The tornadic supercell on the Kanto Plain on 6 May 2012: Polarimetric radar and surface data assimilation with EnKF and ensemble-based sensitivity analysis

Monthly Weather Review

Volumn 144, Issue 9, 2016, Pages 3133-3157

  Yokota, Sho ^a   Seko, Hiromu ^a,b   Kunii, Masaru ^a   Yamauchi, Hiroshi ^a   Niino, Hiroshi ^c  

^a JAPAN METEOROLOGICAL AGENCY   (Japan)

^b JAPAN AGENCY FOR MARINE EARTH SCIENCE AND TECHNOLOGY   (Japan)

^c UNIVERSITY OF TOKYO   (Japan)

Author keywords

Atm Ocean Structure Phenomena; Data assimilation; Ensembles; Forecasting; Kalman filters; Mathematical and statistical techniques; Models and modeling; Observational techniques and algorithms; Radars Radar observations; Surface observations; Tornadoes

Indexed keywords

DOPPLER RADAR; FORECASTING; KALMAN FILTERS; METEOROLOGICAL RADAR; STORMS; TORNADOES;

DATA ASSIMILATION; ENSEMBLES; RADARS/RADAR OBSERVATIONS; STATISTICAL TECHNIQUES; SURFACE OBSERVATION;

SENSITIVITY ANALYSIS;

ALGORITHM; DATA ASSIMILATION; ENSEMBLE FORECASTING; KALMAN FILTER; NUMERICAL MODEL; RADAR; SENSITIVITY ANALYSIS; SUPERCELL; TORNADO; WEATHER FORECASTING;

HONSHU; JAPAN; KANTO; KANTO PLAIN;

EID: 84986269223     PISSN: 00270644     EISSN: 15200493     Source Type: Journal    
DOI: 10.1175/MWR-D-15-0365.1     Document Type: Article

References (108)

