扩展平方根方法以考虑集合方法中的加性预报噪声
Extending the square root method to account for additive forecast noise in ensemble methods
摘要：
平方根的方法被认为是占模型噪声的集合卡尔曼滤波（EnKF）和相关算法的预测步骤中的问题。其主要目的是取代模拟、伪随机、加性噪声的方法，以消除相关的抽样误差。核心方法是基于集成平方根滤波器的分析步骤，并包括在确定性计算的变换矩阵。关于动力一致性的理论优势进行了调查，同样适用于平方根法的分析步骤。一个基本的问题，由于有限大小的合奏子空间进行了讨论，并提出了新的解决方案，补充核心方法和研究。从简单的，低阶动力学的双实验的基准表明改进的性能超过标准的方法，如添加剂，模拟噪声和乘性膨胀。
英文摘要：
A square root approach is considered for the problem of accounting for model noise in the forecast step of the ensemble Kalman filter (EnKF) and related algorithms. The primary aim is to replace the method of simulated, pseudo-random, additive noise so as to eliminate the associated sampling errors. The core method is based on the analysis step of ensemble square root filters, and consists in the deterministic computation of a transform matrix. The theoretical advantages regarding dynamical consistency are surveyed, applying equally well to the square root method in the analysis step. A fundamental problem due to the limited size of the ensemble subspace is discussed, and novel solutions that complement the core method are suggested and studied. Benchmarks from twin experiments with simple, low-order dynamics indicate improved performance over standard approaches such as additive, simulated noise and multiplicative inflation.
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