Analysis of the 6 September 2015 Tornadic Storm Around the Tokyo Metropolitan Area Using Coupled 3DVAR and Incremental Analysis Updates
Ken-ichi Shimose†, Shingo Shimizu, Ryohei Kato, and Koyuru Iwanami
Storm, Flood and Landslide Research Division,
National Research Institute for Earth Science and Disaster Resilience (NIED)
3-1 Tennodai, Tsukuba, Ibaraki 305-0006, Japan
This study reports preliminary results from the three-dimensional variational method (3DVAR) with incremental analysis updates (IAU) of the surface wind field, which is suitable for real-time processing. In this study, 3DVAR with IAU was calculated for the case of a tornadic storm using 500-m horizontal grid spacing with updates every 10 min, for 6 h. Radial velocity observations by eight X-band multi-parameter Doppler radars and three Doppler lidars around the Tokyo Metropolitan area, Japan, were used for the analysis. In this study, three types of analyses were performed between 1800 to 2400 LST (local standard time: UTC + 9 h) 6 September 2015. The first used only 3DVAR (3DVAR), the second used 3DVAR with IAU (3DVAR+IAU), and the third analysis did not use data assimilation (CNTL). 3DVAR+IAU showed the best accuracy of the three analyses, and 3DVAR alone showed the worst accuracy, even though the background was updated every 10 min. Sharp spike signals were observed in the time series of wind speed at 10 m AGL, analyzed by 3DVAR, strongly suggesting that a “shock” was caused by dynamic imbalance due to the instantaneous addition of analysis increments to the background wind components. The spike signal was not shown in 3DVAR+IAU analysis, therefore, we suggest that the IAU method reduces the shock caused by the addition of analysis increments. This study provides useful information on the most suitable DA method for the real-time analysis of surface wind fields.
-  J. Maeda and E. Tomokiyo, “Tornado disaster 2012 in northern Kanto and the features of tornado disasters in Japan,” J. Disaster Res., Vol.8, pp. 1078-1083, 2013.
-  R. Okada, Y. Tamura, M. Matsui, and A. Yoshida, “Critical equivalent wind speeds for overturning and roof blow-off of 2-story wooden houses,” J. Disaster Res., Vol.8, pp. 1084-1089, 2013.
-  M. Noda and F. Nagao, “Wind speed of tornado to make a road damage,” J. Disaster Res., Vol.8, pp. 1090-1095, 2013.
-  J. M. Lewis, S. Lakshmivarahan, and S. K. Dhall, “Dynamic data assimilation: a least squares approach,” Cambridge University Press, 2006.
-  F. Rawlins, S. P. Ballard, K. J. Bovis, A. M. Clayton, D. Li, G. W. Inverarity, A. C. Lorenc, and T. J. Payne, “The Met Office global four-dimensional variational data assimilation scheme,” Q. J. R. Meteorol. Soc., Vol.133, pp. 347-362, 2007.
-  S. Skachko, Q. Errera, R. Menard, Y. Christophe, and S. Chabrillat, “Comparison of the ensemble Kalman filter and 4D-Var assimilation methods using a stratospheric tracer transport model,” Geosci. Model Dev., Vol.7, pp. 1451-1465, 2014.
-  J. Gao, T. M. Smith, D. J. Stensrud, C. Fu, K. Calhoun, K. L. Manross, J. Brogden, V. Lakshmanan, Y. Wang, K. W. Thomas, K. Brewster, and M. Xue, “A real-time weather-adaptive 3DVAR analysis system for severe weather detections and warnings,” Wea. Forecasting, Vol.28, pp. 727-745, 2013.
-  D. K. Lee, D. Y. Eom, J. W. Kim, and J. B. Lee, “High-resolution summer rainfall prediction in the JHWC real-time WRF system,” Aisia-Pacific J. Atmos. Sci., Vol.46, No.3, pp. 341-353, 2010.
-  K. M. Calhoun, T. M. Smith, D. M. Kingfield, J. Gao, and D. J. Stensrud, “Forecaster use and evaluation of real-time 3DVAR analyses during severe thunderstorm and tornado warning operations in the hazardous weather testbed,” Wea. Forecasting, Vol.29, pp. 601-612, 2014.
-  T. M. Smith, V. Lakshmanan, and C. Riedel, “Examination of a real-time 3DVAR analysis system in the hazardous weather testbed,” Wea. Forecasting, Vol.29, pp. 63-76, 2014.