1. Adachi, A., T. Kobayashi, H. Yamauchi, and S. Onogi, 2013: Detection of potentially hazardous convective clouds with a dualpolarized C-band radar. Atmos. Meas. Tech., 6, 2741-2760, doi:10.5194/amt-6-2741-2013.
2. Adlerman, E. J., K. K. Droegemeier, and R. Davies-Jones, 1999:A numerical simulation of cyclic mesocyclogenesis. J. Atmos. Sci., 56, 2045-2069, doi:10.1175/1520-0469(1999)056<2045:ANSOCM>2.0.CO;2.
3. Ancell, B. C., and G. J. Hakim, 2007: Comparing adjoint-and ensemble-sensitivity analysis with applications to observation targeting. Mon. Wea. Rev., 135, 4117-4134, doi:10.1175/2007MWR1904.1.
4. Ancell, B. C., C. F. Mass, and G. J. Hakim, 2011: Evaluation of surface analyses and forecasts with a multiscale ensemble Kalman filter in regions of complex terrain. Mon. Wea. Rev., 139, 2008-2024, doi:10.1175/2010MWR3612.1.
5. Anderson, J. L., and S. L. Anderson, 1999: A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts. Mon. Wea. Rev., 127, 2741-2758, doi:10.1175/1520-0493(1999)127<2741:AMCIOT>2.0.CO;2.
6. Beljaars, A. C. M., and A. A. M. Holtslag, 1991: Flux parameterization over land surfaces for atmospheric models. J. Appl. Meteor., 30, 327-341, doi:10.1175/1520-0450(1991)030<0327:FPOLSF>2.0.CO;2.
7. Borowska, L., A. Ryzhkov, D. Zrnic, C. Simmer, andR. Palmer, 2011: Attenuation and differential attenuation of 5-cm-wavelength radiation in melting hail. J. Appl. Meteor. Climatol., 50, 59-76, doi:10.1175/2010JAMC2465.1.
8. Bringi, V. N., and V. Chandrasekar, 2001: Polarimetric Doppler Weather Radar: Principles and Applications. Cambridge UniversityPress, 636 pp.
9. Browning, K. A., 1964: Airflow and precipitation trajectories within severe local storms which travel to the right of the winds. J.Atmos. Sci., 21, 634-639, doi:10.1175/1520-0469(1964)021<0634:AAPTWS>2.0.CO;2.
10. Carey, L. D., S. A. Rutledge, D. A. Ahijevych, and T. D. Keenan, 2000: Correcting propagation effects in C-band polarimetric radar observations of tropical convection using differential propagation phase. J.Appl.Meteor., 39, 1405-1433, doi:10.1175/1520-0450(2000)039<1405:CPEICB>2.0.CO;2.
11. Caya, A., J. Sun, and C. Snyder, 2005: A comparison between the 4DVAR and the ensemble Kalman filter techniques for radar data assimilation. Mon. Wea. Rev., 133, 3081-3094, doi:10.1175/MWR3021.1.
12. Chandrasekar, V., V. N. Bringi, N. Balakrishnan, and D. S. Zrnic, 1990: Error structure of multiparameter radar and surface measurements of rainfall. Part III: Specific differential phase. J. Atmos. Oceanic Technol., 7, 621-629, doi:10.1175/1520-0426(1990)007<0621:ESOMRA>2.0.CO;2.
13. Cintineo, R. M., and D. J. Stensrud, 2013: On the predictability of supercell thunderstorm evolution. J. Atmos. Sci., 70, 1993-2011, doi:10.1175/JAS-D-12-0166.1.
14. Craven, J. P., and H. E. Brooks, 2004: Baseline climatology of sounding derived parameters associated with deep, moist convection. Natl. Wea. Dig., 28, 13-24.
15. Cressman, G. P., 1959: An operational objective analysis system. Mon. Wea. Rev., 87, 367-374, doi:10.1175/1520-0493(1959)087<0367: AOOAS>2.0.CO;2.
16. Davies-Jones, R. P., 1982: Observational and theoretical aspects of tornadogenesis. Intense Atmospheric Vortices, L. Bengtsson and J. Lighthill, Eds., Springer-Verlag, 175-189.
17. Davies-Jones, R. P., D. W. Burgess, and M. P. Foster, 1990: Test of helicity as a tornado forecast parameter. Preprints, 16th Conf. on Severe Local Storms, Kananaskis, Alberta, Canada, Amer. Meteor. Soc., 588-592.
