Application of two spatial verification methods to ensemble forecasts of low-level rotation

Weather and Forecasting

Volumn 31, Issue 3, 2016, Pages 713-735

  Skinner, Patrick S ^a   Wicker, Louis J ^a   Wheatley, Dustan M ^a,b   Knopfmeier, Kent H ^a,b  

^a NATIONAL SEVERE STORMS LABORATORY   (United States)

^b UNIVERSITY OF OKLAHOMA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ROTATION; STORMS;

DISPLACEMENT COMPONENTS; ENSEMBLE FORECASTS; MATCHING ALGORITHM; NATIONAL SEVERE STORMS LABORATORIES; SPATIAL VERIFICATIONS; TEMPORAL DISPLACEMENT; TORNADIC SUPERCELLS; VERIFICATION TECHNIQUES;

FORECASTING;

ALGORITHM; COMPUTER SIMULATION; ENSEMBLE FORECASTING; MODEL TEST; MODEL VALIDATION; NUMERICAL MODEL; SPATIAL ANALYSIS; SUPERCELL; TORNADO; WEATHER FORECASTING;

EID: 84964940304     PISSN: 08828156     EISSN: 15200434     Source Type: Journal    
DOI: 10.1175/WAF-D-15-0129.1     Document Type: Article

References (58)

