Ensemble kalman filter assimilation of radar observations of the 8 may 2003 oklahoma city supercell: Influences of reflectivity observations on storm-scale analyses

Monthly Weather Review

Volumn 139, Issue 1, 2011, Pages 272-294

  Dowell, David C ^a,b,c   Wicker, Louis J ^b   Snyder, Chris ^c  

^a UNIVERSITY OF OKLAHOMA   (United States)

^b NATIONAL SEVERE STORMS LABORATORY   (United States)

^c NATIONAL CENTER FOR ATMOSPHERIC RESEARCH   (United States)

Author keywords

Data assimilation; Ensembles; Kalman filters; Radar observations; Supercells

Indexed keywords

ASSIMILATION ALGORITHMS; ATMOSPHERIC SIMULATIONS; BIAS ERROR; CLOUD MODELS; CLOUD WATER; COLD POOL; COLLABORATIVE MODEL; CONVECTIVE STORMS; DATA ASSIMILATION; DOPPLER; ENSEMBLE KALMAN FILTER; ENSEMBLES; HORIZONTAL GRID SPACING; HORIZONTAL WINDS; LOCAL PERTURBATION; LOW LEVEL; MIXING RATIOS; MODEL VARIABLES; MULTISCALES; NATIONAL SEVERE STORMS LABORATORIES; OBSERVATION ERRORS; OKLAHOMA CITY; RADAR OBSERVATIONS; SCALE ANALYSIS; SPACE EXPERIMENTS; SPATIAL PATTERNS; STATE ESTIMATES; SUPER CELL; SUPERCELL THUNDERSTORMS; TEMPERATURE ANALYSIS; VERTICAL VELOCITY; WATER VAPOR FIELDS; WIND PROFILES;

COMPUTER SIMULATION; DATA PROCESSING; ERRORS; EXPERIMENTS; KALMAN FILTERS; LAKES; RADAR; RAIN; STATISTICS; THUNDERSTORMS; TOOLS; VELOCITY; WATER VAPOR;

REFLECTION;

COLD POOL; CONVECTIVE SYSTEM; DATA ASSIMILATION; DOPPLER RADAR; ENSEMBLE FORECASTING; KALMAN FILTER; MIXING RATIO; SIZE DISTRIBUTION; STORM; SUPERCELL; THUNDERSTORM; WIND PROFILE;

OKLAHOMA [OKLAHOMA (STT)]; OKLAHOMA [UNITED STATES]; UNITED STATES;

EID: 79951643932     PISSN: 00270644     EISSN: None     Source Type: Journal    
DOI: 10.1175/2010MWR3438.1     Document Type: Article

References (66)