18. Dawson, D. T., M. Xue, J. A. Milbrandt, and M. K. Yau, 2010: Comparison of evaporation and cold pool development between single-moment and multimoment bulk microphysics schemes in idealized simulations of tornadic thunderstorms. Mon. Wea. Rev., 138, 1152-1171, doi:10.1175/2009MWR2956.1.
19. Dawson, D. T., 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.
20. Dawson, D. T., M. Xue, J. A. Milbrandt, and A. Shapiro, 2015: Sensitivity of real-data simulations of the 3 May 1999 Oklahoma city tornadic supercell and associated tornadoes to multimoment microphysics. Part I: Storm-and tornado-scale numerical forecasts. Mon. Wea. Rev., 143, 2241-2265, doi:10.1175/MWR-D-14-00279.1.
21. Dawson, D. T., M. Xue, A. Shapiro, J. A. Milbrandt, and A. D. Schenkman, 2016: Sensitivity of real-data simulations of the 3 May 1999 Oklahoma city tornadic supercell and associated tornadoes to multimoment microphysics. Part II: Analysis of buoyancy and dynamic pressure forces in simulated tornado-like vortices. J. Atmos. Sci., 73, 1039-1061, doi:10.1175/JAS-D-15-0114.1.
22. Dazhang, T., S. G. Geotis, R. E. Passarelli Jr., A. L. Hansen, and C. L. Frush, 1984: Evaluation of an alternating-PRF method for extending the range of unambiguous Doppler velocity. Preprints, 22nd Conf. on Radar Meteorology, Zurich, Switzerland, Amer. Meteor. Soc., 523-527.
23. Deardorff, J. W., 1980: Stratocumulus-capped mixed layers derived from a three-dimensional model. Bound.-Layer Meteor., 18, 495-527, doi:10.1007/BF00119502.
24. Dowell, D. C., F. Zhang, L. J. Wicker, C. Snyder, and N. A. Crook, 2004: Wind and temperature retrievals in the 17 May 1981 Arcadia, Oklahoma, supercell: Ensemble Kalman filter experiments. Mon. Wea. Rev., 132, 1982-2005, doi:10.1175/1520-0493(2004)132<1982:WATRIT>2.0.CO;2.
25. Dowell, D. C., L. J. Wicker, and C. Snyder, 2011: Ensemble Kalman filter assimilation of radar observations of the 8 May 2003 Oklahoma City supercell: Influences of reflectivity observations on storm-scale analyses. Mon. Wea. Rev., 139, 272-294, doi:10.1175/2010MWR3438.1.
26. Fujita, T. T., 1971: Proposed characterization of tornadoes and hurricanes by area and intensity. SMRP Research Paper 91, University of Chicago, 42 pp.
27. Fujita, T., D. J. Stensrud, and D. C. Dowell, 2007: Surface data assimilation using an ensemble Kalman filter approach with initial condition and model physics uncertainties. Mon. Wea. Rev., 135, 1846-1868, doi:10.1175/MWR3391.1.
28. Ha, S.-Y., and C. Snyder, 2014: Influence of surface observations in mesoscale data assimilation using an ensemble Kalman filter.Mon.Wea. Rev., 142, 1489-1508, doi:10.1175/MWR-D-13-00108.1.
29. Hacker, J. P., and C. Snyder, 2005: Ensemble Kalman filter assimilation of fixed screen-height observations in a parameterized PBL. Mon. Wea. Rev., 133, 3260-3275, doi:10.1175/MWR3022.1.
30. Honda, Y., 2010: Observation operator (in Japanese). Extra volume to the annual report of Numerical Prediction Division, Japan Meteorological Agency, JMA Tech. Doc. 56, 19-20.
31. Honda, Y., M. Nishijima, K. Koizumi, Y. Ohta, K. Tamiya, T. Kawabata, and T. Tsuyuki, 2005: A pre-operational variational data assimilation system for a non-hydrostatic model at the Japan Meteorological Agency: Formulation and preliminary results. Quart. J. Roy. Meteor. Soc., 131, 3465-3475, doi:10.1256/qj.05.132.
