Low-Level Polarimetric Radar Signatures in EnKF Analyses and Forecasts of the May 8, 2003 Oklahoma City Tornadic Supercell: Impact of Multimoment Microphysics and Comparisons with Observation
The impact of increasing the number of predicted moments in a multimoment bulk microphysics scheme is investigated using ensemble Kalman filter analyses and forecasts of the May 8, 2003 Oklahoma City tornadic supercell storm and the analyses are validated using dual-polarization radar observations. The triple-moment version of the microphysics scheme exhibits the best performance, relative to the single- and double-moment versions, in reproducing the low- hail core and high- arc, as well as an improved probabilistic track forecast of the mesocyclone. A comparison of the impact of the improved microphysical scheme on probabilistic forecasts of the mesocyclone track with the observed tornado track is also discussed. 1. Introduction The assimilation of radar data into storm scale models using the Ensemble Kalman Filter (EnKF) [1] approach has proven to be an extremely useful tool for the analysis and prediction of convective storms in recent years. There have been many recent successful uses of this approach for both analyses [2–7] and short-range forecasts [8–10] based on these analyses. In general, these studies have focused on improving techniques for assimilation of radar data, on the design of the overall data assimilation system, or on the impact of initial and boundary conditions. High-resolution numerical weather prediction has progressed during the past decade such that prediction of the dynamics of individual convective storms is now routinely attempted. One substantial challenge is the improvement and validation of the microphysics parameterization and the associated impacts on storm structure and behavior (e.g., through the development of the cold pool). Errors from the model’s microphysical parameterization can significantly impact forecasts of these storms. Polarimetric radar observations offer a rich source of data to validate the output of such schemes within this context. Several storm-scale simulation studies have shown that the microphysics parameterization has a profound impact on simulated storm structure and behavior [11–17] and even on tornadic potential [18]. Here, we restrict our discussion to bulk microphysics schemes, which assume a priori a certain functional form for the underlying drop or particle size distribution (DSD/PSD) for several hydrometeor categories. Typically, one or more moments of the PSD for a given category are explicitly predicted within a scheme, with single-moment schemes that predict the mass mixing ratio (proportional to the third moment) being the most common. Double-moment schemes that typically predict
References
[1]
G. Evensen, “Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics,” Journal of Geophysical Research, vol. 99, no. 5, pp. 10143–10162, 1994.
[2]
A. Aksoy, D. C. Dowell, and C. Snyder, “A multicase comparative assessment of the Ensemble Kalman filter for assimilation of radar observations. Part I: storm-scale analyses,” Monthly Weather Review, vol. 137, no. 6, pp. 1805–1824, 2009.
[3]
D. C. Dowell, L. J. Wicker, and C. Snyder, “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, vol. 139, no. 1, pp. 272–294, 2011.
[4]
R. L. Tanamachi, L. J. Wicker, D. C. Dowell, H. B. Bluestein, D. T. Dawson, and M. Xue, “EnKF assimilation of high-resolution, mobile doppler radar data of the 4 May 2007 Greensburg, Kansas, supercell into a numerical cloud model,” Monthly Weather Review, vol. 141, no. 2, pp. 625–648, 2012.
[5]
J. Marquis, Y. Richardson, P. Markowski, D. Dowell, and J. Wurman, “Tornado maintenance investigated with high-resolution dual-doppler and EnKF analysis,” Monthly Weather Review, vol. 140, no. 1, pp. 3–27, 2012.
[6]
Y. Jung, M. Xue, and M. Tong, “Ensemble Kalman filter analyses of the 29-30 May 2004 Oklahoma tornadic thunderstorm using one- and two-moment bulk microphysics schemes, with verification against polarimetric radar data,” Monthly Weather Review, vol. 140, no. 5, pp. 1457–1475, 2012.
[7]
D. Dowell, F. Zhang, L. J. Wicker, C. Snyder, and N. A. Crook, “Wind and temperature retrievals in the 17 May 1981 Arcadia, Oklahoma supercell: ensemble Kalman filter experiments,” Monthly Weather Review, vol. 132, pp. 1982–2005, 2004.
