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

Backman, John; Wood, Curtis R.; Auvinen, Mikko; Kangas, Leena; Hannuniemi, Hanna; Karppinen, Ari; Kukkonen, Jaakko

doi:https://doi.org/10.5194/gmd-10-3793-2017

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

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Final revised paper (published on 17 Oct 2017)
Supplement to the final revised paper
Preprint (discussion started on 13 Jan 2017)
Supplement to the preprint

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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RC1: 'Review of paper', Laurent Hascoet, 24 Feb 2017
- AC1: 'Reply to comment on Sensitivity analysis of the meteorological pre-processor MPP-FMI 3.0 using algorithmic differentiation by Laurent Hascoet', John Backman, 30 Aug 2017
RC2: 'Review of “Sensitivity analysis of the meteorological pre-processor MPP-FMI 3.0 using algorithmic differentiation » submitted to GMDD by Backman et al.', Anonymous Referee #2, 28 Jun 2017
- AC2: 'Reply to comment on Sensitivity analysis of the meteorological pre-processor MPP-FMI 3.0 using algorithmic differentiation by the anonymous referee #2', John Backman, 30 Aug 2017

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by John Backman on behalf of the Authors (30 Aug 2017) Author's response Manuscript

ED: Publish as is (03 Sep 2017) by David Ham

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.