32. Hu, M., and M. Xue, 2007: Impact of configurations of rapid intermittent assimilation of WSR-88D radar data for the 8 May 2003 Oklahoma City tornadic thunderstorm case. Mon. Wea. Rev., 135, 507-525, doi:10.1175/MWR3313.1.
33. Hu, M., and M. Xue, and K. Brewster, 2006a: 3DVAR and cloud analysis with WSR-88D level-II data for the prediction of the Fort Worth, Texas, tornadic thunderstorms. Part I: Cloud analysis and its impact. Mon. Wea. Rev., 134, 675-698, doi:10.1175/MWR3092.1.
34. Hu, M., and M. Xue, J. Gao, and K. Brewster, 2006b: 3DVAR and cloud analysis with WSR-88D level-II data for the prediction of the Fort Worth, Texas, tornadic thunderstorms. Part II: Impact of radial velocity analysis via 3DVAR. Mon. Wea. Rev., 134, 699-721, doi:10.1175/MWR3093.1.
35. Hubbert, J., and V. N. Bringi, 1995: An iterative filtering technique for the analysis of copolar differential phase and dual-frequency radar measurements. J. Atmos. Oceanic Technol., 12, 643-648, doi:10.1175/1520-0426(1995)012<0643:AIFTFT>2.0.CO;2.
36. Hunt, B. R., E. J. Kostelich, and I. Szunyogh, 2007: Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. PhysicaD, 230, 112-126, doi:10.1016/j.physd.2006.11.008.
37. Ikuta, Y., 2012: Radar reflectivity assimilation in JMA's operational meso-analysis system. CAS/JSC WGNE Res. Activ. Atmos. Oceanic Modell., 42, 0105-0116.
38. Ikuta, Y., and Y. Honda, 2011: Development of 1D14DVAR data assimilation of radar reflectivity in JNoVA. CAS/JSC WGNE Res. Activ. Atmos. Oceanic Modell., 41, 0109-0110.
39. Ishida, J., 2007: Development of a hybrid terrain-following vertical coordinate for JMA non-hydrostatic model. CAS/JSC WGNE Res. Activ. Atmos. Oceanic Modell., 37, 0309-0310.
40. JapanMeteorologicalAgency, 2012: Tornadoes generated on 6 May 2012 (report, in Japanese). Meteorological Research Institute, Tokyo District Meteorological Observatory, and Sendai District Meteorological Observatory, 14 pp. [Available online at http://www.jma.go.jp/jma/press/1206/08b/toppu120608.html.]
41. Jung, Y., M. Xue, G. Zhang, and J. M. Straka, 2008a: Assimilation of simulated polarimetric radar data for a convective storm using the ensemble Kalman filter. Part II: Impact of polarimetric data on storm analysis. Mon. Wea. Rev., 136, 2246-2260, doi:10.1175/2007MWR2288.1.
42. Jung, Y., G. Zhang, and M. Xue, 2008b: Assimilation of simulated polarimetric radar data for a convective storm using the ensemble Kalman filter. Part I: Observation operators for reflectivity and polarimetric variables. Mon. Wea. Rev., 136, 2228-2245, doi:10.1175/2007MWR2083.1.
43. Jung, Y., M. Xue, and G. Zhang, 2010a: Simulations of polarimetric radar signatures of a supercell storm using a two-moment bulk microphysics scheme. J. Appl. Meteor. Climatol., 49, 146-163, doi:10.1175/2009JAMC2178.1.
44. Jung, Y., M. Xue, and G. Zhang, 2010b: Simultaneous estimation of microphysical parameters and the atmospheric state using simulated polarimetric radar data and an ensemble Kalman filter in the presence of an observation operator error. Mon. Wea. Rev., 138, 539-562, doi:10.1175/2009MWR2748.1.
45. Kain, J. S., 2004: The Kain-Fritsch convective parameterization: An update. J. Appl. Meteor., 43, 170-181, doi:10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2.
46. Kain, J. S., and J. M. Fritsch, 1990: A one-dimensional entraining/detraining plume model and its application in convective parameterization. J. Atmos. Sci., 47, 2784-2802, doi:10.1175/1520-0469(1990)047<2784:AODEPM>2.0.CO;2.
