Articles | Volume 10, issue 10
https://doi.org/10.5194/gmd-10-3793-2017
https://doi.org/10.5194/gmd-10-3793-2017
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 Backman, Curtis R. Wood, Mikko Auvinen, Leena Kangas, Hanna Hannuniemi, Ari Karppinen, and Jaakko Kukkonen

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Cited articles

Fisher, B., Kukkonen, J., and Schatzmann, M.: Meteorology applied to urban air pollution problems COST 715, Int. J. Environ. Pollut., 16, 560–570, https://doi.org/10.1504/IJEP.2001.000650, 2001.
Griewank, A. and Walther, A.: Evaluating Derivatives Principles and Techniques of Algorithmic Differentiation, vol. 2, Society for Industrial and Applied Mathematics, Philadelphia, USA, 1–56, 2008.
Guerrette, J. J. and Henze, D. K.: Development and application of the WRFPLUS-Chem online chemistry adjoint and WRFDA-Chem assimilation system, Geosci. Model Dev., 8, 1857–1876, https://doi.org/10.5194/gmd-8-1857-2015, 2015.
Hascoet, L. and Pascual, V.: The Tapenade Automatic Differentiation Tool: principles, model, and specification, ACM T. Math. Software, 39, 20:1–20:43, https://doi.org/10.1145/2450153.2450158, 2013.
Karppinen, A., Joffre, S. M., and Vaajama, P.: Boundary-layer parameterization for Finnish regulatory dispersion models, Int. J. Environ. Pollut., 8, 3–6, 1997.
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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 is the most fundamental meteorological input parameter followed by solar radiation.
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