-  N. Gustafsson, “Use of a digital filter as weak constraint in variational data assimilation,” Proc. Workshop on Variational Assimilation, with Special Emphasis on Three-Dimensional Aspects, Reading, United Kingdom, ECMWF, pp. 327-338, 1993.
-  S. C. Bloom, L. L. Takacs, A. M. da Silva, and D. Ledvina, “Data assimilation using incremental analysis updates,” Mon. Wea. Rev., Vol.124, pp. 1256-1271, 1996.
-  M. M. Rienecker, M. J. Suarez, R. Gelaro, R. Todling, J. Bacmeister, E. Liu, M. G. Bosilovich, S. D. Schubert, L. Takacs, G.-K. Kim, S. Bloom, J. Chen, D. Collins, A. Conaty, A. da Silva, W. Gu, J. Joiner, R. D. Koster, R. Lucchesi, A. Molod, T. Owens, S. Pawson, P. Pegion, C. R. Rdedder, R. Reichle, F. R. Robertson, A. G. Ruddick, M. Sienkiewicz, and J. Woollen, “MERRA: NASA’s modern-era retrospective analysis for research and applications,” J. Climate, Vol.24, pp. 3624-3648, 2011.
-  H. Tatebe, M. Ishii, T. Mochizuki, Y. Chikamato, T. T. Sakamato, Y. Komuro, M. Mori, S. Yasunaka, M. Watanabe, K. Ogochi, T. Suzuki, and T. Nishimura, “The initialization of the MIROC climate models with hydrographic data assimilation for decadal prediction,” J. Meteor. Soc. Japan, Vol.90A, pp. 275-293, 2012.
-  P. L. Houtekamer and F. Zhang, “Review of the ensemble Kalman filter for atmospheric data assimilation,” Mon. Wea. Rev., Vol.144, pp. 4489-4532, 2016.
-  K. A. Brewster, “Phase-correcting data assimilation and application to storm-scale numerical weather prediction. Part II: application to a severe storm outbreak,” Mon. Wea. Rev., Vol.131, pp. 493-507, 2003.
-  M. S. Lee, Y. H. Kuo, D. M. Barker, and E. Lim, “Incremental analysis updates initialization technique applied to 10-km MM5 and MM5 3DVAR,” Mon. Wea. Rev., Vol.134, pp. 1389-1404, 2006.
-  M. Dixon, Z. Li, H. Lean, N. Roberts, and S. Ballard, “Impact of data assimilation on forecasting convection over United Kingdom using high-resolution version of the Met Office unified model,” Mon. Wea. Rev., Vol.137, pp. 1562-1584, 2009.
-  J. H. Lee, H. H. Lee, Y. Choi, H. W. Kim, and D. K. Lee, “Radar data assimilation for the simulation of mesoscale convective system,” Adv. Atmos. Sci., Vol.27, No.5, pp. 1025-1042, 2010.
-  K. A. Brewster, F. H. Carr, K. W. Thomas, and D. R. Stratman, “Utilizing heterogeneous radar systems in a real-time high resolution analysis and short-term forecast system in the Dallas/Ft Worth testbed,” Preprints, 37th Conf. on Radar Meteor., Norman, OK, Amer. Meteor. Soc., 2015.
-  K. A. Brewster and D. R. Stratman, “Tuning an analysis and incremental analysis updating assimilation for an efficient high resolution forecast system,” Preprints, 20th Conf. on IAOS-AOLS, Amer. Meteor. Soc., 2016.
-  T. Maesaka, M. Maki, K. Iwanami, S. Tsuchiya, K. Kieda, and A. Hoshi, “Operational rainfall estimation by X-band MP radar network in MLIT, Japan,” Preprints, 35th Conf. on Radar Meteor., Pittsburgh, PA, Amer. Meteor. Soc., 2011.
-  K. Tsuboki and A. Sakakibara, “Large-scale parallel computing of cloud resolving storm simulator,” Lecture Notes in Computer Science, Vol.2327, High Performance Computing, ISHPC 2002, Springer, 2002.
-  M. Murakami, T. L. Clark, and W. D. Hall, “Numerical simulation of convective snow clouds over the Sea of Japan; two-dimensional simulations of mixed layer development and convective snow cloud formation,” J. Meteor. Soc. Japan, Vol.72, pp. 43-62, 1994.