47. Kalnay, E., H. Li, T. Miyoshi, S.-C. Yang, and J. Ballabrera-Poy, 2007: Response to the discussion on ''4-D-Var or EnKF?'' by Nils Gustafsson. Tellus, 59A, 778-780, doi:10.1111/j.1600-0870.2007.00263.x.
48. Kawabata, T., T. Kuroda, H. Seko, and K. Saito, 2011: A cloudresolving 4DVAR assimilation experiment for a local heavy rainfall event in the Tokyo metropolitan area. Mon. Wea. Rev., 139, 1911-1931, doi:10.1175/2011MWR3428.1.
49. Koizumi, K., Y. Ishikawa, and T. Tsuyuki, 2005: Assimilation of precipitation data to the JMA mesoscale model with a four-dimensional variational method and its impact on precipitation forecasts. SOLA, 1, 45-48, doi:10.2151/sola.2005-013.
50. Kunii, M., 2014: The 1000-member ensemble Kalman filtering with the JMA nonhydrostatic mesoscale model on the K computer. J. Meteor. Soc. Japan, 92, 623-633, doi:10.2151/jmsj.2014-607.
51. Li, X., and J. R. Mecikalski, 2010: Assimilation of the dualpolarization Doppler radar data for a convective storm with a warm-rain radar forward operator. J. Geophys. Res., 115, D16208, doi:10.1029/2009JD013666.
52. Li, X., and J. R. Mecikalski, 2012: Impact of the dual-polarization Doppler radar data on two convective storms with a warm-rain radar forward operator. Mon.Wea. Rev., 140, 2147-2167, doi:10.1175/MWR-D-11-00090.1.
53. Li, X., and J. R. Mecikalski, 2013: Evaluation of the sensitivity of the dualpolarization Doppler warm-rain radar data assimilation to radar forward operators for a convective storm. J. Meteor. Soc. Japan, 91, 287-304, doi:10.2151/jmsj.2013-304.
54. Lin, Y.-L., R. D. Farley, and H. D. Orville, 1983: Bulk parameterization of the snow field in a cloud model. J. Climate Appl. Meteor., 22, 1065-1092, doi:10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2.
55. Maddox, R. A., 1976: An evaluation of tornado proximity wind and stability data. Mon. Wea. Rev., 104, 133-142, doi:10.1175/1520-0493(1976)104<0133:AEOTPW>2.0.CO;2.
56. Markowski, P. M., and Y. P. Richardson, 2014: The influence of environmental low-level shear and cold pools on tornadogenesis: Insights from idealized simulations. J. Atmos. Sci., 71, 243-275, doi:10.1175/JAS-D-13-0159.1.
57. Markowski, P. M., J. M. Straka, and E. N. Rasmussen, 2002: Direct surface thermodynamic observations within the rear-flank downdrafts of nontornadic and tornadic supercells. Mon. Wea. Rev., 130, 1692-1721, doi:10.1175/1520-0493(2002)130<1692: DSTOWT>2.0.CO;2.
58. Markowski, P. M., J. M. Straka, and E. N. Rasmussen, 2003: Tornadogenesis resulting from the transport of circulation by a downdraft: Idealized numerical simulations. J. Atmos. Sci., 60, 795-823, doi:10.1175/1520-0469(2003)060<0795:TRFTTO>2.0.CO;2.
59. Markowski, P. M., Y. P. Richardson, E. Rasmussen, J. Straka, R. Davies-Jones, and R. J. Trapp, 2008: Vortex lines within low-level mesocyclones obtained from pseudo-dual-Doppler radar observations. Mon. Wea. Rev., 136, 3513-3535, doi:10.1175/2008MWR2315.1.
60. Marquis, J., Y. Richardson, P. Markowski, D. Dowell, and J. Wurman, 2012: Tornado maintenance investigated with high-resolution dual-Doppler and EnKF analysis. Mon. Wea. Rev., 140, 3-27, doi:10.1175/MWR-D-11-00025.1.
61. Marquis, J., Y. Richardson, P. Markowski, D. Dowell, and J. Wurman, K. Kosiba, P. Robinson, and G. Romine, 2014: An investigation of the Goshen County, Wyoming, tornadic supercell of 5 June 2009 using EnKF assimilation of mobile mesonet and radar observations collected during VORTEX2. Part I: Experiment design and verification of the EnKF analyses. Mon. Wea. Rev., 142, 530-554, doi:10.1175/MWR-D-13-00007.1.
