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| Assimilating Amounts of Precipitation Using a New Four-Dimensional Variational Method |
| LIU Juan-Juan,WANG Bin |
| LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029; Graduate School of the Chinese Academy of Sciences, Beijing 100049,LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029 |
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Abstract Observations of accumulated precipitation are extremely valuable for effectively improving rainfall analysis and forecast. It is, however, difficult to use such observations directly through sequential assimilation methods, such as three-dimensional variational data assimilation or an Ensemble Kalman Filter. In this study, the authors illustrate a new approach that makes effective use of precipitation data to improve rainfall forecast. The new method directly obtains an optimal solution in a reduced space by fitting observations with historical time series generated by the model; it also avoids the implementation of tangent linear model and its adjoint. A lot of historical samples are produced as the ensemble of precipitation observations with the fully nonlinear forecast model. The results show that the new approach is capable of extracting information from precipitation observations to improve the analysis and forecast. This method provides comparable performance with the standard four-dimensional variational data assimilation at a much lower computational cost.
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Received: 08 May 2009
Revised: 10 September 2009
Accepted: 04 November 2009
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