Nonparametric statistical downscaling for the fusion of data of different spatiotemporal support

被引:7
|
作者
Wilkie, C. J. [1 ]
Miller, C. A. [1 ]
Scott, E. M. [1 ]
O'Donnell, R. A. [1 ]
Hunter, P. D. [2 ]
Spyrakos, E. [2 ]
Tyler, A. N. [2 ]
机构
[1] Univ Glasgow, Sch Math & Stat, Glasgow G12 8QQ, Lanark, Scotland
[2] Univ Stirling, Biol & Environm Sci, Stirling, Scotland
基金
英国自然环境研究理事会;
关键词
Bayesian hierarchical modelling; change-of-support; chlorophyll-a; data fusion; statistical downscaling; LAKE BALATON;
D O I
10.1002/env.2549
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Statistical downscaling has been developed for the fusion of data of different spatial support. However, environmental data often have different temporal support, which must also be accounted for. This paper presents a novel method of nonparametric statistical downscaling, which enables the fusion of data of different spatiotemporal support through treating the data at each location as observations of smooth functions over time. This is incorporated within a Bayesian hierarchical model with smoothly spatially varying coefficients, which provides predictions at any location or time, with associated estimates of uncertainty. The method is motivated by an application for the fusion of in situ and satellite remote sensing log(chlorophyll-a) data from Lake Balaton, in order to improve the understanding of water quality patterns over space and time.
引用
收藏
页数:12
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