Tornado pathlength forecasts from 2010 to 2011 using ensemble updraft helicity

Weather and Forecasting

Volumn 28, Issue 2, 2013, Pages 387-407

  Clark, Adam J ^a,b   Gao, Jidong ^b   Marsh, Patrick T ^a,b   Smith, Travis ^a,b   Kain, John S ^b   Correia, James ^a,c   Xue, Ming ^a   Kong, Fanyou ^a  

^a UNIVERSITY OF OKLAHOMA   (United States)

^b NATIONAL SEVERE STORMS LABORATORY   (United States)

^c NOAA EARTH SYSTEM RESEARCH LABORATORY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

TORNADOES; WEATHER FORECASTING;

ENSEMBLE FORECAST SYSTEMS; GRID-SPACINGS; HELICITIES; IDENTIFICATION ALGORITHMS; OBJECT IDENTIFICATION; PATHLENGTHS; RECENT RESEARCHES; THREE-DIMENSIONAL OBJECT; THREE-DIMENSIONAL VARIATIONAL DATA-ASSIMILATION; TORNADO OUTBREAK;

STORMS;

ALGORITHM; ENSEMBLE FORECASTING; PREDICTION; STORM TRACK; THREE-DIMENSIONAL MODELING; TORNADO; UPDRAFT; WEATHER FORECASTING;

EID: 84878177011     PISSN: 08828156     EISSN: 15200434     Source Type: Journal    
DOI: 10.1175/WAF-D-12-00038.1     Document Type: Article

References (58)

