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See detailConstraining model biases in a global general circulation model with ensemble data assimilation methods
Canter, Martin ULiege

Doctoral thesis (2017)

A new method of bias correction using an ensemble transform Kalman filter as data assimilation scheme is developed. The objective is to create a stochastic forcing term which will partially remove the ... [more ▼]

A new method of bias correction using an ensemble transform Kalman filter as data assimilation scheme is developed. The objective is to create a stochastic forcing term which will partially remove the bias from numerical models. The forcing term is considered as a parameter to be estimated through state vector augmentation and the assimilation of observations. The theoretical formulation of this method is introduced in the general context of numerical modelling. A specially designed and modified Lorenz '96 model is studied, and provides a testing environment for this new bias correction method. Several different aspects are considered through both single and iterative assimilation in a twin experiment. The method is then implemented on the global general circulation model of the ocean NEMO-LIM2. The forcing term generation is detailed to respect particular physical constraints. Again, a twin experiment allows to assess the efficiency of the method on a realistic model. The assimilation of sea surface height observations is performed, with sea surface salinity and temperature as control variable. Subsequently, a multivariate assimilation shows further improvement of the bias correction. Finally, the method is confronted to real sea surface height observations from the CNES-CLS09 global mean dynamic topography. A thorough study of the NEMO-LIM2 model response to the bias correction forcing term is proposed, and specific results are highlighted. An iterative assimilation concludes the method investigation. Possible ideas and future developments are suggested. [less ▲]

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Peer Reviewed
See detailCorrecting circulation biases in a lower-resolution global general circulation model with data assimilation
Canter, Martin ULiege; Barth, Alexander ULiege; Beckers, Jean-Marie ULiege

in Ocean Dynamics (2016)

In this study, we aim at developing a new method of bias correction using data assimilation. This method is based on the stochastic forcing of a model to correct bias by directly adding an additional ... [more ▼]

In this study, we aim at developing a new method of bias correction using data assimilation. This method is based on the stochastic forcing of a model to correct bias by directly adding an additional source term into the model equations. This method is presented and tested first with a twin experiment on a fully controlled Lorenz ’96 model. It is then applied to the lower-resolution global circulation NEMO-LIM2 model, with both a twin experiment and a real case experiment. Sea surface height observations are used to create a forcing to correct the poorly located and estimated currents. Validation is then performed throughout the use of other variables such as sea surface temperature and salinity. Results show that the method is able to consistently correct part of the model bias. The bias correction term is presented and is consistent with the limitations of the global circulation model causing bias on the oceanic currents. [less ▲]

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See detailCorrecting Biases in a lower resolution global circulation model with data assimilation
Canter, Martin ULiege; Barth, Alexander ULiege

Poster (2016)

With this work, we aim at developping a new method of bias correction using data assimilation. This method is based on the stochastic forcing of a model to correct bias. First, through a preliminary run ... [more ▼]

With this work, we aim at developping a new method of bias correction using data assimilation. This method is based on the stochastic forcing of a model to correct bias. First, through a preliminary run, we estimate the bias of the model and its possible sources. Then, we establish a forcing term which is directly added inside the model’s equations. We create an ensemble of runs and consider the forcing term as a control variable during the assimilation of observations. We then use this analysed forcing term to correct the bias of the model. Since the forcing is added inside the model, it acts as a source term, unlike external forcings such as wind. This procedure has been developed and successfully tested with a twin experiment on a Lorenz 95 model. It is currently being applied and tested on the sea ice ocean NEMO LIM model, which is used in the PredAntar project. NEMO LIM is a global and low resolution (2 degrees) coupled model (hydrodynamic model and sea ice model) with long time steps allowing simulations over several decades. Due to its low resolution, the model is subject to bias in area where strong currents are present. We aim at correcting this bias by using perturbed current fields from higher resolution models and randomly generated perturbations. The random perturbations need to be constrained in order to respect the physical properties of the ocean, and not create unwanted phenomena. To construct those random perturbations, we first create a random field with the Diva tool (Data-Interpolating Variational Analysis). Using a cost function, this tool penalizes abrupt variations in the field, while using a custom correlation length. It also decouples disconnected areas based on topography. Then, we filter the field to smoothen it and remove small scale variations. We use this field as a random stream function, and take its derivatives to get zonal and meridional velocity fields. We also constrain the stream function along the coasts in order not to have currents perpendicular to the coast. The randomly generated stochastic forcing are then directly injected into the NEMO LIM model’s equations in order to force the model at each timestep, and not only during the assimilation step. Results from a twin experiment will be presented. This method is being applied to a real case, with observations on the sea surface height available from the mean dynamic topography of CNES (Centre national d’études spatiales). The model, the bias correction, and more extensive forcings, in particular with a three dimensional structure and a time-varying component, will also be presented. [less ▲]

