Computational efficiency for the surface renewal method

Kelley, Jason; Higgins, Chad

doi:https://doi.org/10.5194/amt-11-2151-2018

Articles | Volume 11, issue 4

https://doi.org/10.5194/amt-11-2151-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/amt-11-2151-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 11, issue 4

Research article

|

16 Apr 2018

Research article |

| 16 Apr 2018

Computational efficiency for the surface renewal method

Jason Kelley and Chad Higgins

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Final revised paper (published on 16 Apr 2018)
Supplement to the final revised paper
Preprint (discussion started on 01 Jun 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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- Supplement

RC1: 'Review of the article: Computational Efficiency for the Surface Renewal Method', Anonymous Referee #2, 28 Jun 2017
- AC1: 'response to anon. Referee #2', Jason Kelley, 04 Feb 2018
RC2: 'comments on computational efficiency for surface renewal method', Anonymous Referee #3, 07 Jan 2018
- AC2: 'Response to Reviewer #3', Jason Kelley, 04 Feb 2018

Peer-review completion

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

AR by Jason Kelley on behalf of the Authors (05 Mar 2018) Manuscript

ED: Referee Nomination & Report Request started (06 Mar 2018) by Andrew Sayer

RR by Anonymous Referee #3 (15 Mar 2018)

ED: Publish as is (15 Mar 2018) by Andrew Sayer

AR by Jason Kelley on behalf of the Authors (21 Mar 2018)

Short summary

Measuring fluxes of energy and trace gases using the surface renewal (SR) method can be economical and robust, but it requires computationally intensive calculations. Several new algorithms were written to perform the required calculations more efficiently and rapidly, and were tested with field data and computationally rigorous SR methods. These efficient algorithms facilitate expanded use of SR in atmospheric experiments, for applied monitoring, and in novel field implementations.