
Google Scholar
PubMed
Close
,
George Limpert * Department of Earth and Atmospheric Sciences, University of Nebraska–Lincoln, Lincoln, Nebraska

Search for other papers by George Limpert in
Current site
Google Scholar
PubMed
Close
, and
Cody Oppermann Utah Department of Transportation, Salt Lake City, Utah

Search for other papers by Cody Oppermann in
Current site
Google Scholar
PubMed
Close
Restricted access

We are aware of a technical issue preventing figures and tables from showing in some newly published articles in the full-text HTML view.
While we are resolving the problem, please use the online PDF version of these articles to view figures and tables.

Abstract

The Thunderstorm Observation by Radar (ThOR) algorithm is an objective and tunable Lagrangian approach to cataloging thunderstorms. ThOR uses observations from multiple sensors (principally multisite surveillance radar data and cloud-to-ground lightning) along with established techniques for fusing multisite radar data and identifying spatially coherent regions of radar reflectivity (clusters) that are subsequently tracked using a new tracking scheme. The main innovation of the tracking algorithm is that, by operating offline, the full data record is available, not just previous cluster positions, so all possible combinations of object sequences can be developed using all observed object positions. In contrast to Eulerian methods reliant on thunder reports, ThOR is capable of cataloging nearly every thunderstorm that occurs over regional-scale and continental United States (CONUS)-scale domains, thereby enabling analysis of internal properties and trends of thunderstorms.

ThOR is verified against 166 manually analyzed cluster tracks and is also verified using descriptive statistics applied to a large (~35 000 tracks) sample. Verification also relied on a benchmark tracking algorithm that provides context for the verification statistics. ThOR tracks are shown to match the manual tracks slightly better than the benchmark tracks. Moreover, the descriptive statistics of the ThOR tracks are nearly identical to those of the manual tracks, suggesting good agreement. When the descriptive statistics were applied to the ~35 000-track dataset, ThOR tracking produces longer (statistically significant), straighter, and more coherent tracks than those of the benchmark algorithm. Qualitative assessment of ThOR performance is enabled through application to a multiday thunderstorm event and comparison to the behavior of the Storm Cell Identification and Tracking (SCIT) algorithm.

Corresponding author address: Dr. Adam L. Houston, Department of Earth and Atmospheric Sciences, University of Nebraska–Lincoln, 214 Bessey Hall, Lincoln, NE 68588. E-mail: ahouston2@unl.edu

Abstract

The Thunderstorm Observation by Radar (ThOR) algorithm is an objective and tunable Lagrangian approach to cataloging thunderstorms. ThOR uses observations from multiple sensors (principally multisite surveillance radar data and cloud-to-ground lightning) along with established techniques for fusing multisite radar data and identifying spatially coherent regions of radar reflectivity (clusters) that are subsequently tracked using a new tracking scheme. The main innovation of the tracking algorithm is that, by operating offline, the full data record is available, not just previous cluster positions, so all possible combinations of object sequences can be developed using all observed object positions. In contrast to Eulerian methods reliant on thunder reports, ThOR is capable of cataloging nearly every thunderstorm that occurs over regional-scale and continental United States (CONUS)-scale domains, thereby enabling analysis of internal properties and trends of thunderstorms.

ThOR is verified against 166 manually analyzed cluster tracks and is also verified using descriptive statistics applied to a large (~35 000 tracks) sample. Verification also relied on a benchmark tracking algorithm that provides context for the verification statistics. ThOR tracks are shown to match the manual tracks slightly better than the benchmark tracks. Moreover, the descriptive statistics of the ThOR tracks are nearly identical to those of the manual tracks, suggesting good agreement. When the descriptive statistics were applied to the ~35 000-track dataset, ThOR tracking produces longer (statistically significant), straighter, and more coherent tracks than those of the benchmark algorithm. Qualitative assessment of ThOR performance is enabled through application to a multiday thunderstorm event and comparison to the behavior of the Storm Cell Identification and Tracking (SCIT) algorithm.

Corresponding author address: Dr. Adam L. Houston, Department of Earth and Atmospheric Sciences, University of Nebraska–Lincoln, 214 Bessey Hall, Lincoln, NE 68588. E-mail: ahouston2@unl.edu
Save
  • Alexander, W. H., 1915: Distribution of thunderstorms in the United States. Mon. Wea. Rev., 43, 322–340, doi:10.1175/1520-0493(1915)43<322:DOTITU>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Alexander, W. H., 1935: The distribution of thunderstorms in the United States, 1904–33. Mon. Wea. Rev., 63, 157–158, doi:10.1175/1520-0493(1935)63<157:TDOTIT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • American Meteorological Society, 2013: Thunderstorm. Glossary of Meteorology. [Available online at http://glossary.ametsoc.org/wiki/Thunderstorm.]

