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

Viewed

Total article views: 2,990 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,157 718 115 2,990 207 129 128
  • HTML: 2,157
  • PDF: 718
  • XML: 115
  • Total: 2,990
  • Supplement: 207
  • BibTeX: 129
  • EndNote: 128
Views and downloads (calculated since 13 Jan 2017)
Cumulative views and downloads (calculated since 13 Jan 2017)

Viewed (geographical distribution)

Total article views: 2,990 (including HTML, PDF, and XML) Thereof 2,766 with geography defined and 224 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 30 Dec 2024
Download
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.
Read more