62. Mashiko, W., 2016: A numerical study of the 6 May 2012 Tsukuba City supercell tornado. Part I: Vorticity sources of low-level and midlevel mesocyclones. Mon. Wea. Rev., 144, 1069-1092, doi:10.1175/MWR-D-15-0123.1.
63. Meng, M., and F. Zhang, 2007: Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part II: Imperfect model experiments. Mon. Wea. Rev., 135, 1403-1423, doi:10.1175/MWR3352.1.
64. Meng, M., and F. Zhang, 2008: Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part III: Comparison with 3DVAR in a real-data case study. Mon. Wea. Rev., 136, 522-540, doi:10.1175/2007MWR2106.1.
65. Murakami, M., 1990: Numerical modeling of dynamical and microphysical evolution of an isolated convective cloud-The 19 July 1981 CCOPE cloud. J. Meteor. Soc. Japan, 68, 107-128.
66. Muroi, C., T. Fujita, and Y. Ishikawa, 2008: Hourly analysis at the Japan Meteorological Agency (in Japanese). Tenki, 55, 401-408.
67. Nakanishi, M., 2001: Improvement of the Mellor-Yamada turbulence closure model based on large-eddy simulation data. Bound.-Layer Meteor., 99, 349-378, doi:10.1023/A:1018915827400.
68. Nakanishi, M., and H. Niino, 2004: An improvedMellor-Yamada Level-3 model with condensation physics: Its design and verification. Bound.-Layer Meteor., 112, 1-31, doi:10.1023/B: BOUN.0000020164.04146.98.
69. 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, doi:10.1007/s10546-005-9030-8.
70. Nishi, A., K. Araki, K. Saito, T. Kawabata, and H. Seko, 2015: The consideration and application of the quality control method for the atmospheric environmental regional observation system (AEROS) meteorological observation data (in Japanese).Tenki, 8, 627-639.
71. Parker, M. D., and J. M. L. Dahl, 2015: Production of near-surface vertical vorticity by idealized downdrafts. Mon. Wea. Rev., 143, 2795-2816, doi:10.1175/MWR-D-14-00310.1.
72. Putnam, B. J., M. Xue, Y. Jung, N. Snook, and G. Zhang, 2014: The analysis and prediction of microphysical states and polarimetric radar 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.
73. Richardson, Y. P., K. K. Droegemeier, and R. P. Davies-Jones, 2007: The influence of horizontal environmental variability on numerically simulated convective storms. Part I: Variations in vertical shear. Mon. Wea. Rev., 135, 3429-3455, doi:10.1175/MWR3463.1.
74. Rotunno, R., and J. Klemp, 1982: The influence of the shear-induced pressure gradient on thunderstorm motion. Mon. Wea. Rev., 110, 136-151, doi:10.1175/1520-0493(1982)110<0136:TIOTSI>2.0.CO;2.
75. Rotunno, R., and J. Klemp, 1985: On the rotation and propagation of simulated supercell thunderstorms. J. Atmos. Sci., 42, 271-292, doi:10.1175/1520-0469(1985)042<0271:OTRAPO>2.0.CO;2.
76. Sachidananda, M., and D. S. Zrnić, 1986: Differential propagation phase shift and rainfall rate estimation. Radio Sci., 21, 235-247, doi:10.1029/RS021i002p00235.
77. Rotunno, R., and J. Klemp, 1987: Rain rate estimates from differential polarization measurements. J. Atmos. Oceanic Technol., 4, 588-598, doi:10.1175/1520-0426(1987)004<0588:RREFDP>2.0.CO;2.
78. Saito, K., and Coauthors, 2006: The operational JMA nonhydrostatic mesoscale model. Mon. Wea. Rev., 134, 1266-1298, doi:10.1175/MWR3120.1.
