Sea Surface Temperature Modeling using Radial Basis Function Networks With a Dynamically Weighted Particle Filter

• 成果类型: Article
• 署名作者: Ryu, Duchwan; Liang, Faming; Mallick, Bani K.
• 署名单位: University System of Georgia; Augusta University; Texas A&M University System; Texas A&M University College Station
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2012.734151
• 发表日期: 2013
• 页码: 111-123
• 关键词: monte-carlo methods; rejection control; Kalman filter; assimilation; regression

摘要

The sea surface temperature (SST) is an important factor of the earth climate system. A deep understanding of SST is essential for climate monitoring and prediction. In general, SST follows a nonlinear pattern in both time and location and can be modeled by a dynamic system which changes with time and location. In this article, we propose a radial basis function network-based dynamic model which is able to catch the nonlinearity of the data and propose to use the dynamically weighted particle filter to estimate the parameters of the dynamic model. We analyze the SST observed in the Caribbean Islands area after a hurricane using the proposed dynamic model. Comparing to the traditional grid-based approach that requires a supercomputer due to its high computational demand, our approach requires much less CPU time and makes real-time forecasting of SST doable on a personal computer. Supplementary materials for this article are available online.