Assimilating high-resolution winds from a Doppler lidar using an ensemble Kalman filter with lateral boundary adjustment

Masahiro Sawada, Tsuyoshi Sakai, Toshiki Iwasaki, Hiromu Seko, Kazuo Saito, Takemasa Miyoshi

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


Monitoring severe weather, including wind shear and clear air turbulence, is important for aviation safety. To provide accurate information for nowcasts and very short-range forecasts up to an hour, a rapid-update prediction system has been developed, with a particular focus on lateral boundary adjustment (LBA) using the local ensemble transform Kalman filter (LETKF). Due to the small forecast domain, limited-area forecasts are dominated by the lateral boundary conditions from coarse-resolution global forecasts. To effectively extend the forecast lead time for the small domain, a new LBA scheme using the LETKF has been developed and assessed with three sea-breeze front cases. Observing system simulation experiments for high-resolution winds from a simulated Doppler lidar were performed with the Japan Meteorological Agency Nonhydrostatic Mesoscale Model at a horizontal resolution of 400 m and 15-minute update cycle. The results indicate that the LBA improved the forecast significantly. In particular, the 1-hour wind-speed forecast with the LBA is as accurate as the 15-minute forecast without the LBA. The assimilation of Doppler lidar high-resolution wind data with the LBA is a promising approach for very short-range forecasts up to an hour with a small domain, such as for aviation weather.

Original languageEnglish
Article number23473
JournalTellus, Series A: Dynamic Meteorology and Oceanography
Issue number1
Publication statusPublished - 2015


  • Data assimilation
  • Doppler lidar
  • Ensemble kalman filter
  • Lateral boundary
  • Mesoscale model
  • Nowcast and very-shortrange forecast

ASJC Scopus subject areas

  • Oceanography
  • Atmospheric Science


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