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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/gmd-2016-308
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Model evaluation paper
13 Jan 2017
Review status
This discussion paper is under review for the journal Geoscientific Model Development (GMD).
Sensitivity analysis of the meteorological pre-processor MPP-FMI 3.0 using algorithmic differentiation
John Backman1, Curtis Wood1, Mikko Auvinen1,2, Leena Kangas1, Hanna Hannuniemi1, Ari Karppien1, and Jaakko Kukkonen1 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 pre-processing meteorological observations, using a boundary-layer parametrization model. This study presents a sensitivity analysis of a meteorological pre-processor model (MPP-FMI) that utilises readily available meteorological data as input. The sensitivity of the pre-processor 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 pre-processing 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.

Citation: Backman, J., Wood, C., Auvinen, M., Kangas, L., Hannuniemi, H., Karppien, A., and Kukkonen, J.: Sensitivity analysis of the meteorological pre-processor MPP-FMI 3.0 using algorithmic differentiation, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2016-308, in review, 2017.
John Backman et al.
John Backman et al.
John Backman et al.

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Short summary
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 in 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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