  • Biagi, C. J., Cummins K. L. , Kehoe K. E. , and Krider E. P. , 2007: National Lightning Detection Network (NLDN) performance in southern Arizona, Texas, and Oklahoma in 2003–2004. J. Geophys. Res., 112, D05208, doi:10.1029/2006JD007341.

    • Search Google Scholar
    • Export Citation
  • Biggerstaff, M. I., and Listemaa S. A. , 2000: An improved scheme for convective/stratiform echo classification using radar reflectivity. J. Appl. Meteor., 39, 2129–2150, doi:10.1175/1520-0450(2001)040<2129:AISFCS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Changnon, S. A., 1985: Secular variations in thunder-day frequencies in the twentieth century. J. Geophys. Res., 90, 6181–6194, doi:10.1029/JD090iD04p06181.

    • Search Google Scholar
    • Export Citation
  • Changnon, S. A., 1988a: Climatography of thunder events in the conterminous United States. Part I: Temporal aspects. J. Climate, 1, 389–398, doi:10.1175/1520-0442(1988)001<0389:COTEIT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Changnon, S. A., 1988b: Climatography of thunder events in the conterminous United States. Part II: Spatial aspects. J. Climate, 1, 399–405, doi:10.1175/1520-0442(1988)001<0399:COTEIT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Clements, N. C., and Orville R. E. , 2008: The warning time for cloud-to-ground lightning in isolated, ordinary thunderstorms over Houston, Texas. Third Conf. on Meteorological Applications of Lightning Data, New Orleans, LA, Amer. Meteor. Soc., 6.4. [Available online at https://ams.confex.com/ams/88Annual/techprogram/paper_132309.htm.]

  • Cotton, W. R., and Anthes R. A. , 1989: Storm and Cloud Dynamics. International Geophysics Series, Vol. 44, Academic Press, 883 pp.

  • Court, A., and Griffiths J. F. , 1981: Thunderstorm climatology. Thunderstorm Morphology and Dynamics, University of Oklahoma Press, 9–39.

  • Davini, P., Bechini R. , Cremonini R. , and Cassardo C. , 2012: Radar-based analysis of convective storms over northwestern Italy. Atmosphere, 3, 33–58, doi:10.3390/atmos3010033.

    • Search Google Scholar
    • Export Citation
  • Dixon, M., and Wiener G. , 1993: TITAN: Thunderstorm Identification, Tracking, Analysis, and Nowcasting—A radar-based methodology. J. Atmos. Oceanic Technol., 10, 785–797, doi:10.1175/1520-0426(1993)010<0785:TTITAA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Duda, J. D., and Gallus W. A. Jr., 2010: Spring and summer midwestern severe weather reports in supercells compared to other morphologies. Wea. Forecasting, 25, 190–206, doi:10.1175/2009WAF2222338.1.

    • Search Google Scholar
    • Export Citation
  • Easterling, D. R., and Robinson P. J. , 1985: The diurnal variation of thunderstorm activity in the United States. J. Climate Appl. Meteor., 24, 1048–1058, doi:10.1175/1520-0450(1985)024<1048:TDVOTA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Falconer, P. D., 1984: A radar-based climatology of thunderstorm days across New York State. J. Climate Appl. Meteor., 23, 1115–1120, doi:10.1175/1520-0450(1984)023<1115:ARBCOT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Gallus, W. A., Snook N. A. , and Johnson E. V. , 2008: Spring and summer severe weather reports over the Midwest as a function of convective mode: A preliminary study. Wea. Forecasting, 23, 101–113, doi:10.1175/2007WAF2006120.1.

    • Search Google Scholar
    • Export Citation
  • Geerts, B., 1998: Mesoscale convective systems in the Southeast United States during 1994–95: A survey. Wea. Forecasting, 13, 860–869, doi:10.1175/1520-0434(1998)013<0860:MCSITS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Goudenhoofdt, E., and Delobbe L. , 2013: Statistical characteristics of convective storms in Belgium derived from volumetric weather radar observations. J. Appl. Meteor. Climatol., 52, 918–934, doi:10.1175/JAMC-D-12-079.1.

    • Search Google Scholar
    • Export Citation
  • Hocker, J. E., and Basara J. B. , 2008: A geographic information systems–based analysis of supercells across Oklahoma from 1994 to 2003. J. Appl. Meteor. Climatol., 47, 1518–1538, doi:10.1175/2007JAMC1673.1.

    • Search Google Scholar
    • Export Citation
  • Houze, R. A., 1993: Cloud Dynamics. Academic Press, 573 pp.

