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 successfu...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.展开更多
The Hansdorff dimensions of the image and the graph of random fields are given under general conditions.The results can be used to a wider class of self-similar random fields and processes,including Brownian motion,Br...The Hansdorff dimensions of the image and the graph of random fields are given under general conditions.The results can be used to a wider class of self-similar random fields and processes,including Brownian motion,Brownian sheet,fractional Brownian motion,processes with stable or(α,β)-fractional stable components.展开更多
高维数据具有稀疏性与易受维度灾难影响的特点,这使高维数据聚类的精度与的效率一直难以得到保证,因此采用子空间聚类的方式减小稀疏性与维度灾难对聚类结果的影响。首先采用随机抽样的方式从高维数据中挑选出适合聚类的维度生成子空间...高维数据具有稀疏性与易受维度灾难影响的特点,这使高维数据聚类的精度与的效率一直难以得到保证,因此采用子空间聚类的方式减小稀疏性与维度灾难对聚类结果的影响。首先采用随机抽样的方式从高维数据中挑选出适合聚类的维度生成子空间,并结合hoeffding界保证抽样结果的有效性。其次利用网格的邻接性,在子空间内生成混合网格,即可以保证数据的完整性也可以提高子空间密度。最后根据子空间的相似度与相异度,对维度剪枝,再次提高子空间密度。算法在加州大学欧文分校数据集(University of California-Irvine,UCI)上能够取得较好的结果,而且算法在的伸缩性以及抗噪声能力上有较好的表现。展开更多
基金the Ministry of Science and Technology of China for funding the 973 project (Grant No. 2004CB418304) the Ministry of Finance of China and the China Meteorological Administration for the Special Project of Meteorological Sector [Grant No. GYHY(QX)2007-6-15]
文摘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.
基金Supported by the National Natural Science Foundation of China.
文摘The Hansdorff dimensions of the image and the graph of random fields are given under general conditions.The results can be used to a wider class of self-similar random fields and processes,including Brownian motion,Brownian sheet,fractional Brownian motion,processes with stable or(α,β)-fractional stable components.
文摘高维数据具有稀疏性与易受维度灾难影响的特点,这使高维数据聚类的精度与的效率一直难以得到保证,因此采用子空间聚类的方式减小稀疏性与维度灾难对聚类结果的影响。首先采用随机抽样的方式从高维数据中挑选出适合聚类的维度生成子空间,并结合hoeffding界保证抽样结果的有效性。其次利用网格的邻接性,在子空间内生成混合网格,即可以保证数据的完整性也可以提高子空间密度。最后根据子空间的相似度与相异度,对维度剪枝,再次提高子空间密度。算法在加州大学欧文分校数据集(University of California-Irvine,UCI)上能够取得较好的结果,而且算法在的伸缩性以及抗噪声能力上有较好的表现。