1. Anderson, J. L., and N. Collins, 2007: Scalable implementations of ensemble filter algorithms for data assimilation. J. Atmos. Oceanic Technol., 24, 1452-1463, doi:10.1175/JTECH2049.1.
2. Anderson, J. L., T. Hoar, K. Raeder, H. Liu, N. Collins, R. Torn, and A. Avellano, 2009: The Data Assimilation Research Testbed: A community facility. Bull. Amer. Meteor. Soc., 90, 1283-1296, doi:10.1175/2009BAMS2618.1.
3. Atkins, N. T., K. M. Butler, K. R. Flynn, and R. M. Wakimoto, 2014: An integrated damage, visual, and radar analysis of the 2013 Moore, Oklahoma, EF5 tornado. Bull. Amer. Meteor. Soc., 95, 1549-1561, doi:10.1175/BAMS-D-14-00033.1.
4. Bluestein, H. B., J. C. Snyder, and J. B. Houser, 2015: A multiscale overview of the El Reno, Oklahoma, tornadic supercell of 31 May 2013. Wea. Forecasting, 30, 525-552, doi:10.1175/ WAF-D-14-00152.1.
5. Burgess, D., and Coauthors, 2014: 20 May 2013 Moore, Oklahoma, tornado: Damage survey and analysis. Wea. Forecasting, 29, 1229-1237, doi:10.1175/WAF-D-14-00039.1.
6. Burghardt, B. J., C. Evans, and P. J. Roebber, 2014: Assessing the predictability of convection initiation in the high plains using an object-based approach. Wea. Forecasting, 29, 403-418, doi:10.1175/WAF-D-13-00089.1.
7. Cai, H., and R. E. Dumais, 2015: Object-based evaluation of a numerical weather prediction model's performance through storm characteristic analysis. Wea. Forecasting, 30, 1451-1468, doi:10.1175/WAF-D-15-0008.1.
8. Clark, A.J., W.A. Gallus II, and M.L. Weisman, 2010: Neighborhood-based verification of precipitation forecasts from convection-allowing NCAR WRF model simulations and the operational NAM. Wea. Forecasting, 25, 1495-1509, doi10.1175/ 2010WAF2222404.1.
9. 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, doi:10.1175/2010MWR3624.1.
10. Clark, A.J., J. S. Kain, P. T. Marsh, J. Correia Jr., M. Xue, and F. Kong, 2012: Forecasting tornado pathlengths using a three-dimensional object identification algorithm applied to convection-allowing forecasts. Wea. Forecasting, 27, 1090-1113, doi:10.1175/WAF-D-11-00147.1.
11. Clark, A.J., J. Gao, P. T. Marsh, T. Smith, J. S. Kain, J. Correia Jr., M. Xue, and F. Kong, 2013: Tornado pathlength forecasts from 2010 to 2011 using ensemble updraft helicity. Wea. Forecasting, 28, 387-407, doi:10.1175/WAF-D-12-00038.1.
12. Clark, A.J., R. G. Bullock, T. L. Jensen, M. Xue, and F. Kong, 2014: Application of object-based time-domain diagnostics for tracking precipitation systems in convection-allowing models. Wea. Forecasting, 29, 517-542, doi:10.1175/WAF-D-13-00098.1.
13. Davis, C. A., B. G. Brown, and R. G. Bullock, 2006a: Object-based verification of precipitation forecasts. Part I: Methodology and application to mesoscale rain areas. Mon. Wea. Rev., 134, 1772-1784, doi10.1175/MWR3145.1.
14. Clark, A.J., R. G. Bullock, T. L. Jensen, M. Xue, and F. Kong, 2006b: Object-based verification of precipitation forecasts. Part II: Application to convective rain systems. Mon. Wea. Rev., 134, 1785-1795, doi:10.1175/ MWR3146.1.
15. Clark, A.J., R. G. Bullock, T. L. Jensen, M. Xue, and J. H. Gotway, 2009: The Method for Object-based Diagnostic Evaluation (MODE) applied to WRF forecasts from the 2005 Spring Program. Wea. Forecasting, 24, 1252-1267, doi:10.1175/2009WAF2222241.1.
16. Dawson, D. T., II, L. J. Wicker, E. R. Mansell, and R. L. Tanamachi, 2012: Impact of the environmental low-level wind profile on ensemble forecasts of the 4 May 2007 Greensburg, Kansas, tornadic storm and associated mesocyclones. Mon. Wea. Rev., 140, 696-716, doi:10.1175/MWR-D-11-00008.1.
17. Ebert, E. E., 2009: Neighborhood verification: A strategy for rewarding close forecasts. Wea. Forecasting, 24, 1498-1510, doi:10.1175/2009WAF2222251.1.
18. Dawson, D. T., II, L. J. Wicker, E. R. Mansell, and W. A. Gallus Jr., 2009: Toward better understanding of the contiguous rain area (CRA) method for spatial forecast verification. Wea. Forecasting, 24, 1401-1415, doi:10.1175/ 2009WAF2222252.1.
19. Evensen, G., 1994: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99, 10143-10162, doi:10.29/94JC00572.