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See detailAssimilation of sea surface temperature, sea ice concentration and sea ice drift in a model of the Southern Ocean
Barth, Alexander ULiege; Canter, Martin ULiege; Van Schaeybroeck, Bert et al

in Ocean Modelling (2015), 93

Current ocean models have relatively large errors and biases in the Southern Ocean. The aim of this study is to provide a reanalysis from 1985 to 2006 assimilating sea surface temperature, sea ice ... [more ▼]

Current ocean models have relatively large errors and biases in the Southern Ocean. The aim of this study is to provide a reanalysis from 1985 to 2006 assimilating sea surface temperature, sea ice concentration and sea ice drift. In the following it is also shown how surface winds in the Southern Ocean can be improved using sea ice drift estimated from infrared radiometers. Such satellite observations are available since the late seventies and have the potential to improve the wind forcing before more direct measurements of winds over the ocean are available using scatterometry in the late nineties. The model results are compared to the assimilated data and to independent measurements (the World Ocean Database 2009 and the mean dynamic topography based on observations). The overall improvement of the assimilation is quantified, in particular the impact of the assimilation on the representation of the polar front is discussed. Finally a method to identify model errors in the Antarctic sea ice area is proposed based on Model Output Statistics techniques using a series of potential predictors. This approach provides new directions for model improvements. [less ▲]

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See detailOcean Modeling: Bias correction through stochastic forcing.
Canter, Martin ULiege; Barth, Alexander ULiege

Conference (2015, April 14)

With this work, we aim at developping a new method of bias correction using data assimilation. This method is based on the stochastic forcing of a model to correct bias. First, through a preliminary run ... [more ▼]

With this work, we aim at developping a new method of bias correction using data assimilation. This method is based on the stochastic forcing of a model to correct bias. First, through a preliminary run, we estimate the bias of the model and its possible sources. Then, we es- tablish a forcing term which is directly added inside the model’s equations. We create an ensemble of runs and consider the forcing term as a control variable during the assimilation of observations. We then use this analysed forcing term to correct the bias of the model. Since the forcing is added inside the model, it acts as a source term, unlike external forcings such as wind. This procedure has been developed and successfully tested with a twin experiment on a Lorenz 95 model. Indeed, we were able to estimate and recover an artificial bias that had been added into the model. This bias had a spatial structure and was constant through time. The mean and behaviour of the corrected model corresponded to those the reference model. It is currently being applied and tested on the sea ice ocean NEMO LIM model, which is used in the PredAntar project. NEMO LIM is a global and low resolution (2 degrees) coupled model (hydrodynamic model and sea ice model) with long time steps allowing simulations over several decades. Due to its low resolution, the model is subject to bias in area where strong currents are present. We aim at correcting this bias by using perturbed current fields from higher resolution models and randomly generated perturbations. The random perturbations need to be constrained in order to respect the physical properties of the ocean, and not create unwanted phenomena. To construct those random perturbations, we first create a random field with the Diva tool (Data-Interpolating Variational Analysis). Using a cost function, this tool penalizes abrupt variations in the field, while using a custom correlation length. It also decouples disconnected areas based on topography. Then, we filter the field to smoothen it and remove small scale variations. We use this field as a random stream function, and take its derivatives to get zonal and meridional velocity fields. We also constrain the stream function along the coasts in order not to have currents perpendicular to the coast. The randomly generated stochastic forcing are then directly injected into the NEMO LIM model’s equa- tions in order to force the model at each timestep, and not only during the assimilation step. The first results on a twin experiment with the NEMO LIM model will be presented. [less ▲]

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See detailAssimilation of sea surface temperature, sea ice concentration and sea ice drift in a model of the Southern Ocean
Barth, Alexander ULiege; Canter, Martin ULiege; Van Schaeybroeck, Bert et al

Poster (2015)

Current ocean models have relatively large errors and biases in the Southern Ocean. The aim of this study is to provide a reanalysis from 1985 to 2006 assimilating sea surface temperature, sea ice ... [more ▼]

