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Article

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

Citation
Wilkie CJ, Miller CA, Scott EM, O'Donnell RA, Hunter PD, Spyrakos E & Tyler AN (2019) Nonparametric statistical downscaling for the fusion of data of different spatiotemporal support. Environmetrics, 30 (3), Art. No.: e2549. https://doi.org/10.1002/env.2549

Abstract
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.

Keywords
Bayesian hierarchical modelling; change‐of‐support; chlorophyll‐a; data fusion; statistical downscaling

Journal
Environmetrics: Volume 30, Issue 3

StatusPublished
Author(s)Wilkie, Craig J; Miller, Claire A; Scott, Ethel M; O'Donnell, Ruth A; Hunter, Peter D; Spyrakos, Evangelos; Tyler, Andrew N
FundersNatural Environment Research Council
Publication date31/05/2019
Publication date online21/12/2018
Date accepted by journal05/11/2018
URLhttp://hdl.handle.net/1893/28766
ISSN1180-4009
eISSN1099-095X
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