79. Schenkman, A. D., M. Xue, A. Shapiro, K. Brewster, and J. Gao, 2011a: The analysis and prediction of the 8-9 May 2007 Oklahoma tornadic mesoscale convective system by assimilating WSR-88D and CASA radar data using 3DVAR. Mon. Wea. Rev., 139, 224-246, doi:10.1175/2010MWR3336.1.
80. Schenkman, A. D., M. Xue, A. Shapiro, K. Brewster, and J. Gao, 2011b: Impact of CASAradar and Oklahoma mesonet data assimilation on the analysis and prediction of tornadic mesovortices in an MCS. Mon. Wea. Rev., 139, 3422-3445, doi:10.1175/MWR-D-10-05051.1.
81. Seko, H., T. Kawabata, T. Tsuyuki, H. Nakamura, K. Koizumi, and T. Iwabuchi, 2004: Impacts of GPS-derived water vapor and radial wind measured by Doppler radar on numerical prediction of precipitation. J. Meteor. Soc. Japan, 82, 473-489, doi:10.2151/jmsj.2004.473.
82. Seko, H., T. Tsuyuki, K. Saito, and T. Miyoshi, 2013: Development of a two-way nested LETKF systemfor cloud-resolvingmodel. Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications, Vol. 2, S. K. Park and L. Xu, Eds., Springer, 489-507.
83. Seko, H., M. Kunii, S. Yokota, T. Tsuyuki, and T. Miyoshi, 2015: Ensemble experiments using a nested LETKF system to reproduce intense vortices associated with tornadoes of 6 May 2012 in Japan. Prog. Earth Planet. Sci., 2, 42, doi:10.1186/s40645-015-0072-3.
84. Shoji, Y., W. Mashiko, H. Yamauchi, and E. Sato, 2015: Estimation of local-scale precipitable water vapor distribution around each GNSS station using slant path delay: Evaluation of a tornado case using high-resolution NHM. SOLA, 11, 31-35, doi:10.2151/sola.2015-008.
85. Snook, N., M. Xue, and Y. 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.
86. Snook, N., M. Xue, and Y. 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.
87. Snyder, C., and F. Zhang, 2003: Assimilation of simulated Doppler radar observations with an ensemble Kalman filter. Mon. Wea. Rev., 131, 1663-1677, doi:10.1175//2555.1.
88. Sobash, R. A., and D. J. Stensrud, 2015: Assimilating surface mesonet observations with the EnKF to improve ensemble forecasts of convection initiation on 29 May 2012. Mon. Wea. Rev., 143, 3700-3725, doi:10.1175/MWR-D-14-00126.1.
89. Stensrud, D. J., and J. Gao, 2010: Importance of horizontally inhomogeneous environmental initial conditions to ensemble storm-scale radar data assimilation and very short-range forecasts. Mon. Wea. Rev., 138, 1250-1272, doi:10.1175/2009MWR3027.1.
90. Stensrud, D. J., and Coauthors, 2009: Convective-scale warn-on-forecast system. Bull. Amer. Meteor. Soc., 90, 1487-1499, doi:10.1175/2009BAMS2795.1.
91. Stensrud, D. J., and Coauthors, 2013: Progress and challenges with warnon-forecast. Atmos. Res., 123, 2-16, doi:10.1016/j.atmosres.2012.04.004.
92. Straka, J. M., E. N. Rasmussen, R. P. Davies-Jones, and P. M. Markowski, 2007: An observational and idealized numerical examination of low-level counter-rotating vortices in the rear flank of supercells. Electron. J. Severe Storms Meteor., 2 (8). [Available online at http://www.ejssm.org/ojs/index.php/ejssm/issue/view/11.]
93. Sun, J., and N. A. Crook, 1997: Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint. Part I: Model development and simulated data experiments. J. Atmos. Sci., 54, 1642-1661, doi:10.1175/1520-0469(1997)054<1642:DAMRFD>2.0.CO;2.
94. Tabary, P., G. Vulpiani, J. J. Gourley, A. J. Illingworth, R. J. Thompson, and O. Bousquet, 2009: Unusually high differential attenuation at C band: Results from a two-year analysis of the French Trappes polarimetric radar data. J. Appl. Meteor. Climatol., 48, 2037-2053, doi:10.1175/2009JAMC2039.1.
