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.
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.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
07 Jan 2020
Development and technical paper |
| 07 Jan 2020
| 07 Jan 2020
The Land Variational Ensemble Data Assimilation Framework: LAVENDAR v1.0.0
Ewan Pinnington
CORRESPONDING AUTHOR
National Centre for Earth Observation, Department of Meteorology, University of Reading, Reading, UK
Tristan Quaife
National Centre for Earth Observation, Department of Meteorology, University of Reading, Reading, UK
School of Mathematical, Physical and Computational Sciences, University Of Reading, Reading, UK
Amos Lawless
National Centre for Earth Observation, Department of Meteorology, University of Reading, Reading, UK
School of Mathematical, Physical and Computational Sciences, University Of Reading, Reading, UK
Karina Williams
Met Office Hadley Centre, Exeter, UK
Tim Arkebauer
Department of Agronomy and Horticulture, University of Nebraska–Lincoln, Lincoln, Nebraska, USA
Dave Scoby
Department of Agronomy and Horticulture, University of Nebraska–Lincoln, Lincoln, Nebraska, USA
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Cited
14 citations as recorded by crossref.
- Using data assimilation to optimize pedotransfer functions using field-scale in situ soil moisture observations E. Cooper et al. 10.5194/hess-25-2445-2021
- Future increases in soil moisture drought frequency at UK monitoring sites: merging the JULES land model with observations and convection-permitting UK climate projections M. Szczykulska et al. 10.1088/1748-9326/ad7045
- Beyond ecosystem modeling: A roadmap to community cyberinfrastructure for ecological data‐model integration I. Fer et al. 10.1111/gcb.15409
- Simplified Kalman smoother and ensemble Kalman smoother for improving reanalyses B. Dong et al. 10.5194/gmd-16-4233-2023
- From Ecosystem Observation to Environmental Decision-Making: Model-Data Fusion as an Operational Tool T. Smallman et al. 10.3389/ffgc.2021.818661
- Combining local model calibration with the emergent constraint approach to reduce uncertainty in the tropical land carbon cycle feedback N. Raoult et al. 10.5194/esd-14-723-2023
- Scale variance in the carbon dynamics of fragmented, mixed-use landscapes estimated using model–data fusion D. Milodowski et al. 10.5194/bg-20-3301-2023
- Development of an open-source regional data assimilation system in PEcAn v. 1.7.2: application to carbon cycle reanalysis across the contiguous US using SIPNET H. Dokoohaki et al. 10.5194/gmd-15-3233-2022
- A framework for improved predictions of the climate impacts on potential yields of UK winter wheat and its applicability to other UK crops G. Hayman et al. 10.1016/j.cliser.2024.100479
- Cocoa plant productivity in West Africa under climate change: a modelling and experimental study E. Black et al. 10.1088/1748-9326/abc3f3
- Exploring the potential of history matching for land surface model calibration N. Raoult et al. 10.5194/gmd-17-5779-2024
- Improving soil moisture prediction of a high-resolution land surface model by parameterising pedotransfer functions through assimilation of SMAP satellite data E. Pinnington et al. 10.5194/hess-25-1617-2021
- Quantifying and Reducing Uncertainty in Global Carbon Cycle Predictions: Lessons and Perspectives From 15 Years of Data Assimilation Studies With the ORCHIDEE Terrestrial Biosphere Model N. MacBean et al. 10.1029/2021GB007177
- An Agenda for Land Data Assimilation Priorities: Realizing the Promise of Terrestrial Water, Energy, and Vegetation Observations From Space S. Kumar et al. 10.1029/2022MS003259
14 citations as recorded by crossref.
- Using data assimilation to optimize pedotransfer functions using field-scale in situ soil moisture observations E. Cooper et al. 10.5194/hess-25-2445-2021
- Future increases in soil moisture drought frequency at UK monitoring sites: merging the JULES land model with observations and convection-permitting UK climate projections M. Szczykulska et al. 10.1088/1748-9326/ad7045
- Beyond ecosystem modeling: A roadmap to community cyberinfrastructure for ecological data‐model integration I. Fer et al. 10.1111/gcb.15409
- Simplified Kalman smoother and ensemble Kalman smoother for improving reanalyses B. Dong et al. 10.5194/gmd-16-4233-2023
- From Ecosystem Observation to Environmental Decision-Making: Model-Data Fusion as an Operational Tool T. Smallman et al. 10.3389/ffgc.2021.818661
- Combining local model calibration with the emergent constraint approach to reduce uncertainty in the tropical land carbon cycle feedback N. Raoult et al. 10.5194/esd-14-723-2023
- Scale variance in the carbon dynamics of fragmented, mixed-use landscapes estimated using model–data fusion D. Milodowski et al. 10.5194/bg-20-3301-2023
- Development of an open-source regional data assimilation system in PEcAn v. 1.7.2: application to carbon cycle reanalysis across the contiguous US using SIPNET H. Dokoohaki et al. 10.5194/gmd-15-3233-2022
- A framework for improved predictions of the climate impacts on potential yields of UK winter wheat and its applicability to other UK crops G. Hayman et al. 10.1016/j.cliser.2024.100479
- Cocoa plant productivity in West Africa under climate change: a modelling and experimental study E. Black et al. 10.1088/1748-9326/abc3f3
- Exploring the potential of history matching for land surface model calibration N. Raoult et al. 10.5194/gmd-17-5779-2024
- Improving soil moisture prediction of a high-resolution land surface model by parameterising pedotransfer functions through assimilation of SMAP satellite data E. Pinnington et al. 10.5194/hess-25-1617-2021
- Quantifying and Reducing Uncertainty in Global Carbon Cycle Predictions: Lessons and Perspectives From 15 Years of Data Assimilation Studies With the ORCHIDEE Terrestrial Biosphere Model N. MacBean et al. 10.1029/2021GB007177
- An Agenda for Land Data Assimilation Priorities: Realizing the Promise of Terrestrial Water, Energy, and Vegetation Observations From Space S. Kumar et al. 10.1029/2022MS003259
Latest update: 20 Nov 2024
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...
