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Volume 10, issue 10 | Copyright
Geosci. Model Dev., 10, 3793-3803, 2017
https://doi.org/10.5194/gmd-10-3793-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Model evaluation paper 17 Oct 2017

Model evaluation paper | 17 Oct 2017

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

John Backman1, Curtis R. Wood1, Mikko Auvinen1,2, Leena Kangas1, Hanna Hannuniemi1, Ari Karppinen1, and Jaakko Kukkonen1 John Backman et al.
  • 1Atmospheric Composition Research, Finnish Meteorological Institute, Helsinki, Finland
  • 2Department of Physics, Division of Atmospheric Sciences, University of Helsinki, Helsinki, Finland

Abstract. The meteorological input parameters for urban- and local-scale dispersion models can be evaluated by preprocessing meteorological observations, using a boundary-layer 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.

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Meteorological input parameters for urban- and local-scale dispersion models can be derived from meteorological observations. This study presents a sensitivity analysis of a meteorological model that utilises readily available meteorological data to derive specific parameters required to model the atmospheric dispersion of pollutants. The study shows that wind speed is the most fundamental meteorological input parameter followed by solar radiation.
Meteorological input parameters for urban- and local-scale dispersion models can be derived from...
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