15 October 2009 A spatial-temporal covariance model for rainfall analysis
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Proceedings Volume 7492, International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining; 74922T (2009) https://doi.org/10.1117/12.838632
Event: International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, 2009, Wuhan, China
Abstract
Many environmental phenomena are regarded as realizations of random functions which possess both spatial and temporal characteristics. In particular, Geostatistics with an extension of the existing spatial techniques into the space-time domain offers some kinds of methods to model such processes. Although these methods for the analysis of spatial-temporal data are becoming more important for many areas of application, they are less developed than those for the analysis of purely spatial or purely temporal data. In this paper, two kinds of spatial-temporal stationary covariance models are introduced. And the differences between spatial domain and time domain are examined. A product-sum covariance model originally given by De Cesare is extended for spatial-temporal analysis on daily rainfall measurements in the three provinces of Northeast China. Remarkably, this generalized non-separable model does not correspond to the use of a metric one in space-time. The rainfall measurements used for this experiment are taken at 104 monitoring stations from January 2000 to December 2005. In the experiment, the product-sum variogram model is employed for developing ordinary kriging and its application to interpolation of the monthly rainfall data from January 2000 to December 2004 has been used to predict the monthly rainfall of 2005. The true values and the predicted ones are compared. The experimental results have shown that this product-sum covariance model is very effective for rainfall analysis.
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Sha Li, Sha Li, Hong Shu, Hong Shu, Zhengquan Xu, Zhengquan Xu, } "A spatial-temporal covariance model for rainfall analysis", Proc. SPIE 7492, International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining, 74922T (15 October 2009); doi: 10.1117/12.838632; https://doi.org/10.1117/12.838632
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