Changes in the seasonality of tornado and favorable genesis conditions in the central United States

Geophysical Research Letters

Volumn 42, Issue 10, 2015, Pages 4224-4231

  Lu, Mengqian ^a,b   Tippett, Michael ^a,c   Lall, Upmanu ^a,b,d  

^a Department of Earth and Environmental Sciences   (United States)

^b Center for International Forestry Research   (United States)

^c KING ABDULAZIZ UNIVERSITY   (Saudi Arabia)

^d Earth Institute   (United States)

Author keywords

genesis conditions; North America; spatial Poisson process; tornado seasonality; wavelets analysis

Indexed keywords

POTENTIAL ENERGY;

GENESIS CONDITIONS; NORTH AMERICA; SEASONALITY; SPATIAL POISSON PROCESS; WAVELETS ANALYSIS;

TORNADOES;

DIAGENESIS; ENVIRONMENTAL CONDITIONS; POISSON RATIO; POTENTIAL ENERGY; SEASONAL VARIATION; SEASONALITY; STORM SURGE; TORNADO; WAVELET ANALYSIS;

UNITED STATES;

EID: 84931574203     PISSN: 00948276     EISSN: 19448007     Source Type: Journal    
DOI: 10.1002/2015GL063968     Document Type: Article

References (44)