-  J. W. Deardorff, “Three-dimensional numerical study of the height and mean structure of a heated planetary boundary layer,” Boundary-Layer Meteor., Vol.7, pp. 81-106, 1974.
-  J. W. Deardorff, “Stratocumulus-capped mixed layers derived from a three-dimensional model,” Boundary-Layer Meteor., Vol.18, pp. 495-527, 1980.
-  G. L. Mellor and T. Yamada, “A hierarchy of turbulence closure model for planetary boundary layers,” J. Atmos. Sci., Vol.31, pp. 1791-1805, 1974.
-  J. F. Louis, M. Tiedtke, and J. F. Geleyn, “A short history of the operational PBL parameterization at ECMWF,” Preprints, Workshop on Planetary Boundary Layer Parameterization, Shinfield Park, Reading, ECMWF, 1981.
-  J. Kondo, “Air-sea bulk transfer coefficients in diabatic conditions,” Boundary-Layer Meteor., Vol.9, pp. 91-112, 1975.
-  D. M. Barker, W. Huang, Y. R. Guo, and A. Bourgeois, “A three-dimensional variational (3DVAR) data assimilations system for use with MM5,” NCAR Tech. Note NCAR/TN-453 + STR, NCAR, 2003.
-  D. M. Barker, W. Huang, Y. R. Guo, A. J. Bourgeois, and Q. N. Xiao, “A three-dimensional variational data assimilation system for MM5: implementation and initial results,” Mon. Wea. Rev., Vol.132, pp. 897-914, 2004.
-  A. J. Koscielny, R. J. Doviak, and R. Rabin, “Statistical considerations in the estimation of divergence from single-Doppler radar and application of prestorm boundary-layer observations,” J. Appl. Meteor., Vol.21, pp. 197-210, 1982.
-  A. Lorenc, “Iterative analysis using covariance functions and filters,” Quart. J. Roy. Meteor. Soc., Vol.118, pp. 569-591, 1992.
-  C. M. Hayden and R. J. Purser, “Recursive filter objective analysis of meteorological fields: application to NESDIS operational processing,” J. Appl. Meteor., Vol.34, pp. 3-15, 1995.
-  R. J. Purser, W. S. Wu, D. F. Parrish, and N. M. Roberts, “Numerical aspects of the application of recursive filter to variational statistical analysis. Part I: spatially homogeneous and isotropic Gaussian covariance,” Mon. Wea. Rev., Vol.131, pp. 1524-1535, 2003.
-  D. F. Parrish and J. C. Derber, “The National Meteorological Center’s spectral statistical-interpolation analysis system,” Mon. Wea. Rev., Vol. 120, pp. 1747-1763, 1992.
-  T. Kawabata, H. Iwai, H. Seko, Y. Shoji, K. Saito, S. Ishii, and K. Mizutani, “Cloud-resolving 4D-var assimilation of Doppler wind lidar data on a meso-gamma-scale convective system,” Mon. Wea. Rev., Vol.142, pp. 4484-4498, 2014.
-  X. Zou, I. M. Navon, M. Berger, K. H. Phua, T. Schlick, and F. X. Le Dimet, “Numerical experience with limited-memory quasi-Newton and truncated Newton methods,” SIAM J. Optimization, Vol.3, pp. 582-608, 1993.
-  R. R. Rogers, “An extension of the ZR relation for Doppler radar,” Preprints, 11th Conf. on Radar Meteor., Boulder, CO, Amer. Meteor. Soc., pp. 158-161, 1964.
-  P. W. Baker and M. C. Hodson, “Effect of deviations from the Marshall-Palmer drop size distribution on the calculation of vertical air velocity by Rogers’s method,” Vol.24, pp. 495-498, 1985.
-  J. Gao and D. J. Stensrud, “Assimilation of reflectivity data in a convective-scale, cycled 3DVAR framework with hydrometeor classification,” J. Atmos. Sci., Vol.69, pp. 1054-1065, 2012.
-  K. Saito, T. Fujita, Y. Yamada, J. Ishida, Y. Kumagai, K. Aranami, S. Ohmori, R. Nagasawa, S. Kumagai, C. Muroi, T. Kato, H. Eito, and Y. Yamazaki, “The operational JMA nonhydrostatic mesoscale model,” Mon. Wea. Rev., Vol.134, pp. 1266-1298, 2006.