Sensitivity analysis of the meteorological preprocessor MPP-FMI 3.0 using algorithmic differentiation

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dc.contributor University of Helsinki, Department of Physics en Backman, John Wood, Curtis R. Auvinen, Mikko Kangas, Leena Hannuniemi, Hanna Karppinen, Ari Kukkonen, Jaakko 2017-11-03T12:35:00Z 2017-11-03T12:35:00Z 2017-10-17
dc.identifier.citation Backman , J , Wood , C R , Auvinen , M , Kangas , L , Hannuniemi , H , Karppinen , A & Kukkonen , J 2017 , ' Sensitivity analysis of the meteorological preprocessor MPP-FMI 3.0 using algorithmic differentiation ' , Geoscientific Model Development , vol. 10 , no. 10 , pp. 3793-3803 . en
dc.identifier.issn 1991-959X
dc.identifier.other PURE: 92790675
dc.identifier.other PURE UUID: 37b66645-1584-4beb-92b2-a5a1aed8f6fd
dc.identifier.other WOS: 000413113000001
dc.identifier.other Scopus: 85031827814
dc.identifier.other ORCID: /0000-0002-6927-825X/work/39206345
dc.description.abstract The meteorological input parameters for urbanand local-scale dispersion models can be evaluated by pre-processing meteorological observations, using a boundarylayer parameterisation model. This study presents a sensitivity analysis of a meteorological preprocessor model (MPP-FMI) that utilises readily available meteorological data as input. The sensitivity of the preprocessor to meteorological input was analysed using algorithmic differentiation (AD). The AD tool used was TAPENADE. The AD method numerically evaluates the partial derivatives of functions that are implemented in a computer program. In this study, we focus on the evaluation of vertical fluxes in the atmosphere and in particular on the sensitivity of the predicted inverse Obukhov length and friction velocity on the model input parameters. The study shows that the estimated inverse Obukhov length and friction velocity are most sensitive to wind speed and second most sensitive to solar irradiation. The dependency on wind speed is most pronounced at low wind speeds. The presented results have implications for improving the meteorological preprocessing models. AD is shown to be an efficient tool for studying the ranges of sensitivities of the predicted parameters on the model input values quantitatively. A wider use of such advanced sensitivity analysis methods could potentially be very useful in analysing and improving the models used in atmospheric sciences. en
dc.format.extent 11
dc.language.iso eng
dc.relation.ispartof Geoscientific Model Development
dc.rights en
dc.subject MODELING SYSTEM en
dc.subject HELSINKI en
dc.subject AREA en
dc.subject 114 Physical sciences en
dc.title Sensitivity analysis of the meteorological preprocessor MPP-FMI 3.0 using algorithmic differentiation en
dc.type Article
dc.description.version Peer reviewed
dc.type.uri info:eu-repo/semantics/other
dc.type.uri info:eu-repo/semantics/publishedVersion

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