Consistent retrieval of land surface radiation products from EO, including traceable uncertainty estimates

Kaminski, Thomas; Pinty, Bernard; Voßbeck, Michael; Lopatka, Maciej; Gobron, Nadine; Robustelli, Monica

doi:https://doi.org/10.5194/bg-14-2527-2017

Articles | Volume 14, issue 9

https://doi.org/10.5194/bg-14-2527-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue:

Data assimilation in carbon/biogeochemical cycles: consistent...

https://doi.org/10.5194/bg-14-2527-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 14, issue 9

Research article

|

17 May 2017

Research article |

| 17 May 2017

Consistent retrieval of land surface radiation products from EO, including traceable uncertainty estimates

Thomas Kaminski, Bernard Pinty, Michael Voßbeck, Maciej Lopatka, Nadine Gobron, and Monica Robustelli

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Final revised paper (published on 17 May 2017)
Preprint (discussion started on 09 Aug 2016)

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'Good science, poor writing', Anonymous Referee #1, 24 Oct 2016
RC2: 'Referee Comments', Anonymous Referee #2, 24 Nov 2016
AC1: 'author response', Thomas Kaminski, 22 Dec 2016

Peer-review completion

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

ED: Publish subject to minor revisions (Editor review) (31 Jan 2017) by Martin Heimann

AR by Thomas Kaminski on behalf of the Authors (02 Feb 2017) Author's response Manuscript

ED: Publish subject to technical corrections (11 Feb 2017) by Martin Heimann

Short summary

We present the Joint Research Centre Two-stream Inversion Package (JRC-TIP) for retrieval of variables characterising the state of the vegetation–soil system. The system provides a set of land surface variables that satisfy all requirements for assimilation into the land component of climate and numerical weather prediction models.