Fast matrix treatment of 3-D radiative transfer in vegetation canopies: SPARTACUS-Vegetation 1.1

Hogan, Robin J.; Quaife, Tristan; Braghiere, Renato

doi:https://doi.org/10.5194/gmd-11-339-2018

Articles | Volume 11, issue 1

https://doi.org/10.5194/gmd-11-339-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-11-339-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 11, issue 1

Model description paper

|

23 Jan 2018

Model description paper |

| 23 Jan 2018

Fast matrix treatment of 3-D radiative transfer in vegetation canopies: SPARTACUS-Vegetation 1.1

Robin J. Hogan, Tristan Quaife, and Renato Braghiere

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Final revised paper (published on 23 Jan 2018)
Supplement to the final revised paper
Preprint (discussion started on 14 Sep 2017)

Interactive discussion

Status: closed

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

- Printer-friendly version

- Supplement

SC1: 'Code Accessibility', Lutz Gross, 10 Oct 2017
- AC2: 'Reply to SC1', Robin Hogan, 06 Dec 2017
RC1: 'Review of Fast matrix treatment of 3D radiative transfer in vegetation canopies: SPARTACUS-Vegetation 1.0', Anonymous Referee #1, 19 Oct 2017
- AC1: 'Response to referees' comments', Robin Hogan, 06 Dec 2017
RC2: 'Review of Manuscript', Anonymous Referee #2, 21 Oct 2017
- AC1: 'Response to referees' comments', Robin Hogan, 06 Dec 2017
AC1: 'Response to referees' comments', Robin Hogan, 06 Dec 2017

Peer-review completion

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

AR by Robin Hogan on behalf of the Authors (07 Dec 2017) Author's response Manuscript

ED: Publish subject to technical corrections (11 Dec 2017) by Jatin Kala

Short summary

This paper describes a fast new method for calculating how much sunlight is absorbed and reflected by forests and other types of vegetation, rigorously taking account of the complex 3-D structure. Careful evaluation shows it to perform well even in difficult scenes with snow on the ground. The method is suitable for use within the computer models used to make weather and climate forecasts, where it has the potential to improve predictions of near-surface temperature and photosynthesis rates.