1. Ashley, W. S., (2007), Spatial and temporal analysis of tornado fatalities in the United States: 1880-2005, Weather Forecasting, 22 (6), 1214-1228, doi: 10.1175/2007WAF2007004.1.
2. Boruff, B. J., J. A. Easoz, S. D. Jones, H. R. Landry, J. D. Mitchem, and, S. L. Cutter, (2003), Tornado hazards in the United States, Clim. Res., 24 (2), 103-117.
3. Brooks, H. E., and, C. A. Doswell III, (2001a), Normalized damage from major tornadoes in the United States: 1890-1999, Weather Forecasting, 16 (1), 168-176, doi: 10.1175/1520-0434(2001)016<0168:NDFMTI>2.0.CO;2.
4. Brooks, H. E., and, C. A. Doswell III, (2001b), Some aspects of the international climatology of tornadoes by damage classification, Atmos. Res., 56 (1-4), 191-201, doi: 10.1016/S0169-8095(00)00098-3.
5. Brooks, H. E., C. A. Doswell, and, J. Cooper, (1994), On the environments of tornadic and nontornadic mesocyclones, Weather Forecasting, 9 (4), 606-618, doi: 10.1175/1520-0434(1994)009<0606:OTEOTA>2.0.CO;2.
6. Brooks, H. E., C. A. Doswell, and, M. P. Kay, (2003a), climatological estimates of local daily tornado probability for the United States, Weather Forecasting, 18 (4), 626-640, doi: 10.1175/1520-0434(2003)018<0626:CEOLDT>2.0.CO;2.
7. Brooks, H. E., J. W. Lee, and, J. P. Craven, (2003b), The spatial distribution of severe thunderstorm and tornado environments from global reanalysis data, Atmos. Res., 67-68, 73-94, doi: 10.1016/S0169-8095(03)00045-0.
8. Brooks, H. E., G. W. Carbin, and, P. T. Marsh, (2014), Increased variability of tornado occurrence in the United States, Science, 346 (6207), 349-352, doi: 10.1126/science.1257460.
9. Coleman, T. A., and, P. G. Dixon, (2014), An objective analysis of tornado risk in the United States, Weather Forecasting, 29 (2), 366-376, doi: 10.1175/WAF-D-13-00057.1.
10. Cristianini, N., and, J. Shawe-Taylor, (2000), An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods, Cambridge Univ. Press, Cambridge.
11. Daley, D. J., and, D. Vere-Jones, (2007), Spatial point processes, in An Introduction to the Theory of Point Processes: Volume II: General Theory and Structure, pp. 485-505, Springer, New York.
12. Daubechies, I., (1990), The wavelet transform, time-frequency localization and signal analysis, IEEE Trans. Inf. Theory, 36 (5), 961-1005.
13. Diffenbaugh, N. S., M. Scherer, and, R. J. Trapp, (2013), Robust increases in severe thunderstorm environments in response to greenhouse forcing, Proc. Natl. Acad. Sci. U.S.A., 110 (41), 16,361-16,366, doi: 10.1073/pnas.1307758110.
14. Dixon, P. G., A. E. Mercer, K. Grala, and, W. H. Cooke, (2014), objective identification of tornado seasons and ideal spatial smoothing radii, Earth Interact., 18, 1-15, doi: 10.1175/2013EI000559.1.
15. Donoho, D. L., and, I. M. Johnstone, (1994), Ideal spatial adaptation by wavelet shrinkage, Biometrika, 81 (3), 425-455, doi: 10.2307/2337118.
16. Dotzek, N., and, H. E. Brooks, (2008), The Spatial Distribution of Severe Convective Storms and an Analysis of their Secular Changes, Cambridge Univ. Press, Cambridge.
17. Dwyer, J. G., M. Biasutti, and, A. H. Sobel, (2012), Projected changes in the seasonal cycle of surface temperature, J. Clim., 25 (18), 6359-6374, doi: 10.1175/JCLI-D-11-00741.1.
18. Elsner, J. B., S. C. Elsner, and, T. H. Jagger, (2014), The increasing efficiency of tornado days in the United States, Clim. Dyn., doi: 10.1007/s00382-014-2277-3.
19. Farney, T. J., and, P. G. Dixon, (2014), Variability of tornado climatology across the continental United States, Int. J. Climatol., doi: 10.1002/joc.4188.
20. Gensini, V. A., and, W. S. Ashley, (2011), Climatology of potentially severe convective environments from the North American Regional Reanalysis, Electron. J. Severe Storms Meteorol., 6 (8), 1-40.
21. Guyer, J. L., D. A. Imy, and, A. Kis, (2006), Cool season significant (F2-F5) tornadoes in the Gulf Coast states, in Preprints, 23rd Conf. on Severe Local Storms, Am. Meteorol. Soc. 4.2., St. Louis, Mo.
22. Kelly, D. L., J. T. Schaefer, R. P. McNulty, C. A. Doswell, and, R. F. Abbey, (1978), An augmented tornado climatology, Mon. Weather Rev., 106 (8), 1172-1183, doi: 10.1175/1520-0493(1978)106<1172:AATC>2.0.CO;2.
23. Kis, A. K., and, J. M. Straka, (2010), Nocturnal tornado climatology, Weather Forecasting, 25 (2), 545-561, doi: 10.1175/2009WAF2222294.1.
