A statistical downscaling framework for environmental mapping

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dc.contributor.authorMwitondi, Kassim S.
dc.contributor.authorAl-Kuwari, Farha A.
dc.contributor.authorSaeed, Raed A.
dc.contributor.authorZargari, Shahrzad A.
dc.date.accessioned2020-01-21T05:02:50Z
dc.date.available2020-01-21T05:02:50Z
dc.date.copyright2018en_US
dc.date.issued2019
dc.descriptionThis article is not available at CUD collection. The version of scholarly record of this article is published in Journal of Supercomputing (2019), available online at: https://doi.org/10.1007/s11227-018-2624-y.en_US
dc.description.abstractIn recent years, knowledge extraction from data has become increasingly popular, with many numerical forecasting models, mainly falling into two major categories—chemical transport models (CTMs) and conventional statistical methods. However, due to data and model variability, data-driven knowledge extraction from high-dimensional, multifaceted data in such applications require generalisations of global to regional or local conditions. Typically, generalisation is achieved via mapping global conditions to local ecosystems and human habitats which amounts to tracking and monitoring environmental dynamics in various geographical areas and their regional and global implications on human livelihood. Statistical downscaling techniques have been widely used to extract high-resolution information from regional-scale variables produced by CTMs in climate model. Conventional applications of these methods are predominantly dimensional reduction in nature, designed to reduce spatial dimension of gridded model outputs without loss of essential spatial information. Their downside is twofold—complete dependence on unlabelled design matrix and reliance on underlying distributional assumptions. We propose a novel statistical downscaling framework for dealing with data and model variability. Its power derives from training and testing multiple models on multiple samples, narrowing down global environmental phenomena to regional discordance through dimensional reduction and visualisation. Hourly ground-level ozone observations were obtained from various environmental stations maintained by the US Environmental Protection Agency, covering the summer period (June–August 2005). Regional patterns of ozone are related to local observations via repeated runs and performance assessment of multiple versions of empirical orthogonal functions or principal components and principal fitted components via an algorithm with fully adaptable parameters. We demonstrate how the algorithm can be extended to weather-dependent and other applications with inherent data randomness and model variability via its built-in interdisciplinary computational power that connects data sources with end-users. © 2018, The Author(s).en_US
dc.identifier.citationMwitondi, K. S., Al-Kuwari, F. A., Saeed, R. A., & Zargari, S. (2019). A statistical downscaling framework for environmental mapping. Journal of Supercomputing, 75(2), 984–997. https://doi.org/10.1007/s11227-018-2624-yen_US
dc.identifier.issn09208542
dc.identifier.urihttp://dx.doi.org/10.1007/s11227-018-2624-y
dc.identifier.urihttps://hdl.handle.net/20.500.12519/12
dc.language.isoenen_US
dc.publisherSpringer New York LLCen_US
dc.relationAuthors Affiliations: Mwitondi, K.S., Faculty or Science, Technology and Arts, Sheffield Hallam University, Sheffield, United Kingdom; Al-Kuwari, F.A., Statistics and Research Division, Qatar Development Bank, Doha, Qatar; Saeed, R.A., Canadian University Dubai, Dubai, United Arab Emirates; Zargari, S., Faculty or Science, Technology and Arts, Sheffield Hallam University, Sheffield, United Kingdom.
dc.relation.ispartofseriesJournal of Supercomputing;Vol. 75, no. 2
dc.rightsPermission to reuse the abstract has been secured from Springer New York LLC.
dc.rights.holderCopyright : 2018, The Author(s).
dc.subjectChemical transport modelsen_US
dc.subjectDownscalingen_US
dc.subjectEmpirical orthogonal functionsen_US
dc.subjectEnsemble modellingen_US
dc.subjectInterdisciplinary computationen_US
dc.subjectPrincipal component analysisen_US
dc.subjectPrincipal fitted componentsen_US
dc.subjectUnsupervised modellingen_US
dc.subjectChemical analysisen_US
dc.subjectClimate modelsen_US
dc.subjectEnvironmental Protection Agencyen_US
dc.subjectExtractionen_US
dc.subjectMappingen_US
dc.subjectNumerical methodsen_US
dc.subjectOrthogonal functionsen_US
dc.subjectOzoneen_US
dc.subjectChemical transport modelsen_US
dc.subjectDown-scalingen_US
dc.subjectEmpirical orthogonal functionsen_US
dc.subjectEnvironmental phenomenaen_US
dc.subjectPerformance assessmenten_US
dc.subjectPrincipal fitted componentsen_US
dc.subjectStatistical downscalingen_US
dc.subjectUS Environmental Protection Agencyen_US
dc.subjectPrincipal component analysisen_US
dc.titleA statistical downscaling framework for environmental mappingen_US
dc.typeArticleen_US

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