Development of a Two-way Nested LETKF System for Cloud-resolving Model

  • Hiromu SekoEmail author
  • Tadashi Tsuyuki
  • Kazuo Saito
  • Takemasa Miyoshi


A two-way nested Local Ensemble Transform Kalman Filter (LETKF) system has been developed to improve the accuracy of numerical forecasts on local heavy rainfalls. In this system, mesoscale convergence which drives local heavy rainfalls, is first reproduced by the LETKF with a grid interval of 15 km (Outer LETKF) which assimilates conventional data. The convection cells associated with the local heavy rainfall are then reproduced by the higher resolution LETKF with a grid interval of 1.875 km (Inner LETKF) which assimilates local data. The boundary conditions of the Inner LETKF are given by the forecast of the Outer LETKF. To consider the upward cascade effect from storm scale to mesoscale, the forecast results of the Inner LETKF are reflected into the Outer LETKF every 6 h.This system was applied to a thunderstorm that caused a local heavy rainfall event on the Osaka Plain on 5th September 2008. The rainfall distributions similar to the observed ones were reproduced in a few ensemble members of the Inner LETKF, although the observed scattered convection cells were expressed as weak rainfall regions in the Outer LETKF. When the precipitable water vapor or slant-path water vapor data obtained by GPS and horizontal wind or radial wind data observed by Doppler radars were assimilated in the Inner LETKF, the number of ensemble forecasts, which reproduced the local heavy rainfall, increased. The experiments on the small-scale disturbances in the initial seeds of the Inner LETKF and on the initial conditions produced by the no-cost smoother showed that these improvements might enhance the accuracy of local heavy rainfall forecasts.


Ensemble Member Horizontal Wind Convection Cell Ensemble Forecast Doppler Radar 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors would like to express their gratitude to Dr. Yoshinori Shoji of Meteorological Research Institute, Dr. Tadashi Fujita of Numerical Prediction Division/JMA, Mr. Yuji Esaki of Osaka District Meteorological Observatory and anonymous reviewers, who provided the PWV and SWV data and their useful comments. The authors’ gratitude extends to the Geospatial Information Authority of Japan and the Osaka District Meteorological Observatory of JMA, which provided the GPS data and Doppler radar data. The improvements of severe weather forecasts (i.e. local heavy rainfalls), which were achieved by the assimilations of Doppler radar data, will contribute to aviation safety and the mitigation of damages of other urban functions. The “Kasaneru 3D tool” developed by Tokyo District Meteorological Observatory of JMA was used to generate the rainfall distribution graphics observed by the operational radars and the surface meteorological data distribution graphics. This study was performed by the “Studies on formation process of line-shaped rainfall systems and predictability of rainfall intensity and moving speed” and the “Study of advanced data assimilation and cloud resolving ensemble technique for prediction of local heavy rainfall” projects.


