The Land Variational Ensemble Data Assimilation Framework: LAVENDAR v1.0.0

Pinnington, Ewan; Quaife, Tristan; Lawless, Amos; Williams, Karina; Arkebauer, Tim; Scoby, Dave

doi:https://doi.org/10.5194/gmd-13-55-2020

Articles | Volume 13, issue 1

https://doi.org/10.5194/gmd-13-55-2020

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

https://doi.org/10.5194/gmd-13-55-2020

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

Articles | Volume 13, issue 1

Development and technical paper

|

07 Jan 2020

Development and technical paper |

| 07 Jan 2020

The Land Variational Ensemble Data Assimilation Framework: LAVENDAR v1.0.0

Ewan Pinnington, Tristan Quaife, Amos Lawless, Karina Williams, Tim Arkebauer, and Dave Scoby

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Final revised paper (published on 07 Jan 2020)
Supplement to the final revised paper
Preprint (discussion started on 11 Apr 2019)

Interactive discussion

Status: closed

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

- Printer-friendly version

- Supplement

SC1: 'Executive Editor Comment on gmd-2019-60', Astrid Kerkweg, 24 Apr 2019
- AC1: 'Response to Short Comment 1', Ewan Pinnington, 02 May 2019
RC1: 'Review Pinnington et al.', Anonymous Referee #1, 12 May 2019
- AC2: 'Response to Reviewer 1', Ewan Pinnington, 09 Jul 2019
RC2: 'review', Anonymous Referee #2, 14 Jun 2019
- AC3: 'Response to Reviewer 2', Ewan Pinnington, 09 Jul 2019

Peer-review completion

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

AR by Ewan Pinnington on behalf of the Authors (09 Jul 2019) Author's response Manuscript

ED: Referee Nomination & Report Request started (14 Aug 2019) by Adrian Sandu

RR by Anonymous Referee #1 (29 Aug 2019)

RR by Anonymous Referee #2 (10 Sep 2019)

ED: Publish as is (19 Nov 2019) by Adrian Sandu

AR by Ewan Pinnington on behalf of the Authors (26 Nov 2019) Author's response Manuscript

Short summary

We present LAVENDAR, a mathematical method for combining observations with models of the terrestrial environment. Here we use it to improve estimates of crop growth in the UK Met Office land surface model. However, the method is model agnostic, requires no modification to the underlying code and can be applied to any part of the model. In the example application we improve estimates of maize yield by 74 % by assimilating observations of leaf area, crop height and photosynthesis.