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A statistical modeling framework for characterising uncertainty in large datasets: Application to ocean colour

A statistical modeling framework for characterising uncertainty in large datasets: Application to ocean colour

Land, Peter E., Bailey, Trevor C., Taberner, Malcolm, Pardo, Silvia, Sathyendranath, Shubha, Nejabati Zenouz, Kayvan ORCID: 0000-0003-3285-9410, Brammall, Vicki, Shutler, Jamie D. and Quartly, Graham D. ORCID: 0000-0001-9132-9511 (2018) A statistical modeling framework for characterising uncertainty in large datasets: Application to ocean colour. Remote Sensing, 10 (5):695. ISSN 2072-4292 (Online) (doi:https://doi.org/10.3390/rs10050695)

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Abstract

Uncertainty estimation is crucial to establishing confidence in any data analysis, and this is especially true for Essential Climate Variables, including ocean colour. Methods for deriving uncertainty vary greatly across data types, so a generic statistics-based approach applicable to multiple data types is an advantage to simplify the use and understanding of uncertainty data. Progress towards rigorous uncertainty analysis of ocean colour has been slow, in part because of the complexity of ocean colour processing. Here, we present a general approach to uncertainty characterisation, using a database of satellite-in situ matchups to generate a statistical model of satellite uncertainty as a function of its contributing variables. With an example NASA MODIS-Aqua chlorophyll-a matchups database mostly covering the north Atlantic, we demonstrate a model that explains 67% of the squared error in log(chlorophyll-a) as a potentially correctable bias, with the remaining uncertainty being characterised as standard deviation and standard error at each pixel. The method is quite general, depending only on the existence of a suitable database of matchups or reference values, and can be applied to other sensors and data types such as other satellite observed Essential Climate Variables, empirical algorithms derived from in situ data, or even model data.

Item Type: Article
Additional Information: © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Uncontrolled Keywords: uncertainty; satellite; chlorophyll; statistics; bias; matchups; GAMLSS
Subjects: Q Science > QA Mathematics
Faculty / Department / Research Group: Faculty of Architecture, Computing & Humanities
Faculty of Architecture, Computing & Humanities > Department of Mathematical Sciences
Last Modified: 31 Jul 2019 16:11
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/24881

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