Poster (Scientific congresses and symposiums)
Correcting Biases in a lower resolution global circulation model with data assimilation
Canter, Martin; Barth, Alexander
2016EGU
 

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Keywords :
Data assimilation
Abstract :
[en] 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.
Research center :
GHER/MARE/AGO
Disciplines :
Earth sciences & physical geography
Author, co-author :
Canter, Martin ;  Université de Liège > Département d'astrophys., géophysique et océanographie (AGO) > GeoHydrodynamics and Environment Research (GHER)
Barth, Alexander  ;  Université de Liège > Département d'astrophys., géophysique et océanographie (AGO) > GeoHydrodynamics and Environment Research (GHER)
Language :
English
Title :
Correcting Biases in a lower resolution global circulation model with data assimilation
Publication date :
2016
Event name :
EGU
Event date :
17–22 April 2016
Tags :
CÉCI : Consortium des Équipements de Calcul Intensif
Funders :
F.R.S.-FNRS - Fonds de la Recherche Scientifique [BE]
CÉCI - Consortium des Équipements de Calcul Intensif [BE]
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since 02 January 2017

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