20. Gallus, W. A. J., Jr., 2010: Application of object-based verification techniques to ensemble precipitation forecasts. Wea. Forecasting, 25, 144-158, doi:10.1175/2009WAF2222274.1.
21. Gao, J., and Coauthors, 2013: A real-time weather-adaptive 3DVAR analysis system for severe weather detections and warnings. Wea. Forecasting, 28, 727-745, doi:10.1175/ WAF-D-12-00093.1.
22. Gilleland, E., D. Ahijevych, B. Brown, and E. Ebert, 2009: Intercomparison of spatial forecast verification methods. Wea. Forecasting, 24, 1416-1430, doi:10.1175/2009WAF2222269.1.
23. Gilleland, E., D. Ahijevych, B. Brown, and E. Ebert, 2010: Verifying forecasts spatially. Bull. Amer. Meteor. Soc., 91, 1365-1373, doi:10.1175/2010BAMS2819.1.
24. Johnson, A., and X. Wang, 2012: Verification and calibration of neighborhood and object-based probabilistic precipitation forecasts from a multimodel convection-allowing ensemble. Mon. Wea. Rev., 140, 3054-3077, doi:10.1175/MWR-D-11-00356.1.
25. Johnson, A., and X. Wang, 2013: Object-based evaluation of a storm-scale ensemble during the 2009 NOAA Hazardous Weather Test-bed Spring Experiment. Mon. Wea. Rev., 141, 1079-1098, doi:10.1175/MWR-D-12-00140.1.
26. Johnson, A., X. Wang, F. Kong, and M. Xue, 2011: Hierarchical cluster analysis of a convection-allowing ensemble during the Hazardous Weather Testbed 2009 Spring Experiment. Part I: Development of the object-oriented cluster analysis method for precipitation fields. Mon. Wea. Rev., 139, 3673-3693, doi:10.1175/ MWR-D-11-00015.1.
27. Gilleland, E., D. Ahijevych, B. Brown, and E. Ebert, 2013: Object-based evaluation of the impact of horizontal grid spacing on convection-allowing forecasts. Mon. Wea. Rev., 141, 3413-3425, doi:10.1175/ MWR-D-13-00027.1.
28. Jones, T. A., K. Knopfmeier, D. Wheatley, G. Creager, P. Minnis, and R. Palikondo, 2016: Storm-scale data assimilation and ensemble forecasting with the NSSL experimental Warn-on-Forecast system. Part II: Combined radar and satellite data experiments. Wea. Forecasting, 31, 297-327, doi:10.1175/ WAF-D-15-0107.1.
29. Kain, J. S., and Coauthors, 2008: Some practical considerations regarding horizontal resolution in the first generation of operational convection-allowing NWP. Wea. Forecasting, 23, 931-952, doi:10.1175/WAF2007106.1.
30. Keil, C., and G. C. Craig, 2007: A displacement-based error measure applied in a regional ensemble forecasting system. Mon. Wea. Rev., 135, 3248-3259, doi:10.1175/MWR3457.1.
31. Keil, C., and G. C. Craig, 2009: A displacement and amplitude score employing an optical flow technique. Wea. Forecasting, 24, 1297-1308, doi:10.1175/2009WAF2222247.1.
32. Lakshmanan, V., C. Karstens, J. Krause, and L. Tang, 2014: Quality control of weather radar data using polarimetric variables. J. Atmos. Oceanic Technol., 31, 1234-1249, doi:10.1175/ JTECH-D-13-00073.1.
33. Lange, H., and G. C. Craig, 2014: The impact of data assimilation length scales on analysis and prediction of convective storms. Mon. Wea. Rev., 142, 3781-3808, doi:10.1175/MWR-D-13-00304.1.
34. Marzban, C., and S. Sandgathe, 2010: Optical flow for verification. Wea. Forecasting, 25, 1479-1494, doi:10.1175/2010WAF2222351.1.
35. Marzban, C., S. Sandgathe, H. Lyons, and N. Lederer, 2009: Three spatial verification techniques: Cluster analysis, variogram, and optical flow. Wea. Forecasting, 24, 1457-1471, doi:10.1175/ 2009WAF2222261.1.
36. Meng, Z., and F. Zhang, 2011: Limited-area ensemble-based data assimilation. Mon. Wea. Rev., 139, 2025-2045, doi:10.1175/ 2011MWR3418.1.
37. Miller, M. L., V. Lakshmanan, and T. M. Smith, 2013: An automated method for depicting mesocyclone paths and intensities. Wea. Forecasting, 28, 570-585, doi:10.1175/WAF-D-12-00065.1.
38. Miller, P. A., M. F. Barth, L. A. Benjamin, R. S. Artz, and W. R. Pendergrass, 2007: MADIS support for UrbaNet. Preprints, 14th Symp. on Meteorological Observation and Instrumentation/16th Conf. on Applied Climatology, San Antonio, TX, Amer. Meteor. Soc., JP2.5. [Available online at https://ams.confex.com/ams/pdfpapers/119116.pdf.]
39. Newman, J. F., V. Lakshmanan, P. L. Heinselman, M. B. Richman, and T. M. Smith, 2013: Range-correcting azimuthal shear in Doppler radar data. Wea. Forecasting, 28, 194-211, doi:10.1175/WAF-D-11-00154.1.
