Implicit Particle Methods and Their Connection with Variational Data Assimilation
Implicit Particle Methods and Their Connection with Variational Data Assimilation
复制标题
隐式粒子方法及其与变分数据同化的联系
DOI：
--
复制
发表时间：
2012
期刊：
影响因子：
--
通讯作者：
A. Chorin
中科院分区：
文献类型：
--
作者：
E. Atkins;M. Morzfeld;A. Chorin
隐式粒子滤波是一种用于数据同化的序列蒙特卡罗方法，它通过包括最小化在内的一系列步骤将粒子引导到高概率区域。提出了一种新的更一般的方法，并将该方法推广到粒子平滑和完美模型的数据同化。隐式粒子方法的最小化要求与变分数据同化的最小化要求相似，并探讨了隐式粒子方法与变分数据同化的联系。特别是，有人认为现有的变分码可以以较低的额外成本转换为隐式粒子方法，通常会产生更好的估计，这些估计也配备了不确定性的定量测量。给出了一个详细的例子。
AbstractThe implicit particle filter is a sequential Monte Carlo method for data assimilation that guides the particles to the high-probability regions via a sequence of steps that includes minimizations. A new and more general derivation of this approach is presented and the method is extended to particle smoothing as well as to data assimilation for perfect models. Minimizations required by implicit particle methods are shown to be similar to those that one encounters in variational data assimilation, and the connection of implicit particle methods with variational data assimilation is explored. In particular, it is argued that existing variational codes can be converted into implicit particle methods at a low additional cost, often yielding better estimates that are also equipped with quantitative measures of the uncertainty. A detailed example is presented.