A statistical downscaling framework for environmental mapping

dc.contributor.author Mwitondi, Kassim S.
dc.contributor.author Al-Kuwari, Farha A.
dc.contributor.author Saeed, Raed A.
dc.contributor.author Zargari, Shahrzad A.
dc.date.accessioned 2020-01-21T05:02:50Z
dc.date.available 2020-01-21T05:02:50Z
dc.date.copyright 2018 en_US
dc.date.issued 2019
dc.description This 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.abstract In 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.citation Mwitondi, 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-y en_US
dc.identifier.issn 09208542
dc.identifier.uri http://dx.doi.org/10.1007/s11227-018-2624-y
dc.identifier.uri https://hdl.handle.net/20.500.12519/12
dc.language.iso en en_US
dc.publisher Springer New York LLC en_US
dc.relation Authors 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.ispartofseries Journal of Supercomputing;Vol. 75, no. 2
dc.rights Permission to reuse the abstract has been secured from Springer New York LLC.
dc.rights.holder Copyright : 2018, The Author(s).
dc.subject Chemical transport models en_US
dc.subject Downscaling en_US
dc.subject Empirical orthogonal functions en_US
dc.subject Ensemble modelling en_US
dc.subject Interdisciplinary computation en_US
dc.subject Principal component analysis en_US
dc.subject Principal fitted components en_US
dc.subject Unsupervised modelling en_US
dc.subject Chemical analysis en_US
dc.subject Climate models en_US
dc.subject Environmental Protection Agency en_US
dc.subject Extraction en_US
dc.subject Mapping en_US
dc.subject Numerical methods en_US
dc.subject Orthogonal functions en_US
dc.subject Ozone en_US
dc.subject Chemical transport models en_US
dc.subject Down-scaling en_US
dc.subject Empirical orthogonal functions en_US
dc.subject Environmental phenomena en_US
dc.subject Performance assessment en_US
dc.subject Principal fitted components en_US
dc.subject Statistical downscaling en_US
dc.subject US Environmental Protection Agency en_US
dc.subject Principal component analysis en_US
dc.title A statistical downscaling framework for environmental mapping en_US
dc.type Article en_US
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