An economical approach to four-dimensional variational data assimilation
By Bin Wang, Juanjuan Liu, Shudong Wang, Wei Cheng, Liu Juan, Chengsi Liu, Q. Xiao, Ying-Hwa Kuo. 2010
92 citations recorded by the source corpus.
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 dimensionreduced 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.
Subfields: Atmospheric Science, Environmental Engineering, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/68d44adf690d41899900572e264ef3a6155bc391
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Citations in the corpus
- A non-linear least squares enhanced POD-4DVar algorithm for data assimilation (2015, methodology): “As stated in Wang et al. (2010), rainfall observations through 4DVar may significantly improve rainfall-forecasting skill (Zou and Kuo, 1996) because the precipitation process depends upon many model variables (e.g. wind, temperature, and water vapour mixing ratio) through model dynamics and…”