1. Chen, F., and J. Dudhia, 2001: Coupling an advanced land-surface-hydrology model with the Penn State-NCAR MM5 modeling system. Part I: Model description and implementation. Mon. Wea. Rev., 129, 569-585.
2. Chou, M.-D., and M. J. Suarez, 1994: An efficient thermal infrared radiation parameterization for use in general circulation models. NASA Tech. Memo. 104606, Vol. 3, 85 pp.
3. Clark,A. J., and Coauthors, 2011: Probabilistic precipitation forecast skill as a function of ensemble size and spatial scale in a convection- allowing ensemble. Mon. Wea. Rev., 139, 1410-1418.
4. Clark,A. J., and Coauthors, 2012a: An overview of the 2010 Hazardous Weather Testbed Experimental Forecast Program Spring Experiment. Bull. Amer. Meteor. Soc., 93, 55-74.
5. Clark,A. J., J. S. Kain, P. T. Marsh, J. Correia Jr., M. Xue, and F. Kong, 2012b: Forecasting tornado pathlengths using a three-dimensional object identification algorithm applied to convection-allowing forecasts. Wea. Forecasting, 27, 1090-1113.
6. Coffer, B. E., L. C. Maudlin, P. G. Veals, and A. J. Clark, 2013: Dryline position errors in experimental convection-allowing NSSL-WRF model forecasts and the operational NAM. Wea. Forecasting, in press.
7. Doswell, C. A., III, G. Carbin, and H. E. Brooks, 2012: The tornadoes of spring 2011 in the USA: An historical perspective. Weather, 67, 88-94.
8. Du, J., J. McQueen, G. DiMego, Z. Toth, D. Jovic, B. Zhou, and H.-Y. Chuang, 2006: New dimension of NCEP Short-Range Ensemble Forecasting (SREF) system: Inclusion of WRF members. WMO Expert Team Meeting on Ensemble Prediction Systems, Exeter, United Kingdom, WMO, 5 pp. [Available online at http://www.emc.ncep.noaa.gov/mmb/SREF/ WMO06_full.pdf.]
9. Ferrier, B. S., Y. Jin, Y. Lin, T. Black, E. Rogers, and G. DiMego, 2002: Implementation of a new grid-scale cloud and rainfall scheme in the NCEP Eta Model. Preprints, 15th Conf. on Numerical Weather Prediction, San Antonio, TX, Amer. Meteor. Soc., 10.1. [Available online at https://ams.confex.com/ams/SLS_WAF_NWP/techprogram/paper_47241.htm.]
10. Gallus, W. A., Jr., N. A. Snook, and E. V. Johnson, 2008: Spring and summer severe weather reports over the Midwest as a function of convective mode: A preliminary study. Wea. Forecasting, 23, 101-113.
11. Gao, J., M. Xue, K. Brewster, and K. K. Droegemeier, 2004: A three-dimensional variational data analysis method withrecursive filter for Doppler radars. J. Atmos. Oceanic Technol., 21, 457-469.
12. Gao, J., D. J. Stensrud, and M. Xue, 2009: Three-dimensional analyses of several thunderstorms observed during VORTEX2 field operations. Preprints, 34th Conf. on Radar Meteorology, Williamsburg, VA, Amer. Meteor. Soc., P9.8. [Available online at https://ams.confex.com/ams/34Radar/webprogram/Paper156114.html.]
13. Gao, J., D. J. Stensrud, T. Smith, K. Manross, J. Brogden, and K. Kuhlman, 2011: A realtime weather-adaptive 3DVAR analysis system with automatic storm positioning and on-demand capability. Preprints, 35th Conf. on Radar Meteorology, Pittsburgh, PA, Amer. Meteor. Soc., 115. [Available online at https://ams.confex.com/ams/35Radar/webprogram/Paper191834.html.]
14. Gao, J., and Coauthors, 2013: A real-time weather-adaptive 3DVAR analysis system for severe weather detections and warnings. Wea. Forecasting, in press.
15. Grams, J. S., R. L. Thompson, D. V. Snively, J. A. Prentice, G. M. Hodges, and L. J. Reames, 2012: A climatology and comparison of parameters for significant tornado events in the United States. Wea. Forecasting, 27, 106-123.
16. Hamill, T. M., and S. J. Colucci, 1997: Verification of Eta-RSM short-range ensemble forecasts. Mon. Wea. Rev., 125, 1312-1327.
17. Hamill, T. M., and S. J. Colucci, 1998: Evaluation of Eta-RSM ensemble probabilistic precipitation forecasts. Mon. Wea. Rev., 126, 711-724.
18. Hong, S.-Y., and J.-O. J. Lim, 2006: The WRF single-moment 6-class microphysics scheme (WSM6). J. Korean Meteor. Soc., 42, 129-151.