Current ocean models have relatively large errors and biases in the Southern Ocean. The aim of this study is to provide a reanalysis from 1985 to 2006 assimilating sea surface temperature, sea ice concentration and sea ice drift. In the following it is also shown how surface winds in the Southern Ocean can be improved using sea ice drift es- timated from infrared radiometers. Such satellite observations are available since the late seventies and have the potential to improve the wind forcing before more direct measure- ments of winds over the ocean are available using scatterometry in the late nineties. The model results are compared to the assimilated data and to independent measurements (the World Ocean Database 2009 and the mean dynamic topography based on observations). The overall improvement of the assimilation is quantified, in particular the impact of the assimilation on the representation of the polar front is discussed. Finally a method to iden- tify model errors in the Antarctic sea ice area is proposed based on Model Output Statistics techniques using a series of potential predictors. This approach provides new directions for model improvements. [less ▲]

Detailed reference viewed: 21 (3 ULiège)
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Peer Reviewed
See detailLocal ensemble assimilation scheme with global constraints and conservation
Barth, Alexander ULiege; Yan, Yajing ULiege; Canter, Martin ULiege et al

Conference (2015)

Ensemble assimilation schemes applied in their original, global formulation have no problem in respecting linear conservation properties if the ensemble perturbations are setup accordingly. For realistic ... [more ▼]

Ensemble assimilation schemes applied in their original, global formulation have no problem in respecting linear conservation properties if the ensemble perturbations are setup accordingly. For realistic ocean systems, only a relatively small number of ensemble members can be calculated. A localization of the ensemble increment is thus necessary to filter out spurious long-range correlations. However, the conservation of the global property will be lost if the assimilation is performed locally since the conservation requires a coupling between model grid points, which is filtered out by the localization. In the ocean, the distribution of observations is highly inhomogeneous. System- atic errors of the observed parts of the ocean state can lead to spurious systematic adjust- ments of the non-observed part of the ocean state due to data assimilation. As a result, global properties which should be conserved, increase or decrease in long-term simulations. We propose an assimilation scheme (with stochastic or deterministic analysis steps) which is formulated globally (i.e. for the whole state vector) but where spurious long-range correlations can be filtered out. The scheme can thus be used to enforce global conservation properties and non-local observation operators. Both aspects are indeed linked since one can introduce the global conservation as a weak constraint by using a global ob- servation operator. The conserved property becomes thus an observed value. The proposed scheme is tested with the Kuramoto-Sivashinsky model which is conservative. The benefit compared to the traditional covariance localization scheme (with an ad-hoc step enforcing conservation) where observations are assimilated sequentially is shown. The assimilation scheme is suitable to be implemented on parallel computers where the number of available computing cores is a multiple of the ensemble size. [less ▲]

Detailed reference viewed: 27 (3 ULiège)
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See detailBias correction with data assimilation
Canter, Martin ULiege; Barth, Alexander ULiege

Conference (2015)

With this work, we aim at developping a new method of bias correction using data assimilation. This method is based on the stochastic forcing of a model to correct bias. First, through a preliminary run ... [more ▼]

With this work, we aim at developping a new method of bias correction using data assimilation. This method is based on the stochastic forcing of a model to correct bias. First, through a preliminary run, we estimate the bias of the model and its possible sources. Then, we establish a forcing term which is directly added inside the model’s equa- tions. We create an ensemble of runs and consider the forcing term as a control variable during the assimilation of observations. We then use this analysed forcing term to correct the bias of the model. Since the forcing is added inside the model, it acts as a source term, unlike external forcings such as wind. This procedure has been developed and successfully tested with a twin experiment on a Lorenz 95 model. Indeed, we were able to estimate and recover an artificial bias that had been added into the model. This bias had a spatial structure and was constant through time. The mean and behaviour of the corrected model corresponded to those the reference model. It is currently being applied and tested on the sea ice ocean NEMO LIM model, which is used in the PredAntar project. NEMO LIM is a global and low resolution (2 de- grees) coupled model (hydrodynamic model and sea ice model) with long time steps allow- ing simulations over several decades. Due to its low resolution, the model is subject to bias in area where strong currents are present. We aim at correcting this bias by using perturbed current fields from higher resolution models and randomly generated perturbations. The random perturbations need to be constrained in order to respect the physical properties of the ocean, and not create unwanted phenomena. To construct those random perturbations, we first create a random field with the Diva tool (Data-Interpolating Varia- tional Analysis). Using a cost function, this tool penalizes abrupt variations in the field, while using a custom correlation length. It also decouples disconnected areas based on to- pography. Then, we filter the field to smoothen it and remove small scale variations. We use this field as a random stream function, and take its derivatives to get zonal and meridional velocity fields. We also constrain the stream function along the coasts in order not to have currents perpendicular to the coast. The randomly generated stochastic forcing are then directly injected into the NEMO LIM model’s equations in order to force the model at each timestep, and not only during the assimilation step. The first results on a twin experiment with the NEMO LIM model will be presented. [less ▲]