[8]
D. T. Dawson, L. J. Wicker, E. R. Mansell, and R. L. Tanamachi, “Impact of the environmental low-level wind profile on ensemble forecasts of the 4 may 2007 Greensburg, Kansas, tornadic storm and associated mesocyclones,” Monthly Weather Review, vol. 140, no. 2, pp. 696–716, 2012.
[9]
A. Aksoy, D. C. Dowell, and C. Snyder, “A multicase comparative assessment of the ensemble Kalman filter for assimilation of radar observations. Part II: short-range ensemble forecasts,” Monthly Weather Review, vol. 138, no. 4, pp. 1273–1292, 2010.
[10]
N. Yussouf, E. R. Mansell, L. J. Wicker, D. M. Wheatley, and D. J. Stensrud, “The ensemble kalman filter analyses and forecasts of the 8 May 2003 Oklahoma city tornadic supercell storm using single and double moment microphysics schemes,” Monthly Weather Review, 2013.
[11]
J. A. Milbrandt and M. K. Yau, “A multimoment bulk microphysics parameterization. Part IV: sensitivity experiments,” Journal of the Atmospheric Sciences, vol. 63, no. 12, pp. 3137–3159, 2006.
[12]
D. T. Dawson II, M. Xue, J. A. Milbrandt, and M. K. Yau, “Comparison of evaporation and cold pool development between single-moment and multimoment bulk microphysics schemes in idealized simulations of tornadic thunderstorms,” Monthly Weather Review, vol. 138, no. 4, pp. 1152–1171, 2010.
[13]
H. Morrison and J. Milbrandt, “Comparison of two-moment bulk microphysics schemes in idealized supercell thunderstorm simulations,” Monthly Weather Review, vol. 139, no. 4, pp. 1103–1130, 2011.
[14]
M. S. Gilmore, J. M. Straka, and E. N. Rasmussen, “Precipitation uncertainty due to variations in precipitation particle parameters within a simple microphysics scheme,” Monthly Weather Review, vol. 132, no. 11, pp. 2610–2627, 2004.
[15]
S. C. van den Heever and W. R. Cotton, “The impact of hail size on simulated supercell storms,” Journal of the Atmospheric Sciences, vol. 61, pp. 1596–1609, 2004.
[16]
H. Morrison, G. Thompson, and V. Tatarskii, “Impact of cloud microphysics on the development of trailing stratiform precipitation in a simulated squall line: comparison of one- and two-moment schemes,” Monthly Weather Review, vol. 137, no. 3, pp. 991–1007, 2009.
[17]
H. Morrison, S. A. Tessendorf, K. Ikeda, and G. Thompson, “Sensitivity of a simulated midlatitude squall line to parameterization of raindrop breakup,” Monthly Weather Review, vol. 140, no. 8, pp. 2437–2460, 2012.
[18]
N. Snook and M. Xue, “Effects of microphysical drop size distribution on tornadogenesis in supercell thunderstorms,” Geophysical Research Letters, vol. 35, no. 24, Article ID L24803, 2008.
[19]
H. Morrison, J. A. Curry, and V. I. Khvorostyanov, “A new double-moment microphysics parameterization for application in cloud and climate models. Part I: description,” Journal of the Atmospheric Sciences, vol. 62, no. 6, pp. 1665–1677, 2005.
[20]
E. R. Mansell, C. L. Ziegler, and E. C. Bruning, “Simulated electrification of a small thunderstorm with two-moment bulk microphysics,” Journal of the Atmospheric Sciences, vol. 67, no. 1, pp. 171–194, 2010.
[21]
G. Thompson, P. R. Field, R. M. Rasmussen, and W. D. Hall, “Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: implementation of a new snow parameterization,” Monthly Weather Review, vol. 136, no. 12, pp. 5095–5115, 2008.
[22]
K.-S. S. Lim and S.-Y. Hong, “Development of an effective double-moment cloud microphysics scheme with prognostic cloud condensation nuclei (CCN) for weather and climate models,” Monthly Weather Review, vol. 138, no. 5, pp. 1587–1612, 2010.
[23]
J. A. Milbrandt and M. K. Yau, “A multimoment bulk microphysics parameterization. Part II: a proposed three-moment closure and scheme description,” Journal of the Atmospheric Sciences, vol. 62, no. 9, pp. 3065–3081, 2005.
