An Economical Approach to Four-dimensional Variational Data Assimilation |
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Authors: | WANG Bin LIU Juanjuan WANG Shudong CHENG Wei LIU Juan LIU Chengsi Qingnong XIAO and Ying-Hwa KUO |
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Institution: | State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, Graduate School of the Chinese Academy of Sciences, Beijing 100049,State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, Graduate School of the Chinese Academy of Sciences, Beijing 100049,State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, Graduate School of the Chinese Academy of Sciences, Beijing 100049,State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, Graduate School of the Chinese Academy of Sciences, Beijing 100049,State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, Graduate School of the Chinese Academy of Sciences, Beijing 100049,College of Marine Science, University of South Florida, St. Petersburg, FL 33701, USA,Mesoscale and Microscale Meteorology Division, National Center for Atmospheric Research, Boulder, CO $80307$--3000, USA |
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Abstract: | Four-dimensional variational data assimilation (4DVar) is one of the most
promising methods to provide optimal analysis for numerical weather
prediction (NWP). Five national NWP centers in the world have successfully
applied 4DVar methods in their global NWPs, thanks to the increment method
and adjoint technique. However, the application of 4DVar is still limited by
the computer resources available at many NWP centers and research
institutes. It is essential, therefore, to further reduce the computational
cost of 4DVar. Here, an economical approach to implement 4DVar is proposed,
using the technique of dimension-reduced projection (DRP), which is called
``DRP-4DVar." The proposed approach is based on dimension reduction using
an ensemble of historical samples to define a subspace. It directly obtains
an optimal solution in the reduced space by fitting observations with
historical time series generated by the model to form consistent forecast
states, and therefore does not require implementation of the adjoint of
tangent linear approximation.
To evaluate the performance of the DRP-4DVar on assimilating different types
of mesoscale observations, some observing system simulation experiments are
conducted using MM5 and a comparison is made between adjoint-based 4DVar and
DRP-4DVar using a 6-hour assimilation window. |
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Keywords: | 4DVar adjoint dimension reduction historical sample observing system simulation experiment |
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