1. Aksoy, A., D. C. Dowell, and C. Snyder, 2009: A multicase comparative assessment of the ensemble Kalman filter for assimilation of radar observations. Part I: Storm-scale analyses. Mon. Wea. Rev., 137, 1805-1824.
2. 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.
3. Anderson, J. L., and N. Collins, 2007: Scalable implementations of ensemble filter algorithms for data assimilation. J. Atmos. Oceanic Technol., 24, 1452-1463.
4. Annan, J. D., D. J. Lunt, J. C. Hargreaves, and P. J. Valdes, 2005: Parameter estimation in an atmospheric GCM using the ensemble Kalman filter. Nonlinear Processes Geophys., 12, 363-371.
5. Browning, K. A., 1968: The organization of severe local storms. Weather, 23, 429-434.
6. Bryan, G. H., J. C. Wyngaard, and J. M. Fritsch, 2003: Resolution requirements for the simulation of deep moist convection. Mon. Wea. Rev., 131, 2394-2416.
7. Burgess, D. W., 2004: High resolution analyses of the 8 May 2003 Oklahoma City storm. Part I: Storm structure and evolution from radar data. Preprints, 22nd Conf. on Severe Local Storms, Hyannis, MA, Amer. Meteor. Soc., 12.4. [Available online at http://ams.confex.com/ams/pdfpapers/81939.pdf.]
8. 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.
9. Coniglio, M. C., D. J. Stensrud, and L. J. Wicker, 2006: Effects of upper-level shear on the structure and maintenance of strong quasi-linear mesoscale convective systems. J. Atmos. Sci., 63, 1231-1252.
10. Cressman, G. P., 1959: An operational objective analysis system. Mon. Wea. Rev., 87, 367-374.
11. 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.
12. Dee, D. P., 2005: Bias and data assimilation. Quart. J. Roy. Meteor. Soc., 131, 3323-3343.
13. Doviak, R. J., and D. S. Zrnic, 1993: Doppler Radar and Weather Observations. 2nd ed. Academic Press, 562 pp.
14. Dowell, D. C., and L. J. Wicker, 2009: Additive noise for stormscale ensemble data assimilation. J. Atmos. Oceanic Technol., 26, 911-927.
15. Dowell, D. C., and L. J. Wicker, and J. D. Stensrud, 2004a: High resolution analyses of the 8 May 2003 Oklahoma City storm. Part II: EnKF data assimilation and forecast experiments. Preprints, 22nd Conf. on Severe Local Storms, Hyannis, MA, Amer. Meteor. Soc., 12.5. [Available online at http://ams.confex.com/ams/11aram22sls/techprogram/paper_81393.htm.]
16. Dowell, D. C., F. Zhang, L. J. Wicker, C. Snyder, and N. A. Crook, 2004b: Wind and temperature retrievals in the 17 May 1981 Arcadia, Oklahoma, supercell: Ensemble Kalman filter experiments. Mon. Wea. Rev., 132, 1982-2005.
17. Evensen, G., 1994: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte-Carlo methods to forecast error statistics. J. Geophys. Res., 99 (C5), 10 143-10 162.
18. Fujita, T. T., 1981: Tornadoes and downbursts in the context of generalized planetary scales. J. Atmos. Sci., 38, 1511-1534.
19. Gao, J., and M. Xue, 2008: An efficient dual-resolution approach for ensemble data assimilation and tests with simulated Doppler radar data. Mon. Wea. Rev., 136, 945-963.
20. Gaspari, G., and S. E. Cohn, 1999: Construction of correlation functions in two and three dimensions. Quart. J. Roy. Meteor. Soc., 125, 723-757.
21. Gilmore, M. S., J. M. Straka, and E. N. Rasmussen, 2004a: Precipitation and evolution sensitivity in simulated deep convective storms: Comparisons between liquid-only and simple ice and liquid phase microphysics. Mon. Wea. Rev., 132, 1897-1916.
22. Gilmore, M. S., J. M. Straka, and E. N. Rasmussen, 2004b: Precipitation uncertainty due to variations in precipitation particle parameters within a simple microphysics scheme. Mon. Wea. Rev., 132, 2610-2627.
23. Hamill, T. M., J. S. Whitaker, and C. Snyder, 2001: Distancedependent filtering of background error covariance estimates in an ensemble Kalman filter.Mon. Wea. Rev., 129, 2776-2790.
24. Houtekamer, P. L., and H. L. Mitchell, 1998: Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev., 126, 796-811.
25. Houtekamer, P. L., and H. L. Mitchell, 2001: A sequential ensemble Kalman filter for atmospheric data assimilation. Mon. Wea. Rev., 129, 123-137.
26. 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.
27. Hu, M., M. Xue, J. Gao, and K. Brewster, 2006: 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.
28. Jung, Y., G. Zhang, and M. Xue, 2008a: 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.
29. Jung, Y., M. Xue, G. Zhang, and J. M. Straka, 2008b: 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.
30. 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.
31. Kessler, E., III, 1969: On the Distribution and Continuity of Water Substance in Atmospheric Circulation. Meteor. Monogr., No. 32, Amer. Meteor. Soc., 84 pp.
32. Lei, T., M. Xue, and T. Yu, 2009: Multi-scale analysis and prediction of the 8 May 2003 Oklahoma City tornadic supercell storm assimilating radar and surface network data using EnKF. Preprints, 13th Conf. on Integrated Observing and Assimilation Systems for Atmosphere, Oceans, and Land Surface (IOASAOLS), Phoenix, AZ, Amer. Meteor. Soc., 6.4. [Available online at http://ams.confex.com/ams/89annual/techprogram/paper_150404.htm.]
33. 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.
34. Lorenc, A. C., 2003: The potential of the ensemble Kalman filter for NWP-Acomparison with 4D-Var. Quart. J. Roy. Meteor. Soc., 129, 3183-3203.
