An economical approach to four-dimensional variational data assimilation
An economical approach to four-dimensional variational data assimilation
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DOI：
10.1007/s00376-009-9122-3
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发表时间：
2010-07-01
影响因子：
5.8
通讯作者：
Kuo, Ying-Hwa
中科院分区：
文献类型：
--
作者：
Wang Bin;Liu Juanjuan;Kuo, Ying-Hwa
四维变分资料同化(4DVar)是为数值天气预报(NWP)提供优化分析的最有前途的方法之一。世界上已有5个国家数值预报中心在其全球数值预报中成功地应用了4DVar方法，这要归功于增量方法和伴随技术。然而，4DVar的应用仍然受到许多数值预报中心和研究机构可用的计算机资源的限制。因此，进一步降低4DVar的计算代价是至关重要的。本文提出了一种经济的实现4DVar的方法，使用降维投影(DRP)技术，称为“DRP-4DVar”。该方法基于历史样本集合降维来定义子空间。通过将模型生成的历史时间序列与观测值进行拟合，直接在降维空间中得到最优解，从而形成一致的预报状态，因此不需要实现切线线性近似的伴随。为了评估DRP-4DVar同化不同类型中尺度观测的性能，利用MM5进行了观测系统模拟实验，并与基于伴随的4DVar和DRP-4DVar进行了6小时同化窗口的比较。
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.