Ocean Modeling: Bias correction through stochastic forcing.
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...
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ftorbi:oai:orbi.ulg.ac.be:2268/190987 2024-04-21T08:11:23+00:00 Ocean Modeling: Bias correction through stochastic forcing. Canter, Martin Barth, Alexander Geohydrodynamics and Environment Reasearch - GHER 2015-04-14 https://orbi.uliege.be/handle/2268/190987 https://orbi.uliege.be/bitstream/2268/190987/1/EGU2015MartinCanter_3.pdf en eng https://orbi.uliege.be/handle/2268/190987 info:hdl:2268/190987 https://orbi.uliege.be/bitstream/2268/190987/1/EGU2015MartinCanter_3.pdf open access http://purl.org/coar/access_right/c_abf2 info:eu-repo/semantics/openAccess European Geosciences Union General Assembly 2015, Vienna, Austria [AT], 12 – 17 April 2015 Data assimilation Bias Correction Physical chemical mathematical & earth Sciences Earth sciences & physical geography Physique chimie mathématiques & sciences de la terre Sciences de la terre & géographie physique conference paper not in proceedings http://purl.org/coar/resource_type/c_18cp info:eu-repo/semantics/conferencePaper 2015 ftorbi 2024-03-27T14:47:03Z 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 ... Conference Object Sea ice University of Liège: ORBi (Open Repository and Bibliography) |
institution |
Open Polar |
collection |
University of Liège: ORBi (Open Repository and Bibliography) |
op_collection_id |
ftorbi |
language |
English |
topic |
Data assimilation Bias Correction Physical chemical mathematical & earth Sciences Earth sciences & physical geography Physique chimie mathématiques & sciences de la terre Sciences de la terre & géographie physique |
spellingShingle |
Data assimilation Bias Correction Physical chemical mathematical & earth Sciences Earth sciences & physical geography Physique chimie mathématiques & sciences de la terre Sciences de la terre & géographie physique Canter, Martin Barth, Alexander Ocean Modeling: Bias correction through stochastic forcing. |
topic_facet |
Data assimilation Bias Correction Physical chemical mathematical & earth Sciences Earth sciences & physical geography Physique chimie mathématiques & sciences de la terre Sciences de la terre & géographie physique |
description |
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 ... |
author2 |
Geohydrodynamics and Environment Reasearch - GHER |
format |
Conference Object |
author |
Canter, Martin Barth, Alexander |
author_facet |
Canter, Martin Barth, Alexander |
author_sort |
Canter, Martin |
title |
Ocean Modeling: Bias correction through stochastic forcing. |
title_short |
Ocean Modeling: Bias correction through stochastic forcing. |
title_full |
Ocean Modeling: Bias correction through stochastic forcing. |
title_fullStr |
Ocean Modeling: Bias correction through stochastic forcing. |
title_full_unstemmed |
Ocean Modeling: Bias correction through stochastic forcing. |
title_sort |
ocean modeling: bias correction through stochastic forcing. |
publishDate |
2015 |
url |
https://orbi.uliege.be/handle/2268/190987 https://orbi.uliege.be/bitstream/2268/190987/1/EGU2015MartinCanter_3.pdf |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
European Geosciences Union General Assembly 2015, Vienna, Austria [AT], 12 – 17 April 2015 |
op_relation |
https://orbi.uliege.be/handle/2268/190987 info:hdl:2268/190987 https://orbi.uliege.be/bitstream/2268/190987/1/EGU2015MartinCanter_3.pdf |
op_rights |
open access http://purl.org/coar/access_right/c_abf2 info:eu-repo/semantics/openAccess |
_version_ |
1796953279898320896 |