19. Hu, M., M. Xue, and K. Brewster, 2006:3DVARand cloud analysis with WSR-88D level-II data for the prediction of Fort Worth tornadic thunderstorms. Part I: Cloud analysis and its impact. Mon. Wea. Rev., 134, 675-698.
20. Janjić, Z. I., 2002: Nonsingular implementation of the Mellor- Yamada level 2.5 scheme in the NCEP Meso model. NCEP Office Note 437, NOAA/NWS, 61 pp.
21. Kain, J. S., and Coauthors, 2008: Some practical considerations regarding horizontal resolution in the first generation of operational convection-allowing NWP. Wea. Forecasting, 5, 931-952.
22. Kain, J. S., S. R. Dembek, S. J. Weiss, J. L. Case, J. J. Levit, and R. A. Sobash, 2010: Extracting unique information from highresolution forecast models: Monitoring selected fields and phenomena every time step. Wea. Forecasting, 25, 1536-1542.
23. Kong, F., and Coauthors, 2011: CAPS storm-scale ensemble forecast in HWT 2011 Spring Experiment: Sensitivity of WRF physics on QPF. Extended Abstracts, 12th Users' Workshop, Boulder, CO, NCAR. [Available online at http://www.mmm.ucar.edu/wrf/users/workshops/WS2011/Power%20Points%202011/6_3_Kong_WRFworkshop_11.pdf.]
24. Lakshmanan, V., 2002: WDSSII: An extensible, multi-source meteorological algorithm development interface. Preprints, 21st Conf. on Severe Local Storms, San Antonio, TX, Amer. Meteor. Soc., 134-137.
25. Lim, K.-S. S., and S.-Y. Hong, 2010: Development of an effective double-moment cloud microphysics scheme with prognostic cloud condensation nuclei (CCN) for weather and climate models. Mon. Wea. Rev., 138, 1587-1612.
26. Mason, I., 1982:Amodel for assessment of weather forecasts. Aust. Meteor. Mag., 30, 291-303.
27. Mellor, G. L., and T. Yamada, 1982: Development of a turbulence closure model for geophysical fluid problems. Rev. Geophys., 20, 851-875.
28. Milbrandt, J. A., and M. K. Yau, 2005: A multimoment bulk microphysics parameterization. Part I: Analysis of the role of the spectral shape parameter. J. Atmos. Sci., 62, 3051-3064.
29. Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102 (D14), 16 663-16 682.
30. Morrison, H., J. A. Curry, and V. I. Khvorostyanov, 2005: A new double-moment microphysics parameterization for application in cloud and climate models, Part I: Description. J. Atmos. Sci., 62, 1665-1677.
31. Nakanishi, M., 2000: Large-eddy simulation of radiation fog. Bound.-Layer Meteor., 94, 461-493.
32. Nakanishi, M., 2001: Improvement of the Mellor-Yamada turbulence closure model based on large-eddy simulation data. Bound.- Layer Meteor., 99, 349-378.
33. Nakanishi, M., and H. Niino, 2004: An improved Mellor-Yamada level-3 model with condensation physics: Its design and verification. Bound.-Layer Meteor., 112, 1-31.
34. Nakanishi, M., and H. Niino, 2006: An improved Mellor-Yamada level-3 model: Its numerical stability and application to a regional prediction of advection fog. Bound.-Layer Meteor., 119, 397-407.
35. Noh, Y., W. G. Cheon, S.-Y. Hong, and S. Raasch, 2003: Improvement of the K-profile model for the planetary boundary layer based on large eddy simulation data. Bound.-Layer Meteor., 107, 401-427.
36. Pleim, J. E., 2007:Acombined local and nonlocal closure model for the atmospheric boundary layer. Part I: Model description and testing. J. Appl. Meteor. Climatol., 46, 1383-1395.
37. Purser, R. J., W.-S. Wu, D. Parrish, and N. M. Roberts, 2003a: Numerical aspects of the application of recursive filters to variational statistical analysis. Part I: Spatially homogeneous and isotropic Gaussian covariances. Mon. Wea. Rev., 131, 1524-1535.
38. Purser, R. J., W.-S. Wu, D. Parrish, and N. M. Roberts, 2003b: Numerical aspects of the application of recursive filters to variational statistical analysis. Part II: Spatially inhomogeneous and anisotropic general covariances. Mon. Wea. Rev., 131, 1536-1548.
39. R Development Core Team, cited 2012: R: A language and environment for statistical computing. RFoundation for Statistical Computing, Vienna, Austria. [Available online at http://www.R-project.org.]