95. Tanamachi, R. L., L. J. Wicker, D. C. Dowell, H. B. Bluestein, D. T. Dawson II, and M. Xue, 2013: EnKF assimilation of high-resolution, mobile Doppler radar data of the 4 May 2007 Greensburg, Kansas, supercell into a numerical cloud model. Mon. Wea. Rev., 141, 625-648, doi:10.1175/MWR-D-12-00099.1.
96. Tong, M., and M. Xue, 2005: Ensemble Kalman filter assimilation of Doppler radar data with a compressible nonhydrostatic model: OSS experiments. Mon. Wea. Rev., 133, 1789-1807, doi:10.1175/MWR2898.1.
97. Torn, R. D., and G. J. Hakim, 2008: Ensemble-based sensitivity analysis. Mon. Wea. Rev., 136, 663-677, doi:10.1175/2007MWR2132.1.
98. Tromel, S., M. R. Kumjian, A. V. Ryzhkov, C. Simmer, and M. Diederich, 2013: Backscatter differential phase-Estimation and variability. J. Appl. Meteor. Climatol., 52, 2529-2548, doi:10.1175/JAMC-D-13-0124.1.
99. Wicker, L. J., and R. B. Wilhelmson, 1995: Simulation and analysis of tornado development and decay within a three-dimensional supercell thunderstorm. J. Atmos. Sci., 52, 2675-2703, doi:10.1175/1520-0469(1995)052<2675:SAAOTD>2.0.CO;2.
100. Xue, M., M. Tong, and K. K. Droegemeier, 2006: An OSSE framework based on the ensemble square root Kalman filter for evaluating the impact of data from radar networks on thunderstorm analysis and forecasting. J. Atmos. Oceanic Technol., 23, 46-66, doi:10.1175/JTECH1835.1.
101. Xue, M., M. Hu, and A. D. Schenkman, 2014: Numerical prediction of the 8 May 2003 Oklahoma city tornadic supercell and embedded tornado using ARPS with the assimilation of WSR-88D data. Wea. Forecasting, 29, 39-62, doi:10.1175/WAF-D-13-00029.1.
102. Yamauchi, H., O. Suzuki, and K. Akaeda, 2006: A hybrid multi-PRI method to dealias Doppler velocities. SOLA, 2, 92-95, doi:10.2151/sola.2006-024.
103. Yamauchi, H., H. Niino, O. Suzuki, Y. Shoji, E. Sato, A. Adachi, and W. Mashiko, 2013: Vertical structure of the Tsukuba F3 tornado on 6 May 2012 as revealed by a polarimetric radar. Preprints, 36th Conf. on Radar Meteorology, Breckenridge, CO, Amer. Meteor. Soc., 320. [Available online at https://ams. confex.com/ams/36Radar/webprogram/Paper228827.html.]
104. Yang, S.-C., M. Corazza, A. Carrassi, E. Kalnay, and T. Miyoshi, 2009: Comparison of local ensemble transform Kalman filter, 3DVAR, and 4DVAR in a quasigeostrophic model. Mon. Wea. Rev., 137, 693-709, doi:10.1175/2008MWR2396.1.
105. Yussouf, N., E. R. Mansell, L. J. Wicker, D. M. Wheatley, and D. J. Stensrud, 2013a: The ensemble Kalman filter analyses and forecasts of the 8 May 2003 Oklahoma City tornadic supercell storm using single-and double-moment microphysics schemes. Mon. Wea. Rev., 141, 3388-3412, doi:10.1175/MWR-D-12-00237.1.
106. Yussouf, N., J. Gao, D. J. Stensrud, and G. Ge, 2013b: The impact of mesoscale environmental uncertainty on the prediction of a tornadic supercell storm using ensemble data assimilation approach. Adv. Meteor., 2013, 1-15, doi:10.1155/2013/731647.
107. Zhang, F., C. Snyder, and J. Sun, 2004: Impacts of initial estimate and observation availability on convective-scale data assimilation with an ensemble Kalman filter. Mon.Wea. Rev., 132, 1238-1253, doi:10.1175/1520-0493(2004)132<1238:IOIEAO>2.0.CO;2.
108. Zhang, F., Z. Meng, and A. Aksoy, 2006: Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part I: Perfect model experiments. Mon. Wea. Rev., 134, 722-736, doi:10.1175/MWR3101.1.