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Data Assimilation

Data Assimilation is like a bridge between numerical simulation models and actual observations (see figure below). If we let the numerical models run without any constraint from the real world, the model states will evolve purely by the model equations. Although this can be an approach to understand the Nature through improving the models, data assimilation provides a means that directly connects the models and Nature.

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My group has been studying fundamental problems about data assimilation theories and methods, with particular focus on ensemble-based data assimilation. The fundamental theoretical studies interact synergistically with applications to various numerical models.

Related publications

• Miyoshi, T., E. Kalnay, and H. Li, 2012: Estimating and including observation error correlations in data assimilation. Inv. Prob. Sci. Eng., in press.
• Miyoshi, T., 2011: The Gaussian Approach to Adaptive Covariance Inflation and Its Implementation with the Local Ensemble Transform Kalman Filter. Mon. Wea. Rev., 139, 1519-1535. doi:10.1175/2010MWR3570.1
• Greybush, S. J., E. Kalnay, T. Miyoshi, K. Ide, and B. R. Hunt, 2011: Balance and Ensemble Kalman Filter Localization Techniques. Mon. Wea. Rev., 139, 511-522. doi:10.1175/2010MWR3328.1
• Liu, J., E. Kalnay, T. Miyoshi, and C. Cardinali, 2009: Analysis sensitivity calculation within an ensemble Kalman filter. Quart. J. Roy. Meteor. Soc., 135, 1842-1851. doi:10.1002/qj.511
• Li, H., E. Kalnay, T. Miyoshi, and C. M. Danforth, 2009: Accounting for Model Errors in Ensemble Data Assimilation. Mon. Wea. Rev., 137, 3407-3419. doi:10.1175/2009MWR2766.1
• Danforth, C. M., E. Kalnay, and T. Miyoshi, 2007: Estimating and Correcting Weather Model Error. Mon. Wea. Rev., 135, 281-299. doi:10.1175/MWR3289.1

Tropical Cyclone Forecasts

The actual observations are assimilated into global and regional Numerical Weather Prediction (NWP) models to "forecast" the past-case tropical cyclones (TCs). Intense TCs are called "Hurricanes" in the Atlantic, "Typhoons" in the Western Pacific, or "Cyclones" in the Indian Ocean.

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In the past decades, NWP of TC track forecasts (that is, where the Hurricanes are heading to in the next 5 days) were improved consistently. However, it is rather surprising that in spite of rapid advance of computer technology and consistent development of NWP models, we gained essentially no skill during the past two decades in the TC intensity forecasts (that is, how strong or weak the Hurricanes will become in the next a few days). Hurricanes move generally along with the large-scale atmospheric flow, and we actually improved consistently the forecast skill of larger-scale weather forecasts. This implies that the intensity forecasting is involved with smaller scales or complex air-sea interaction process which have not been addressed well enough in the past research and development. I believe that data assimilation plays a key role in improving TC forecasts, both in terms of track and intensity, by best utilizing all available observations from satellites, aircrafts, ships and buoys, for both atmosphere and ocean components. My group has been working on how to enhance TC forecasting capabilities by improving data assimilation approaches.

Related publications

• Yang, S.-C., E. Kalnay, and T. Miyoshi, 2012: Improving EnKF spin-up for typhoon assimilation and prediction. Weather and Forecasting, in press.
• Kunii, M., T. Miyoshi, and E. Kalnay, 2012: Estimating impact of real observations in regional numerical weather prediction using an ensemble Kalman filter. Mon. Wea. Rev., 140, 1975-1987. doi:10.1175/MWR-D-11-00205.1
• Miyoshi, T. and M. Kunii, 2012: The Local Ensemble Transform Kalman Filter with the Weather Research and Forecasting Model: Experiments with Real Observations. Pure and Appl. Geophys., 169, 321-333. doi:10.1007/s00024-011-0373-4
• Miyoshi, T., T. Komori, H. Yonehara, R. Sakai, and M. Yamaguchi, 2010: Impact of Resolution Degradation of the Initial Condition on Typhoon Track Forecasts. Weather and Forecasting, 25, 1568-1573. doi:10.1175/2010WAF2222392.1