  1. Dowell DC, Zhang F, Wicker LJ, Snyder C, Crook NA (2004) Wind and temperature retrievals in the 17 May 1981 Arcadia, Oklahoma, Supercell: ensemble Kalman filter experiments. Mon Wea Rev 132:1982–2005CrossRefGoogle Scholar
  2. Evensen G (2003) The ensemble Kalman filter: theoretical formulation and practical implementation. Ocean Dyn 53:343–367CrossRefGoogle Scholar
  3. Fujita T, Tohru K, Origuchi S, Seko H, Saito K (2011) Development of Meso-LETKF. In: Proceedings of the autumn conference of meteorological society Japan B213:(in Japanese)Google Scholar
  4. Hunt BR, Kostelich EJ, Szunyogh I (2007) Efficient data assimilation for spatiotemporal chaos: a local ensemble transform Kalman filter. Physica D 230:112–126CrossRefGoogle Scholar
  5. Kalnay E, Li H, Miyoshi T, Yang S-C, Ballabrera-Poy J (2007) Response to the discussion on “4-D-Var or EnKF?” by Nils Gustafsson. Tellus 59A:778–780Google Scholar
  6. Kawabata T, Seko H, Saito K, Kuroda T, Tamiya K, Tsuyuki T, Honda Y, Wakazuki Y (2007) An assimilation and forecasting experiment of the nerima heavy rainfa11 with a cloud-resolving nonhydrostatic 4-dimensional variational data assimilation system. J Meteor Soc Jpn 85: 255–276CrossRefGoogle Scholar
  7. Meng Z, Zhang F (2008) Test 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: 522–540CrossRefGoogle Scholar
  8. Miyoshi T, Aranami K (2006) Applying a four-dimensional local ensemble transform Kalman filter (4D-LETKF) to the JMA nonhydrostatic model (NHM). SOLA 2:128–131CrossRefGoogle Scholar
  9. Miyoshi T, Kunii M (2012) The local ensemble transform Kalman filter with the weather research and forecasting model: experiments with real observations. Pure Appl Geophys 169:321–333CrossRefGoogle Scholar
  10. Saito K, Fujita T, Yamada Y, Ishida J, Kumagai Y, Aranami K, Ohmori S, Nagasawa R, Kumagai S, Muroi C, Kato T, Eito H, Yamazaki Y (2006) The operational JMA nonhydrostatic mesoscale model. Mon Wea Rev 134:1266–1298CrossRefGoogle Scholar
  11. Seko H, Kawabata T, Tsuyuki T, Nakamura H, Koizumi K, Iwabuchi T (2004) Impacts of GPS-derived water vapor and radial wind measured by Doppler radar on numerical prediction of precipitation. J Meteor Soc Jpn 82:473–489CrossRefGoogle Scholar
  12. Seko H, Miyoshi T, Shoji Y Saito K (2011) Data assimilation experiments of precipitable water vapor using the LETKF system: intense rainfall event over Japan 28 July 2008. Tellus 63A: 402–412Google Scholar
  13. Shoji Y, Nakamura H, Iwabuchi T, Aonashi K, Seko H, Mishima K, Itagaki A, Ichikawa R, Ohtani R (2004) Tsukuba GPS dense net campaign observation: improvement in GPS analysis of slant path delay by stacking one-way postfit phase residuals. J Meteor Soc Jpn 82:301–314CrossRefGoogle Scholar
  14. Snyder C, Zhang F (2003) Assimilation of simulated Doppler radar observations with an ensemble Kalman filter. Mon Wea Rev 131:1663–1677CrossRefGoogle Scholar
  15. Tanaka Y, Suzuki O (2000) Development of radar analysis software “Draft”. In: Proceedings of the spring conference of meteorological society Japan vol 303:(in Japanese)Google Scholar
  16. Tong M, Xue M (2005) Ensemble Kalman filter assimilation of Doppler radar data with a compressible nonhydrostatic model: OSSE experiments. Mon Wea Rev 133:1789–1807CrossRefGoogle Scholar
  17. Xue M, Tong M, Droegemeier KK (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 Tech 23:46–66CrossRefGoogle Scholar
  18. Yang S-C, Corazza M, Carrassi A, Kalnay E, Miyoshi T (2009) Comparison of local ensemble transform kalman filter, 3DVAR, and 4DVAR in quasigeostrophic model. Mon Wea Rev 137:693–709CrossRefGoogle Scholar
  19. Zhang F, Snyder C, Sun J (2004) Impacts of initial estimate and observation availability with an ensemble Kalman filter. Mon Wea Rev 132:1238–1253CrossRefGoogle Scholar
  20. Zhang F, Meng Z, Aksoy A (2006) Test of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part I: perfect model experiments. Mon Wea Rev 134:722–736CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Hiromu Seko
    • 1
    Email author
  • Tadashi Tsuyuki
    • 1
  • Kazuo Saito
    • 1
  • Takemasa Miyoshi
    • 2
  1. 1.Meteorological Research InstituteTsukubaJapan
  2. 2.RIKEN Advanced Institute for Computational ScienceKobeJapan

Personalised recommendations