TIER version 1.0: an open-source Topographically InformEd Regression (TIER) model to estimate spatial meteorological fields

Newman, Andrew J.; Clark, Martyn P.

doi:https://doi.org/10.5194/gmd-13-1827-2020

Articles | Volume 13, issue 4

https://doi.org/10.5194/gmd-13-1827-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-13-1827-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 13, issue 4

Model description paper

|

06 Apr 2020

Model description paper |

| 06 Apr 2020

TIER version 1.0: an open-source Topographically InformEd Regression (TIER) model to estimate spatial meteorological fields

Andrew J. Newman and Martyn P. Clark

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Final revised paper (published on 06 Apr 2020)
Preprint (discussion started on 03 Jul 2019)

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'Review', Anonymous Referee #1, 19 Sep 2019
RC2: 'Emphasize more on the relative uncertainty between model parameters', Anonymous Referee #2, 30 Sep 2019
AC1: 'Author Comments to Reviewers', Andrew Newman, 21 Nov 2019

Peer-review completion

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

AR by Andrew Newman on behalf of the Authors (19 Dec 2019) Author's response Manuscript

ED: Publish as is (12 Feb 2020) by Richard Neale

AR by Andrew Newman on behalf of the Authors (21 Feb 2020)

Short summary

This paper introduces the Topographically InformEd Regression (TIER) model, which uses terrain attributes to turn observations of precipitation and temperature into spatial maps. TIER allows our understanding of complex atmospheric processes such as terrain-enhanced precipitation to be modeled in a very simple way. TIER lets users change the model so they can experiment with different ways of making maps. A key conclusion is that small changes in TIER will change the final map.