40. Pinto, J. O., J. A. Grim, and M. Steiner, 2015: Assessment of the High-Resolution Rapid Refresh model's ability to predict mesoscale convective systems using object-based evaluation. Wea. Forecasting, 30, 892-913, doi:10.1175/WAF-D-14-00118.1.
41. Potvin, C. K., and M. L. Flora, 2015: Sensitivity of idealized supercell simulations to horizontal grid spacing: Implications for Warn-on-Forecast. Mon. Wea. Rev., 143, 2998-3024, doi:10.1175/MWR-D-14-00416.1.
42. Putnam, B. J., M. Xue, Y. Yung, N. A. Snook, and G. Zhang, 2014: The analysis and prediction of microphysical states and polarimetric variables in a mesoscale convective system using double-moment microphysics, multinetwork radar data, and the ensemble Kalman filter. Mon. Wea. Rev., 142, 141-162, doi:10.1175/MWR-D-13-00042.1.
43. Schwartz, C. S., and Coauthors, 2009: Next-day convection-allowing WRF model guidance: A second look at 2-km versus 4-km grid spacing. Mon. Wea. Rev., 137, 3351-3372, doi:10.1175/2009MWR2924.1.
44. Skamarock, W. C., 2004: Evaluating mesoscale NWP models using kinetic energy spectra. Mon. Wea. Rev., 132, 3019-3032, doi:10.1175/MWR2830.1.
45. Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-4751STR, 113 pp., doi:10.5065/D68S4MVH.
46. Smith, T. M., and K. L. Elmore, 2004: The use of radial velocity derivatives to diagnose rotation and divergence. Preprints, 11th Conf. on Aviation, Range, and Aerospace, Hyannis, MA, Amer. Meteor. Soc., P5.6. [Available online at https://ams.confex.com/ams/pdfpapers/81827.pdf.]
47. Snook, N., M. Xue, and J. Jung, 2012: Ensemble probabilistic forecasts of a tornadic mesoscale convective system from ensemble Kalman filter analyses using WSR-88D and CASA radar data. Mon. Wea. Rev., 140, 2126-2146, doi:10.1175/ MWR-D-11-00117.1.
48. Snook, N., M. Xue, and J. Jung, 2015: Multiscale EnKF assimilation of radar and conventional observations and ensemble forecasting for a tornadic mesoscale convective system. Mon. Wea. Rev., 143, 1035-1057, doi:10.1175/MWR-D-13-00262.1.
49. Sobash, R. A., and L. J. Wicker, 2014: Ensemble forecasts of the 17 November 2013 Illinois tornado outbreak using EnKF radar and surface data assimilation. Proc. 27th Conf. on Severe Local Storms, Madison, WI, Amer. Meteor. Soc., 8A.6. [Available online at https://ams.confex.com/ams/ 27SLS/webprogram/Paper255618.html.]
50. Stensrud, D. J., and Coauthors, 2009: Convective-scale warn-on-forecast system. Bull. Amer. Meteor. Soc., 90, 1487-1499, doi:10.1175/2009BAMS2795.1.
51. Stensrud, D. J., and Coauthors, 2013: Progress and challenges with Warn-on-Forecast. Atmos. Res., 123, 2-16, doi:10.1016/j.atmosres.2012.04.004.
52. Trapp, R. J., G. J. Stumpf, and K. L. Manross, 2005: A reassessment of the percentage of tornadic mesocyclones. Wea. Forecasting, 20, 680-687, doi:10.1175/WAF864.1.
53. Van der Walt, S., and Coauthors, 2014: Scikit-image: Image processing in Python. Peer J, 2, e453, doi:10.7717/peerj.453.
54. Wheatley, D. M., K. H. Knopfmeier, T. A. Jones, and G. J. Creager, 2015: Storm-scale data assimilation and ensemble forecasting with the NSSL experimental Warn-on-Forecast system. Part I: Radar data experiments. Wea. Forecasting, 30, 1795-1817, doi:10.1175/WAF-D-15-0043.1.
55. Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences. Elsevier, 627 pp.
56. Wolff, J. K., M. Harrold, T. Fowler, J. H. Gotway, L. Nance, and B. G. Brown, 2014: Beyond the basics: Evaluating model-based precipitation forecasts using traditional, spatial, and object-based methods. Wea. Forecasting, 29, 1451-1472, doi:10.1175/ WAF-D-13-00135.1.
57. Yussouf, N., E. R. Mansell, L. J. Wicker, D. M. Wheatley, and D. J. Stensrud, 2013: The ensemble Kalman filter analyses and forecasts of the 8 May 2003 Oklahoma City tornadic supercell storms using single- and double-moment microphysics schemes. Mon. Wea. Rev., 141, 3388-3412, doi:10.1175/ MWR-D-12-00237.1.
58. Yussouf, N., D. C. Dowell, L. J. Wicker, K. H. Knopfmeier, and D. M. Wheatley, 2015: Storm-scale data assimilation and ensemble forecasts for the 27 April 2011 severe weather outbreak in Alabama. Mon. Wea. Rev., 143, 3044-3066, doi:10.1175/ MWR-D-14-00268.1.