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

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http://hdl.handle.net/10138/228281

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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 . https://doi.org/10.5194/gmd-10-3793-2017

Julkaisun nimi: Sensitivity analysis of the meteorological preprocessor MPP-FMI 3.0 using algorithmic differentiation
Tekijä: Backman, John; Wood, Curtis R.; Auvinen, Mikko; Kangas, Leena; Hannuniemi, Hanna; Karppinen, Ari; Kukkonen, Jaakko
Muu tekijä: University of Helsinki, Department of Physics
Päiväys: 2017-10-17
Kieli: eng
Sivumäärä: 11
Kuuluu julkaisusarjaan: Geoscientific Model Development
ISSN: 1991-959X
URI: http://hdl.handle.net/10138/228281
Tiivistelmä: 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.
Avainsanat: ATMOSPHERIC RADIATIVE-TRANSFER
URBAN AIR-POLLUTION
MODELING SYSTEM
HELSINKI
AREA
114 Physical sciences
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