ANALYSIS-METHODS FOR NUMERICAL WEATHER PREDICTION
ANALYSIS-METHODS FOR NUMERICAL WEATHER PREDICTION
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DOI：
10.1002/qj.49711247414
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发表时间：
1986-10-01
影响因子：
8.9
通讯作者：
LORENC, AC
中科院分区：
文献类型：
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作者：
LORENC, AC
贝叶斯概率参数用于导出理想化方程，以找到数值天气预报的最佳分析。根据数值预报分析问题的物理特征，将这些方程与其它已发表的方法进行了比较;即分析基础的预定性质，由于高阶系统而需要近似，仅使用观测时问题的欠确定性，先验关系源于（1）对模型时间演化的了解（与观测的时间分布一起构成四维数据同化）;（2）大气变化缓慢的知识（平衡关系）;（3）大气中其他参数与尺度耦合的非线性关系。讨论的方法包括变分技术、光滑样条、克立格、最优插值、连续修正、约束初始化、卡尔曼-布西滤波器和伴随模型数据同化。它们都与理想化的分析有关，因此彼此有关。对于特定方法何时可能更合适，给出了意见。通过与理想化方法的比较，对实际方法中参数的合理选择有了一定的认识。
Bayesian probabilistic arguments are used to derive idealized equations for finding the best analysis for numerical weather prediction. These equations are compared with those from other published methods in the light of the physical characteristics of the NWP analysis problem; namely the predetermined nature of the basis for the analysis, the need for approximation because of large‐order systems, the underdeterminacy of the problem when using observations alone, and the availability of prior relationships to resolve the underdeterminacy.Prior relationships result from (1) knowledge of the time evolution of the model (which together with the use of a time distribution of observations constitutes four‐dimensional data assimilation); (2) knowledge that the atmosphere varies slowly (leading to balance relationships); (3) other nonlinear relationships coupling parameters and scales in the atmosphere.Methods discussed include variational techniques, smoothing splines, Kriging, optimal interpolation, successive corrections, constrained initialization, the Kalman‐Bucy filter, and adjoint model data assimilation. They are all shown to relate to the idealized analysis, and hence to each other. Opinions are given on when particular methods might be more appropriate. By comparison with the idealized method some insight is gained into appropriate choices of parameters in the practical methods.