35. Lu, H., and Q. Xu, 2009: Trade-offs between measurements accuracy and resolutions in configuring phased-array radar velocity scans for ensemble-based storm-scale data assimilation. J. Appl. Meteor. Climatol., 48, 1230-1244.
36. Marshall, J. S., and W. M. Palmer, 1948: The distribution of raindrops with size. J. Meteor., 5, 165-166.
37. 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.
38. Morrison, H., and W. W. Grabowski, 2008: A novel approach for representing ice microphysics in models: Description and tests using a kinematic framework. J. Atmos. Sci., 65, 1528-1548.
39. Romine, G. S., D. W. Burgess, and R. B. Wilhelmson, 2008: A dual-polarization radar-based assessment of the 8May 2003 Oklahoma City area tornadic supercell. Mon. Wea. Rev., 136, 2849-2870.
40. Ryzhkov, A. V., T. J. Schuur, D. W. Burgess, and D. S. Zrnic, 2005: Polarimetric tornado detection. J. Appl. Meteor., 44, 557-570.
41. Seifert, A., and K. D. Beheng, 2001: A double-moment parameterization for simulating autoconversion, accretion and selfcollection. Atmos. Res., 59, 265-281.
42. Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, W. Wang, and J. G. Powers, 2005:Adescription of the Advanced ResearchWRF version 2. NCARTech. Note NCAR/TN-4681STR, 88 pp.
43. Smith, P. L., 1984: Equivalent radar reflectivity factors for snow and ice particles. J. Climate Appl. Meteor., 23, 1258-1260.
44. Smith, P. L., C. G. Myers, and H. D. Orville, 1975: Radar reflectivity factor calculations in numerical cloud models using bulk parameterization of precipitation. J. Appl. Meteor., 14, 1156-1165.
45. Snyder, C., and F. Zhang, 2003: Assimilation of simulated Doppler radar observations with an ensemble Kalman filter. Mon. Wea. Rev., 131, 1663-1677.
46. Stout, G. E., and F. A. Huff, 1953: Radar records Illinois tornadogenesis. Bull. Amer. Meteor. Soc., 34, 281-284.
47. 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.
48. Sun, J., and N. A. Crook, 1998: Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint. Part II: Retrieval experiments of an observed Florida convective storm. J. Atmos. Sci., 55, 835-852.
49. Sun, J., and N. A. Crook, 2001: Real-time low-level wind and temperature analysis using single WSR-88D data. Wea. Forecasting, 16, 117-132.
50. 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.
51. Tong, M., and M. Xue, 2008a: Simultaneous estimation of microphysical parameters and atmospheric state with simulated radar data and ensemble square root Kalman filter. Part I: Sensitivity analysis and parameter identifiability. Mon. Wea. Rev., 136, 1630-1648.
52. Tong, M., and M. Xue, 2008b: Simultaneous estimation of microphysical parameters and atmospheric state with simulated radar data and ensemble square root Kalman filter. Part II: Parameter estimation experiments. Mon. Wea. Rev., 136, 1649-1668.
53. Ulbrich, C. W., and L. G. Lee, 1999: Rainfall measurement error by WSR-88D radars due to variations in Z-R law parameters and the radar constant. J. Atmos. Oceanic Technol., 16, 1017-1024.
54. van Tassel, E. L., 1955: The North Platte Valley tornado outbreak of June 27, 1955. Mon. Wea. Rev., 83, 255-264.
55. Weisman, M. L., and J. B. Klemp, 1984: The structure and classification of numerically simulated convective storms in directionally varying wind shears. Mon. Wea. Rev., 112, 2479-2498.
56. Whitaker, J. S., and T. M. Hamill, 2002: Ensemble data assimilation without perturbed observations. Mon. Wea. Rev., 130, 1913-1924.
57. Wicker, L. J., 1996: The role of near surface wind shear on low-level mesocyclone generation and tornadoes. Preprints, 18th Conf. on Severe Local Storms, San Francisco, CA, Amer. Meteor. Soc., 115-119.
58. Wicker, L. J., and W. C. Skamarock, 2002: Time-splitting methods for elastic models using forward time schemes. Mon. Wea. Rev., 130, 2088-2097.
59. Wilson, J. W., and E. A. Brandes, 1979: Radar measurement of rainfall-A summary. Bull. Amer. Meteor. Soc., 60, 1048-1058.
60. Xu, Q., H. Lu, S. Gao, M. Xue, and M. Tong, 2008: Time-expanded sampling for ensemble Kalman filter: Assimilation experiments with simulated radar observations. Mon. Wea. Rev., 136, 2651-2667.
61. Xue, M., K. K. Droegemeier, and V. Wong, 2000: The Advanced Regional Prediction System (ARPS)-A multiscale nonhydrostatic atmospheric simulation and prediction tool. Part I: Model dynamics and verification. Meteor. Atmos. Phys., 75, 161-193.
62. 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, 143-165.
63. Xue, M, M. Tong, and G. Zhang, 2009: Simultaneous state estimation and attenuation correction for thunderstorms with radar data using an ensemble Kalman filter: Tests with simulated data. Quart. J. Roy. Meteor. Soc., 135, 1409-1423.
64. Yussouf, N., and D. J. Stensrud, 2010: Impact of phased-array radar observations over a short assimilation period: Observing system simulation experiments using an ensemble Kalman filter. Mon. Wea. Rev., 138, 517-538.
65. Zhang, F., C. Snyder, and J. Sun, 2004: Impacts of initial estimate and observations on convective-scale data assimilation with an ensemble Kalman filter. Mon. Wea. Rev., 132, 1238-1253.
66. Zhang, F, Y. Weng, J. A. Sippel, Z. Meng, and C. H. Bishop, 2009: Cloud-resolving hurricane initialization and prediction through assimilation of Doppler radar observations with an ensemble Kalman filter. Mon. Wea. Rev., 137, 2105-2125.