40. Rogers, E., and Coauthors, 2009: The NCEP North American Mesoscale modeling system: Recent changes and future plans. Preprints, 23rd Conf. on Weather Analysis and Forecasting/ 19th Conf. on Numerical Weather Prediction, Omaha, NE, Amer. Meteor. Soc., 2A.4. [Available online at http://ams.confex.com/ams/pdfpapers/154114.pdf.]
41. Schwartz, C. S., and Coauthors, 2010: Toward improved convectionallowing ensembles: Model physics sensitivities and optimizing probabilistic guidance with small ensemble membership. Wea. Forecasting, 25, 263-280.
42. Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF version 2. NCAR Tech Note NCAR/TN-4751STR, 113 pp. [Available online at http://www.mmm.ucar.edu/wrf/users/docs/arw_v3.pdf.]
43. Smirnova, T. G., J. M. Brown, and S. G. Benjamin, 1997: Performance of different soil model configurations in simulating ground surface temperature and surface fluxes. Mon. Wea. Rev., 125, 1870-1884.
44. Smirnova, T. G., J. M. Brown, S. G. Benjamin, and D. Kim, 2000: Parameterization of coldseason processes in the MAPS land-surface scheme. J. Geophys. Res., 105 (D3), 4077-4086.
45. Smith, T. M., K. M. Kuhlman, K. L. Ortega, K. L. Manross, D. W. Burgess, J. Gao, and D. J. Stensrud, 2010: A survey of realtime 3DVAR analyses conducted during the 2010 Experimental Warning Program Spring Experiment. Preprints, 25th Conf. on Severe Local Storms, Denver, CO, Amer. Meteor. Soc., P5.7. [Available online at http://ams.confex.com/ams/pdfpapers/176211.pdf.]
46. Sukoriansky, S., B. Galperin, and V. Perov, 2006: A quasi-normal scale elimination model of turbulence and its application to stably stratified flows. Nonlinear Processes Geophys., 13, 9-22.
47. Thompson, G., R. M. Rasmussen, and K. Manning, 2004: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part I: Description and sensitivity analysis. Mon. Wea. Rev., 132, 519-542.
48. Thompson, R. L., R. Edwards, J. A. Hart, K. L. Elmore, and P. Markowski, 2003: Close proximity soundings within supercell environments obtained from the Rapid Update Cycle. Wea. Forecasting, 18, 1243-1261.
49. Thompson, R. L., C. M. Mead, and R. Edwards, 2007: Effective storm-relative helicity and bulk shear in supercell thunderstorm environments. Wea. Forecasting, 22, 102-115.
50. Trapp, R. J., G. J. Stumpf, and K. L. Manross, 2005a: A reassessment of the percentage of tornadic mesocyclones. Wea. Forecasting, 20, 680-687.
51. Trapp, R. J., S. A. Tessendorf, E. S. Godfrey, and H. E. Brooks, 2005b: Tornadoes from squall lines and bow echoes. Part I: Climatological distribution. Wea. Forecasting, 20, 23-34.
52. Wandishin, M. S., S. L. Mullen, D. J. Stensrud, and H. E. Brooks, 2001: Evaluation of a short-range multimodel ensemble system. Mon. Wea. Rev., 129, 729-747.
53. Weiss, S. J., and M. D. Vescio, 1998: Severe local storm climatology 1955-1996: Analysis of reporting trends and implications for NWS operations. Preprints, 19th Conf. on Severe Local Storms, Minneapolis, MN, Amer. Meteor. Soc., 536-539.
54. Wilks, D. S., 1995: Statisitcal Methods in the Atmospheric Sciences: An Introduction. Academic Press, 467 pp.
55. Xue, M., K. K. Droegemeier, and V. Wong, 2000: The Advanced Regional Prediction System (ARPS)-A multiscale nonhydrostatic atmospheric simulation and prediction model. Part I: Model dynamics and verification. Meteor. Atmos. Phys., 75, 161-193.
56. Xue, M., and Coauthors, 2001: The Advanced Regional Prediction System (ARPS)-A multiscale nonhydrostatic atmospheric simulation and prediction tool. Part II: Model physics and applications. Meteor. Atmos. Phys., 76, 134-165.
57. Xue, M., D. Wang, J. , K. Brewster, and K. K. Droegemeier, 2003: The Advanced Regional Prediction System (ARPS) storm-scale numerical weather prediction and data assimilation. Meteor. Atmos. Phys., 82, 139-170.
58. Xue, M., and Coauthors, 2010: CAPS realtime storm scale ensemble and high-resolution forecasts for the NOAA Hazardous Weather Testbed 2010 Spring Experiment. Preprints, 25th Conf. on Severe Local Storms, Denver, CO, Amer. Meteor. Soc., 7B.3. [Available online at https://ams.confex.com/ams/25SLS/webprogram/Paper176056.html.]