[24]
N. Balakrishnan and D. S. Zrnic, “Use of polarization to characterize precipitation and discriminate large hail,” Journal of the Atmospheric Sciences, vol. 47, no. 13, pp. 1525–1540, 1990.
[25]
P. H. Herzegh and A. R. Jameson, “Observing precipitation through dual-polarization radar measurements,” Bulletin of the American Meteorological Society, vol. 73, no. 9, pp. 1365–1374, 1992.
[26]
A. V. Ryzhkov and D. S. Zrnic, “Discrimination between rain and snow with a polarimetric radar,” Journal of Applied Meteorology, vol. 37, no. 10, pp. 1228–1240, 1998.
[27]
D. S. Zrnic and A. V. Ryzhkov, “Polarimetry for weather surveillance radars,” Bulletin of the American Meteorological Society, vol. 80, no. 3, pp. 389–406, 1999.
[28]
J. M. Straka, D. S. Zrnic, and A. V. Ryzhkov, “Bulk hydrometeor classification and quantification using polarimetric radar data: synthesis of relations,” Journal of Applied Meteorology, vol. 39, no. 8, pp. 1341–1372, 2000.
[29]
V. N. Bringi and V. Chandrasekar, Polarimetric Doppler Weather Radar: Principles and Applications, Cambridge University Press, Cambridge, UK, 2001.
[30]
D. S. Zrnic, A. Ryzhkov, J. Straka, Y. Liu, and J. Vivekanandan, “Testing a procedure for automatic classification of hydrometeor types,” Journal of Atmospheric and Oceanic Technology, vol. 18, no. 6, pp. 892–913, 2001.
[31]
S. A. Tessendorf, L. J. Miller, K. C. Wiens, and S. A. Rutledge, “The 29 June 2000 supercell observed during STEPS. Part I: kinematics and microphysics,” Journal of the Atmospheric Sciences, vol. 62, no. 12, pp. 4127–4150, 2005.
[32]
P. L. Heinselman and A. V. Ryzhkov, “Validation of polarimetric hail detection,” Weather and Forecasting, vol. 21, no. 5, pp. 839–850, 2006.
[33]
H. S. Park, A. V. Ryzhkov, D. S. Zrni？, and K.-E. Kim, “The hydrometeor classification algorithm for the polarimetric WSR-88D: description and application to an MCS,” Weather and Forecasting, vol. 24, no. 3, pp. 730–748, 2009.
[34]
Y. Jung, M. Xue, and G. Zhang, “Simulations of polarimetric radar signatures of a supercell storm using a two-moment bulk microphysics scheme,” Journal of Applied Meteorology and Climatology, vol. 49, no. 1, pp. 146–163, 2010.
[35]
M. R. Kumjian and A. V. Ryzhkov, “Polarimetric signatures in supercell thunderstorms,” Journal of Applied Meteorology and Climatology, vol. 47, no. 7, pp. 1940–1961, 2008.
[36]
M. Hu and M. Xue, “Impact of configurations of rapid intermittent assimilation of WSR-88D radar data for the 8 May 2003 Oklahoma City tornadic thunderstorm case,” Monthly Weather Review, vol. 135, no. 2, pp. 507–525, 2007.
[37]
G. S. Romine, D. W. Burgess, and R. B. Wilhelmson, “A dual-polarization-radar-based assessment of the 8 May 2003 Oklahoma city area tornadic supercell,” Monthly Weather Review, vol. 136, no. 8, pp. 2849–2870, 2008.
[38]
D. C. Dowell and L. J. Wicker, “Additive noise for storm-scale ensemble data assimilation,” Journal of Atmospheric and Oceanic Technology, vol. 26, no. 5, pp. 911–927, 2009.
[39]
J. A. Milbrandt and M. K. Yau, “A multimoment bulk microphysics parameterization. Part I: analysis of the role of the spectral shape parameter,” Journal of the Atmospheric Sciences, vol. 62, no. 9, pp. 3051–3064, 2005.
[40]
L. J. Wicker and R. B. Wilhelmson, “Simulation and analysis of tornado development and decay within a three-dimensional supercell thunderstorm,” Journal of the Atmospheric Sciences, vol. 52, no. 15, pp. 2675–2703, 1995.
