Dynamic multi-resolution spatial models
Dynamic multi-resolution spatial models
复制标题
动态多分辨率空间模型
DOI：
--
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发表时间：
2007
影响因子：
3.8
通讯作者：
Hsin
中科院分区：
文献类型：
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作者：
G. Jóhannesson;N. Cressie;Hsin
来自遥感平台的数据在监测平流层臭氧分布等环境过程方面发挥着重要作用。遥感数据通常是空间的、时间的和海量的。现有的预测方法，如克里金法是计算上不可行的。多分辨率空间模型（MRSM）捕捉非平稳的空间依赖性，并使用分辨率变化卡尔曼滤波器产生快速的最佳估计。然而，过去的数据可以提供有关正在调查的过程的当前状态的有价值的信息。在这篇文章中，我们通过开发一个动态MRSM将时间依赖性纳入过程。一个月的每日总柱臭氧数据的动态MRSM的应用程序，并在给定的一天的后验推理的结果进行了比较的空间MRSM。很明显，在数据缺失的区域，例如当整个卫星数据条带缺失时，使用动态MRSM具有优势。
Data from remote-sensing platforms play an important role in monitoring environmental processes, such as the distribution of stratospheric ozone. Remote-sense data are typically spatial, temporal, and massive. Existing prediction methods such as kriging are computationally infeasible. The multi-resolution spatial model (MRSM) captures nonstationary spatial dependence and produces fast optimal estimates using a change-of-resolution Kalman filter. However, past data can provide valuable information about the current status of the process being investigated. In this article, we incorporate the temporal dependence into the process by developing a dynamic MRSM. An application of the dynamic MRSM to a month of daily total column ozone data is presented, and on a given day the results of posterior inference are compared to those for the spatial-only MRSM. It is apparent that there are advantages to using the dynamic MRSM in regions where data are missing, such as when a whole swath of satellite data is missing.