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

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

Abstract

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.

Keywords

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.

Notes

Acknowledgements

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.

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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

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