[41]
M. C. Coniglio, D. J. Stensrud, and L. J. Wicker, “Effects of upper-level shear on the structure and maintenance of strong quasi-linear mesoscale convective systems,” Journal of the Atmospheric Sciences, vol. 63, no. 4, pp. 1231–1252, 2006.
[42]
J. Sun and N. A. Crook, “Real-time low-level wind and temperature analysis using single WSR-88D data,” Weather and Forecasting, vol. 16, no. 1, pp. 117–132, 2001.
[43]
C. L. Ziegler, “Retrieval of thermal and microphysical variables in observed convective storms. Part 1: model development and preliminary testing,” Journal of the Atmospheric Sciences, vol. 42, no. 14, pp. 1487–1509, 1985.
[44]
E. R. Mansell, “On sedimentation and advection in multimoment bulk microphysics,” Journal of the Atmospheric Sciences, vol. 67, no. 9, pp. 3084–3094, 2010.
[45]
J. A. Milbrandt and R. Mctaggart-Cowan, “Sedimentation-induced errors in bulk microphysics schemes,” Journal of the Atmospheric Sciences, vol. 67, no. 12, pp. 3931–3948, 2010.
[46]
U. Wacker and A. Seifert, “Evolution of rain water profiles resulting from pure sedimentation: spectral vs. parameterized description,” Atmospheric Research, vol. 58, no. 1, pp. 19–39, 2001.
[47]
C. W. Ulbrich, “Natural variations in the analytical form of the raindrop size distribution,” Journal of Climate & Applied Meteorology, vol. 22, no. 10, pp. 1204–1215, 1983.
[48]
D. T. Dawson II, E. R. Mansell, Y. Jung, L. J. Wicker, M. R. Kumjian, and M. Xue, “Low-level ZDR signatures in supercell forward flanks: the role of size sorting and melting of hail,” Journal of the Atmospheric Sciences, 2013.
[49]
E. R. Mansell and C. L. Ziegler, “Aerosol effects on simulated storm electrification and precipitation in a two-moment bulk microphysics model,” Journal of the Atmospheric Sciences, vol. 70, no. 7, pp. 2032–2050, 2013.
[50]
P. C. Waterman, “Scattering by dielectric obstacles,” Alta Frequenza, vol. 38, pp. 348–352, 1969.
[51]
J. Vivekanandan, W. M. Adams, and V. N. Bringi, “Rigorous approach to polarimetric radar modeling of hydrometeor orientation distributions,” Journal of Applied Meteorology, vol. 30, no. 8, pp. 1053–1063, 1991.
[52]
M. I. Mishchenko, “Calculation of the amplitude matrix for a nonspherical particle in a fixed orientation,” Applied Optics, vol. 39, no. 6, pp. 1026–1031, 2000.
[53]
R. M. Rasmussen, V. Levizzani, and H. R. Pruppacher, “A wind tunnel and theoretical study of the melting behavior of atmospheric ice particles: III: experiment and theory for spherical ice particles of radius >500？μm,” Journal of the Atmospheric Sciences, vol. 41, no. 3, pp. 381–388, 1984.
[54]
M. R. Kumjian and A. V. Ryzkhov, “Storm-relative helicity revealed from polarimetric radar measurements,” Journal of the Atmospheric Sciences, vol. 66, no. 3, pp. 667–685, 2009.
[55]
M. R. Kumjian and A. V. Ryzhkov, “The impact of size sorting on the polarimetric radar variables,” Journal of the Atmospheric Sciences, vol. 69, no. 6, pp. 2042–2060, 2012.
[56]
D. J. Stensrud and J. Gao, “Importance of horizontally inhomogeneous environmental initial conditions to ensemble storm-scale radar data assimilation and very short-range forecasts,” Monthly Weather Review, vol. 138, no. 4, pp. 1250–1272, 2010.
[57]
D. J. Stensrud, X. Ming, L. J. Wicker et al., “Convective-scale warn-on-forecast system: a vision for 2020,” Bulletin of the American Meteorological Society, vol. 90, no. 10, pp. 1487–1499, 2009.
[58]
J. D. Hunter, “Matplotlib: a 2D graphics environment,” Computing in Science and Engineering, vol. 9, no. 3, pp. 90–95, 2007.
