Sensitivity analysis of the meteorological pre-processor MPP-FMI 3.0 using
John Backman1, Curtis Wood1, Mikko Auvinen1,2, Leena Kangas1, Hanna Hannuniemi1, Ari Karppien1, and Jaakko Kukkonen11Atmospheric Composition Research, Finnish Meteorological Institute, Helsinki, Finland 2Department of Physics, Division of Atmospheric Sciences, University of Helsinki, Helsinki, Finland
Received: 16 Dec 2016 – Accepted for review: 13 Jan 2017 – Discussion started: 13 Jan 2017
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.
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., doi:10.5194/gmd-2016-308, in review, 2017.