24. Long, J. A., and, P. C. Stoy, (2014), Peak tornado activity is occurring earlier in the heart of "Tornado Alley", Geophys. Res. Lett., 41, 6259-6264, doi: 10.1002/2014GL061385.
25. McMillen, D., (2013), McSpatial: Nonparametric spatial data analysis, R Packag, version, 2.
26. Mercer, A. E., C. M. Shafer, C. A. Doswell, L. M. Leslie, and, M. B. Richman, (2009), Objective classification of tornadic and nontornadic severe weather outbreaks, Mon. Weather Rev., 137 (12), 4355-4368, doi: 10.1175/2009MWR2897.1.
27. Mesinger, F., G. DiMego, E. Kalnay, K. Mitchell, P. C. Shafran, W. Ebisuzaki, D. Jovic, J. Woollen, E. Rogers, and, E. H. Berbery, (2006), North American regional reanalysis, Bull. Am. Meteorol. Soc., 87 (3), 343-360, doi: 10.1175/BAMS-87-3-343.
28. Muller, K., S. Mika, G. Ratsch, K. Tsuda, and, B. Scholkopf, (2001), An introduction to kernel-based learning algorithms, IEEE Trans. Neural Networks, 12 (2), 181-201, doi: 10.1109/72.914517.
29. Rasmussen, E. N., and, D. O. Blanchard, (1998), A baseline climatology of sounding-derived supercell and tornado forecast parameters, Weather Forecasting, 13 (4), 1148-1164, doi: 10.1175/1520-0434(1998)013<1148:ABCOSD>2.0.CO;2.
30. Robinson, E. D., R. J. Trapp, and, M. E. Baldwin, (2013), The geospatial and temporal distributions of severe thunderstorms from high-resolution dynamical downscaling, J. Appl. Meteorol. Climatol., 52 (9), 2147-2161, doi: 10.1175/JAMC-D-12-0131.1.
31. Sander, J., J. F. Eichner, E. Faust, and, M. Steuer, (2013), Rising variability in thunderstorm-related U.S. losses as a reflection of changes in large-scale thunderstorm forcing, Weather Clim. Soc., 5 (4), 317-331, doi: 10.1175/WCAS-D-12-00023.1.
32. Schaefer, J. T., and, R. Edwards, (1999), The SPC tornado/severe thunderstorm database, in Preprints,11th Conf. on Applied Climatology, vol. 6, pp. 603-606, Am. Meteorol. Soc., Dallas, Tex.
33. Schaefer, J. T., D. L. Kelly, and, R. F. Abbey, (1986), A Minimum assumption tornado-hazard probability model, J. Clim. Appl. Meteorol., 25 (12), 1934-1945, doi: 10.1175/1520-0450(1986)025<1934:AMATHP>2.0.CO;2.
34. Scholkopf, B., S. Mika, C. J. C. Burges, P. Knirsch, K. Muller, G. Ratsch, and, A. J. Smola, (1999), Input space versus feature space in kernel-based methods, IEEE Trans. Neural Networks, 10 (5), 1000-1017, doi: 10.1109/72.788641.
35. Shafer, C. M., A. E. Mercer, C. A. Doswell, M. B. Richman, and, L. M. Leslie, (2009), Evaluation of WRF forecasts of tornadic and nontornadic outbreaks when initialized with synoptic-scale input, Mon. Weather Rev., 137 (4), 1250-1271, doi: 10.1175/2008MWR2597.1.
36. Standohar-Alfano, C. D., and, J. W. van de Lindt, (2014), Empirically based probabilistic tornado hazard analysis of the United States using 1973-2011 data, Nat. Hazards Rev., 16 (1), 04014013, doi: 10.1061/(ASCE)NH.1527-6996.0000138.
37. Thom, H. C. S., (1963), Tornado probabilities, Mon. Weather Rev., 91 (10), 730-736, doi: 10.1175/1520-0493(1963)091<0730:TP>2.3.CO;2.
38. Tippett, M. K., (2014), Changing volatility of U.S. annual tornado reports, Geophys. Res. Lett., 41, 6956-6961, doi: 10.1002/2014GL061347.
39. Tippett, M. K., A. H. Sobel, and, S. J. Camargo, (2012), Association of U.S. tornado occurrence with monthly environmental parameters, Geophys. Res. Lett., 39, L02801, doi: 10.1029/2011GL050368.
40. Tippett, M. K., A. H. Sobel, S. J. Camargo, and, J. T. Allen, (2014), An empirical relation between U.S. tornado activity and monthly environmental parameters, J. Clim., 27 (8), 2983-2999, doi: 10.1175/JCLI-D-13-00345.1.
41. Tippett, M. K., J. T. Allen, V. A. Gensini, and, H. E. Brooks, (2015), Climate and hazardous convective weather, Curr. Clim. Change Rep., 1, 60-73, doi: 10.1007/s40641-015-0006-6.
42. Torrence, C., and, G. P. Compo, (1998), A practical guide to wavelet analysis, Bull. Am. Meteorol. Soc., 79 (1), 61-78, doi: 10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2.
43. Verbout, S. M., H. E. Brooks, L. M. Leslie, and, D. M. Schultz, (2006), Evolution of the U.S. tornado database: 1954-2003, Weather Forecasting, 21, 86-93, doi: 10.1175/WAF910.1.
44. Wand, P., and, C. Jones, (1994), Kernel Smoothing, Chapman & Hall/CRC, London.