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

Pinnington, Ewan; Quaife, Tristan; Lawless, Amos S.; Williams, Karina; Arkebauer, T. J.; Scoby, David

doi:10.5194/gmd-13-55-2020

Cited by 21 publications

(20 citation statements)

References 53 publications

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“…In this paper we have used an ensemble size of 50, as in related experiments in Pinnington et al (2020) and Liu et al (2008).…”

Section: Methodsmentioning

confidence: 99%

“…In this paper, we have compared JULES soil moisture predictions with soil moisture observations from the COSMOS-UK dataset (Stanley et al, 2019); these observations are measured by cosmic ray neutron sensors (CRNS) over a footprint of up to 12 ha. We have then used the LaVEnDAR four dimensional ensemble variational data assimilation framework (Pinnington et al, 2020) to combine COSMOS-UK soil moisture observations at 16 sites with equivalent JULES soil moisture estimates. We have thereby optimized constants in the Cosby pedotransfer function (Cosby et al, 1984).…”

Section: Introductionmentioning

confidence: 99%

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Using data assimilation to optimize pedotransfer functions using large-scale in-situ soil moisture observations

Cooper¹,

Blyth²,

Cooper³

et al. 2020

Preprint

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Abstract. Soil moisture predictions from land surface models are important in hydrological, ecological and meteorological applications. In recent years the availability of wide-area soil-moisture measurements has increased, but few studies have combined model-based soil moisture predictions with in-situ observations beyond the point scale. Here we show that we can markedly improve soil moisture estimates from the JULES land surface model using field scale observations and data assimilation techniques. Rather than directly updating soil moisture estimates towards observed values, we optimize constants in the underlying pedotransfer functions, which relate soil texture to JULES soil physics parameters. In this way we generate a single set of newly calibrated pedotransfer functions based on observations from a number of UK sites with different soil textures. We demonstrate that calibrating a pedotransfer function in this way can improve the performance of land surface models, leading to the potential for better flood, drought and climate projections.

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“…In this paper we have used an ensemble size of 50, as in related experiments in Pinnington et al (2020) and Liu et al (2008).…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using data assimilation to optimize pedotransfer functions using large-scale in-situ soil moisture observations

Cooper¹,

Blyth²,

Cooper³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…By publicly archiving and reporting results community cyberinfrastructure enables comparisons of different forecasting approaches, future syntheses, and assessment of improvement over time. These features are integral to the vision for such an infrastructure and could then be coupled to, and build upon, existing community tools for workflow scheduling (Oliver et al, 2019) and data assimilation (Fox et al, 2018;Raiho et al, 2020;Pinnington et al, 2020).…”

Section: Data a Ss Imil Ati On And Ecolog Ic Al Forec A S Tingmentioning

confidence: 99%

Beyond ecosystem modeling: A roadmap to community cyberinfrastructure for ecological data‐model integration

Fer

Gardella

Shiklomanov

et al. 2020

Global Change Biology

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“…In order to estimate the identified pedotransfer function parameters we use the LAVENDAR data assimilation framework (Pinnington et al, 2020). This involves running an ensemble of JULES models, with each model in the ensemble utilising a distinct soil ancillary data-set.…”

Section: Data Assimilation Frameworkmentioning

confidence: 99%

Improving Soil Moisture Prediction of a High–Resolution Land Surface Model by Parameterising Pedotransfer Functions through Assimilation of SMAP Satellite Data

Pinnington¹,

Amezcua²,

Cooper³

et al. 2020

Preprint

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Abstract. Pedotransfer functions are used to relate gridded databases of soil texture information to the soil hydraulic and thermal parameters of land surface models. The parameters within these pedotransfer functions are uncertain and calibrated through analyses of point soil samples. How these calibrations relate to the soil parameters at the spatial scale of modern land surface models is unclear, because gridded databases of soil texture represent an area average. We present a novel approach for calibrating such pedotransfer functions to improve land surface model soil moisture prediction by using observations from the Soil Moisture Active Passive (SMAP) satellite mission within a data assimilation framework. Unlike traditional calibration procedures data assimilation always takes into account the relative uncertainties given to both model and observed estimates to find a maximum likelihood estimate. After performing the calibration procedure we find improved estimates of soil moisture for the JULES land surface model (run at a 1 km resolution) when compared to estimates from a cosmic-ray soil moisture monitoring network (COSMOS-UK). The spatial resolution of these COSMOS probes is much more representative of the 1 km model grid than traditional point based soil moisture sensors. For 11 cosmic-ray neutron soil moisture probes located across the modelled domain we find an average 22 % reduction in root-mean squared error, a 16 % reduction in unbiased root-mean squared error and a 16 % increase in correlation after using data assimilation techniques to retrieve new pedotransfer function parameters.

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

Cited by 21 publications

References 53 publications

Using data assimilation to optimize pedotransfer functions using large-scale in-situ soil moisture observations

Using data assimilation to optimize pedotransfer functions using large-scale in-situ soil moisture observations

Beyond ecosystem modeling: A roadmap to community cyberinfrastructure for ecological data‐model integration

Improving Soil Moisture Prediction of a High–Resolution Land Surface Model by Parameterising Pedotransfer Functions through Assimilation of SMAP Satellite Data

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