Wilkie, C.J., Miller, C.A. , Scott, E.M. , O'Donnell, R.A. , Hunter, P.D., Spyrakos, E. and Tyler, A.N. (2019) Nonparametric statistical downscaling for the fusion of data of different spatiotemporal support. Environmetrics, 30(3), e2549. (doi: 10.1002/env.2549)
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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.
Item Type: | Articles |
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Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | O'Donnell, Dr Ruth and Miller, Professor Claire and Wilkie, Dr Craig and Scott, Professor Marian |
Authors: | Wilkie, C.J., Miller, C.A., Scott, E.M., O'Donnell, R.A., Hunter, P.D., Spyrakos, E., and Tyler, A.N. |
College/School: | College of Science and Engineering > School of Mathematics and Statistics > Statistics |
Journal Name: | Environmetrics |
Publisher: | Wiley |
ISSN: | 1180-4009 |
ISSN (Online): | 1099-095X |
Published Online: | 21 December 2018 |
Copyright Holders: | Copyright © 2018 The Authors |
First Published: | First published in Environmetrics 30:e2549 |
Publisher Policy: | Reproduced under a Creative Commons License |
Data DOI: | 10.5525/gla.researchdata.651 |
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