  • Johnson, J. T., MacKeen P. L. , Witt A. , Mitchell E. D. W. , Stumpf G. J. , Eilts M. D. , and Thomas K. W. , 1998: The Storm Cell Identification and Tracking algorithm: An enhanced WSR-88D algorithm. Wea. Forecasting, 13, 263–276, doi:10.1175/1520-0434(1998)013<0263:TSCIAT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kolodziej Hobson, A. G. K., Lakshmanan V. , Smith T. M. , and Richman M. , 2012: An automated technique to categorize storm type from radar and near-storm environment data. Atmos. Res., 111, 104–113, doi:10.1016/j.atmosres.2012.03.004.

    • Search Google Scholar
    • Export Citation
  • Kuo, J.-T., and Orville H. D. , 1973: A radar climatology of summertime convective clouds in the Black Hills. J. Appl. Meteor., 12, 359–368, doi:10.1175/1520-0450(1973)012<0359:ARCOSC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Lakshmanan, V., and Smith T. , 2009: Data mining storm attributes from spatial grids. J. Atmos. Oceanic Technol., 26, 2353–2365, doi:10.1175/2009JTECHA1257.1.

    • Search Google Scholar
    • Export Citation
  • Lakshmanan, V., and Smith T. , 2010: An objective method of evaluating and devising storm-tracking algorithms. Wea. Forecasting, 25, 701–709, doi:10.1175/2009WAF2222330.1.

    • Search Google Scholar
    • Export Citation
  • Lakshmanan, V., DeBrunner V. , and Rabin R. , 2002: Nested partitions using texture segmentation. SSIAI ’02: Proceedings of the Fifth IEEE Southwest Symposium on Image Analysis and Interpretation, IEEE Computer Society, 153–157.

  • Lakshmanan, V., Smith T. , Hondl K. , Stumpf G. J. , and Witt A. , 2006: A real-time, three-dimensional, rapidly updating, heterogeneous radar merger technique for reflectivity, velocity, and derived products. Wea. Forecasting, 21, 802–823, doi:10.1175/WAF942.1.

    • Search Google Scholar
    • Export Citation
  • Lakshmanan, V., Smith T. , Stumpf G. , and Hondl K. , 2007: The Warning Decision Support System–Integrated Information. Wea. Forecasting, 22, 596–612, doi:10.1175/WAF1009.1.

    • Search Google Scholar
    • Export Citation
  • Lakshmanan, V., Hondl K. , and Rabin R. , 2009: An efficient, general-purpose technique for identifying storm cells in geospatial images. J. Atmos. Oceanic Technol., 26, 523–537, doi:10.1175/2008JTECHA1153.1.

    • Search Google Scholar
    • Export Citation
  • López, R. E., Blanchard D. O. , Rosenfeld D. , Hiscox W. L. , and Casey M. J. , 1984: Population characteristics, development processes and structure of radar echoes in south Florida. Mon. Wea. Rev., 112, 56–75, doi:10.1175/1520-0493(1984)112<0056:PCDPAS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • MacGorman, D. R., Apostolakopoulos I. R. , Lund N. R. , Demetriades N. W. S. , Murphy M. J. , and Krehbiel P. R. , 2011: The timing of cloud-to-ground lightning relative to total lightning activity. Mon. Wea. Rev., 139, 3871–3886, doi:10.1175/MWR-D-11-00047.1.

    • Search Google Scholar
    • Export Citation
  • MacKeen, P. L., Brooks H. E. , and Elmore K. L. , 1999: Radar reflectivity–derived thunderstorm parameters applied to storm longevity forecasting. Wea. Forecasting, 14, 289–295, doi:10.1175/1520-0434(1999)014<0289:RRDTPA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • May, P. T., and Ballinger A. , 2007: The statistical characteristics of convective cells in a monsoon regime (Darwin, northern Australia). Mon. Wea. Rev., 135, 82–92, doi:10.1175/MWR3273.1.

    • Search Google Scholar
    • Export Citation
  • Mesinger, F., and Coauthors, 2006: North American Regional Reanalysis. Bull. Amer. Meteor. Soc., 87, 343–360, doi:10.1175/BAMS-87-3-343.

    • Search Google Scholar
    • Export Citation
  • Michaels, P. J., Pielke R. A. , McQueen J. T. , and Sappington D. E. , 1987: Composite climatology of Florida summer thunderstorms. Mon. Wea. Rev., 115, 2781–2791, doi:10.1175/1520-0493(1987)115<2781:CCOFST>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Mohee, F. M., and Miller C. , 2010: Climatology of thunderstorms for North Dakota, 2002–06. J. Appl. Meteor. Climatol., 49, 1881–1890, doi:10.1175/2010JAMC2400.1.

