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An Application of the Multi-Physics Ensemble Kalman Filter to Typhoon Forecast
Authors:Chanh Kieu  Pham Thi Minh  Hoang Thi Mai
Institution:1. Laboratory for Weather and Climate Forecasting, Hanoi College of Science, Vietnam National University, Hanoi, 10000, Vietnam
3. I. M. Systems Group, NOAA/NWS/NCEP/EMC, Camp Spring, MA, 20746, USA
2. Center for Environmental Fluid Dynamics, Hanoi College of Science, Vietnam National University, Hanoi, 10000, Vietnam
Abstract:This study examines the roles of the multi-physics approach in accounting for model errors for typhoon forecasts with the local ensemble transform Kalman filter (LETKF). Experiments with forecasts of Typhoon Conson (2010) using the weather research and forecasting (WRF) model show that use of the WRF’s multiple physical parameterization schemes to represent the model uncertainties can help the LETKF provide better forecasts of Typhoon Conson in terms of the forecast errors, the ensemble spread, the root mean square errors, the cross-correlation between mass and wind field as well as the coherent structure of the ensemble spread along the storm center. Sensitivity experiments with the WRF model show that the optimum number of the multi-physics ensemble is roughly equal to the number of combinations of different physics schemes assigned in the multi-physics ensemble. Additional idealized experiments with the Lorenz 40-variable model to isolate the dual roles of the multi-physics ensemble in correcting model errors and expanding the local ensemble space show that the multi-physics approach appears to be more essential in augmenting the local rank representation of the LETKF algorithm rather than directly accounting for model errors during the early cycles. The results in this study suggest that the multi-physics approach is a good option for short-range forecast applications with full physics models in which the spinup of the ensemble Kalman filter may take too long for the ensemble spread to capture efficiently model errors and cross-correlations among model variables.
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