Detailed reference viewed: 22 (7 ULiège)
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See detailBias correction using data assimilation: Application on the Lorenz ’95 and NEMO-LIM models.
Canter, Martin ULiege; Barth, Alexander ULiege

Poster (2014, May 01)

Data assimilation has been used for decades in fields like engineering or signal processing to improve forecast models. Ensemble Kalman filters and other sequential data assimilation methods are examples ... [more ▼]

Data assimilation has been used for decades in fields like engineering or signal processing to improve forecast models. Ensemble Kalman filters and other sequential data assimilation methods are examples of developments which reduce the uncertainty of the model by taking observations into account. The widespread interest in addressing systematic forecast model errors only arose when the advances in modelling, data assimilation and computational power had reduced random errors to the point of commensurability with systematic errors, also known as bias. We present here a new method of bias correction using data assimilation. This method is based on the stochastic forcing of a model to correct bias. First, through a preliminary run, we estimate the bias of the model and its possible sources. Then, we establish a forcing term which is directly added inside the model’s equations. We create an ensemble of runs and consider the forcing term as a control variable during the assimilation of observations. We then use this analysed forcing term to correct the bias of the model. Since the forcing is added inside the model, it acts as a source term, unlike external forcings such as wind. This procedure has been developed and successfully tested with a twin experiment on a Lorenz 95 model. Indeed, we were able to estimate and recover an artificial bias that had been added into the model. This bias had a spatial structure and was constant through time. The mean and behaviour of the corrected model corresponded to those the reference model. It is currently being applied and tested on the sea ice ocean NEMO LIM model, which is used in the PredAntar project. NEMO LIM is a global and low resolution (2 degrees) coupled model (hydrodynamic model and sea ice model) with long time steps allowing simulations over several decades. Due to its low resolution, the model is subject to bias in area where strong currents are present. We aim at correcting this bias by using perturbed current fields from higher resolution models and randomly generated perturbations. [less ▲]

Detailed reference viewed: 31 (4 ULiège)
Full Text
See detailLocal ensemble assimilation scheme with global constraints and conservation
Barth, Alexander ULiege; Yan, Yajing ULiege; Canter, Martin ULiege et al

Poster (2014, April)

Ensemble assimilation schemes applied in their original, global formulation have no problem in respecting linear conservation properties if the ensemble perturbations are setup accordingly. For realistic ... [more ▼]

Ensemble assimilation schemes applied in their original, global formulation have no problem in respecting linear conservation properties if the ensemble perturbations are setup accordingly. For realistic ocean systems, only a relatively small number of ensemble members can be calculated. A localization of the ensemble increment is thus necessary to filter out spurious long-range correlations. However, the conservation of the global property will be lost if the assimilation is performed locally since the conservation requires a coupling between model grid points, which is filtered out by the localization. In the ocean, the distribution of observations is highly inhomogeneous. Systematic errors of the observed parts of the ocean state can lead to spurious systematic adjustments of the non-observed part of the ocean state due to data assimilation. As a result, global properties which should be conserved, increase or decrease in long-term simulations. We propose an assimilation scheme (with stochastic or deterministic analysis steps) which is formulated globally (i.e. for the whole state vector) but where spurious long-range correlations can be filtered out. The scheme can thus be used to enforce global conservation properties and non-local observation operators. Both aspects are indeed linked since one can introduce the global conservation as a weak constraint by using a global observation operator. The conserved property becomes thus an observed value. The proposed scheme is tested with the Kuramoto-Sivashinsky model which is conservative. The benefit compared to the traditional covariance localization scheme (with an ad-hoc step enforcing conservation) where observations are assimilated sequentially is shown. The assimilation scheme is suitable to be implemented on parallel computers where the number of available computing cores is a multiple of the ensemble size. [less ▲]

Detailed reference viewed: 56 (3 ULiège)