Data Assimilation Research Team

Data assimilation is a cross-disciplinary science to synergize numerical simulations and observational data, using statistical methods and applied mathematics. As computers become more powerful and enable more precise simulations, it will become more important to compare the simulation with actual observations. Data Assimilation Research Team ("DA team") performs cutting-edge research and development on advanced data assimilation methods and their wide applications, aiming at integrating computer simulations and observational data in the wisest way. Particularly, the DA team will tackle challenging problems of developing efficient and accurate data assimilation systems for high-dimensional simulations with large amount of data. The specific areas include 1) research on parallel-efficient algorithms for data assimilation with the super-parallel K computer, 2) research on data assimilation methods and applications by taking advantage of the world-leading K computer, and 3) development of most advanced data assimilation software optimized for the K computer.

Research Subjects

• Ensemble-based data assimilation suitable to various high-dimensional simulations
• Theoretical research on challenging problems in data assimilation
• Wide applications of data assimilation
• Cutting-edge data assimilation research on geophysical applications

Publications

1. Miyoshi, T., Kalnay, E., and Li, H.:
   "Estimating and including observation error correlations in data assimilation"
   Inverse Problems in Science and Engineering, doi:10.1080/17415977.2012.712527 (2012)
2. Miyoshi, T., and Kunii, M.:
   "Using AIRS retrievals in the WRF-LETKF system to improve regional numerical weather prediction"
   Tellus, 64A, 18408 (2012)
3. Miyoshi, T., and Kunii, M.:
   "The Local Ensemble Transform Kalman Filter with the Weather Research and Forecasting Model: Experiments with Real Observations"
   Pure and Appl. Geophys., 169, 321-333 (2012)
4. Miyoshi, T.:
   "The Gaussian Approach to Adaptive Covariance Inflation and Its Implementation with the Local Ensemble Transform Kalman Filter"
   Monthly Weather Review, 139, 1519-1535 (2011)
5. Miyoshi, T., Sato, Y., and Kadowaki, T.:
   "Ensemble Kalman filter and 4D-Var inter-comparison with the Japanese operational global analysis and prediction system"
   Monthly Weather Review, 138, 2846-2866 (2010)
6. Miyoshi, T., and Yamane, S.:
   "Local ensemble transform Kalman filtering with an AGCM at a T159/L48 resolution"
   Monthly Weather Review, 135, 3841-3861 (2007)
7. Miyoshi, T., Komori, T., Yonehara, H., Sakai, R., and Yamaguchi, M.:
   "Impact of Resolution Degradation of the Initial Condition on Typhoon Track Forecasts"
   Weather and Forecasting, 25, 1568-1573 (2010)
8. Miyoshi, T., and Kadowaki, T.:
   "Accounting for Flow-dependence in the Background Error Variance within the JMA Global Four-dimensional Variational Data Assimilation System"
   SOLA, 4, 37-40 (2008)
9. Miyoshi, T., Yamane, S., and Enomoto, T.:
   "The AFES-LETKF Experimental Ensemble Reanalysis: ALERA"
   SOLA, 3, 45-48 (2007)
10. Miyoshi, T., and Aranami, K.:
    "Applying a Four-dimensional Local Ensemble Transform Kalman Filter (4D-LETKF) to the JMA Nonhydrostatic Model (NHM)"
    SOLA, 2, 128-131 (2006)