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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2019-60
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-60
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: development and technical paper 11 Apr 2019

Submitted as: development and technical paper | 11 Apr 2019

Review status
This discussion paper is a preprint. A revision of the manuscript was accepted for the journal Geoscientific Model Development (GMD).

The Land Variational Ensemble Data Assimilation fRamework: LaVEnDAR

Ewan Pinnington1, Tristan Quaife1,2, Amos Lawless1,2, Karina Williams3, Tim Arkebauer4, and Dave Scoby4 Ewan Pinnington et al.
  • 1National Centre for Earth Observation, Department of Meteorology, University of Reading, Reading, UK
  • 2School of Mathematical, Physical and Computational Sciences, University Of Reading, Reading, UK
  • 3Met Office Hadley Centre, Exeter, UK
  • 4Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska, USA

Abstract. The Land Variational Ensemble Data Assimilation fRamework (LaVEnDAR) implements the method of Four-Dimensional Ensemble Variational data assimilation for land surface models. Four-Dimensional Ensemble Variational data assimilation negates the often costly calculation of a model adjoint required by traditional variational techniques (such as 4DVar) for optimising parameters/state variables over a time window of observations. In this paper we implement LaVEnDAR with the JULES land surface model. We show the system can recover seven parameters controlling crop behaviour in a set of twin experiments. We run the same experiments at the Mead continuous maize FLUXNET site in Nebraska, USA to show the technique working with real data. We find that the system accurately captures observations of leaf area index, canopy height and gross primary productivity after assimilation and improves posterior estimates of the amount of harvestable material from the maize crop by 74 %. LaVEnDAR requires no modification to the model that it is being used with and is hence able to keep up to date with model releases more easily than other data assimilation methods.

Ewan Pinnington et al.
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Status: final response (author comments only)
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Ewan Pinnington et al.
Ewan Pinnington et al.
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Latest update: 19 Nov 2019
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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.
We present LaVEnDAR, a mathematical method for combining observations with models of the...
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