    • Search Google Scholar
    • Export Citation
  • Mosier, R. M., Schumacher C. , Orville R. E. , and Carey L. D. , 2011: Radar nowcasting of cloud-to-ground lightning over Houston, Texas. Wea. Forecasting, 26, 199–212, doi:10.1175/2010WAF2222431.1.

    • Search Google Scholar
    • Export Citation
  • NOAA, 2013: Natural hazard statistics. Accessed 25 October 2014. [Available online at http://www.nws.noaa.gov/om/hazstats.shtml.]

  • Parker, M. D., and Johnson R. H. , 2000: Organizational modes of midlatitude mesoscale convective systems. Mon. Wea. Rev., 128, 3413–3436, doi:10.1175/1520-0493(2001)129<3413:OMOMMC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Potts, R. J., Keenan T. D. , and May P. T. , 2000: Radar characteristics of storms in the Sydney area. Mon. Wea. Rev., 128, 3308–3319, doi:10.1175/1520-0493(2000)128<3308:RCOSIT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Rakov, V. A., 2013: Electromagnetic methods of lightning detection. Surv. Geophys., 34, 731–753, doi:10.1007/s10712-013-9251-1.

  • Reap, R. M., and Foster D. S. , 1979: Automated 12–36 hour probability forecasts of thunderstorms and severe local storms. J. Appl. Meteor., 18, 1304–1315, doi:10.1175/1520-0450(1979)018<1304:AHPFOT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Reed, J., and Trostel J. , 2012: Evaluation of an improved Storm Cell Identification and Tracking (SCIT) algorithm based on DBSCAN clustering and JPDA tracking methods. 28th Conf. on Interactive Information Processing Systems (IIPS), New Orleans, LA, Amer. Meteor. Soc., 4B.3. [Available online at https://ams.confex.com/ams/92Annual/webprogram/Paper201783.html.]

  • Root, B., Yeary M. B. , and Yu T. Y. , 2011: Novel storm cell tracking with multiple hypothesis tracking. 27th Conf. on Interactive Information Processing Systems (IIPS), Seattle, WA, Amer. Meteor. Soc., 8B.3. [Available online at https://ams.confex.com/ams/91Annual/webprogram/Paper184250.html.]

  • Scharenbroich, L., Magnusdottir G. , Smyth P. , Stern H. , and Wang C.-c. , 2010: A Bayesian framework for storm tracking using a hidden-state representation. Mon. Wea. Rev., 138, 2132–2148, doi:10.1175/2009MWR2944.1.

    • Search Google Scholar
    • Export Citation
  • Seroka, G. N., Orville R. E. , and Schumacher C. , 2012: Radar nowcasting of total lightning over the Kennedy Space Center. Wea. Forecasting, 27, 189–204, doi:10.1175/WAF-D-11-00035.1.

    • Search Google Scholar
    • Export Citation
  • Smith, B. T., Thompson R. L. , Grams J. S. , Broyles C. , and Brooks H. E. , 2012: Convective modes for significant severe thunderstorms in the contiguous United States. Part I: Storm classification and climatology. Wea. Forecasting, 27, 1114–1135, doi:10.1175/WAF-D-11-00115.1.

    • Search Google Scholar
    • Export Citation
  • Steiner, M., Houze R. A. , and Yuter S. E. , 1995: Climatological characterization of three-dimensional storm structure from operational radar and rain gauge data. J. Appl. Meteor., 34, 1978–2007, doi:10.1175/1520-0450(1995)034<1978:CCOTDS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Stumpf, G. J., Witt A. , Mitchell E. D. , Spencer P. L. , Johnson J. T. , Eilts M. D. , Thomas K. W. , and Burgess D. W. , 1998: The National Severe Storms Laboratory mesocyclone detection algorithm for the WSR-88D. Wea. Forecasting, 13, 304–326, doi:10.1175/1520-0434(1998)013<0304:TNSSLM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wallace, J. M., 1975: Diurnal variations in precipitation and thunderstorm frequency over the conterminous United States. Mon. Wea. Rev., 103, 406–419, doi:10.1175/1520-0493(1975)103<0406:DVIPAT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wiggert, V., Lockett G. J. , and Ostlund S. S. , 1981: Radar rainshower growth histories and variations with wind speed, echo motion, location and merger status. Mon. Wea. Rev., 109, 1467–1494, doi:10.1175/1520-0493(1981)109<1467:RRGHAV>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2011: Statistical Methods in the Atmospheric Sciences. 3rd ed. Elsevier, 676 pp.

  • Zipser, E. J., and Lutz K. R. , 1994: The vertical profile of radar reflectivity of convective cells: A strong indicator of storm intensity and lightning probability? Mon. Wea. Rev., 122, 1751–1759, doi:10.1175/1520-0493(1994)122<1751:TVPORR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1399 668 198
PDF Downloads 770 185 7