Data Assimilation

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Improving weather and climate predictions by training of supermodels

Submitted by Alberto Carrassi on 22. October 2019 - 15:48

Schevenhoven, F., Selten, F., Carrassi, A. & Keenlyside, N. Improving weather and climate predictions by training of supermodels. Earth System Dynamics (2019).

Data assimilation in the geosciences - An overview of methods, issues and perspectives

Submitted by Alberto Carrassi on 19. June 2018 - 13:15

Carrassi, A., Bocquet, M., Bertino, L. & Evensen, G. Data assimilation in the geosciences - An overview of methods, issues and perspectives. WIREs Climate Change (2018).doi:doi: 10.1002/wcc.535

Stochastic parametrization identification using ensemble Kalman filtering combined with expectation-minimization and Newton-Raphson maximum likelihood methods

Submitted by Alberto Carrassi on 14. February 2018 - 17:41

Stochastic parametrization identification using ensemble Kalman filtering combined with expectation-minimization and Newton-Raphson maximum likelihood methods.

A new method for constraining the ocean in coupled system model for long reanalysis and climate predictions

Submitted by Lasse Pettersson on 8. December 2016 - 11:04

Nansen and Bjerknes scientists have for the first time demonstrated the capability - thanks to an advanced data assimilation method - to constrain ocean variability in key regions (e.g. North Atlantic, Equatorial and North Pacific) of a fully coupled Earth system model just using sea surface temperatures. This result opens new possibilities for a long fully coupled reanalysis dating back to 1850 and test thoroughly the skills of decadal predictions by the Norwegian Climate Prediction model. The research team lead by Dr.

Geophysical data analysis: a dynamically based assimilation method

Submitted by Alberto Carrassi on 27. November 2016 - 20:13

Carrassi, A. Geophysical data analysis: a dynamically based assimilation method. (2005).

Scientific article on data assimilation highlighted by EGU

Submitted by Lasse Pettersson on 11. December 2015 - 13:55

Nansen Center PhD student Patrick Raanes and co-authors have published the article Expanding the validity of the ensemble Kalman filter without the intrinsic need for inflation in the journal Nonlinear Processes

ES-PhD: Ensemble Smoother - Patrick Raanes

Research on the ‘Ensemble Smoother’ (ES) method for parameter estimation (history matching) and model conditioning.

Project tasks include:

Evaluation of ES for parameter estimation in a number of known test problems of varying complexity to evaluate ES performance with respect to other traditional methods
Evaluation, implementation and testing of recently proposed iterative smoother algorithms
Development, implementation and testing of new iterative smoother algorithms
Application and demonstration of smoother algorithms in agree field examples.

Project Details

Funding Agency:

Statoil E&P

Coordinating Institute:

Nansen Environmental and Remote Sensing Center

Project Status:

Completed

EmblA: Ensemble-based data assimilation for environmental monitoring and prediction

Project Logo:

EmblA is the Nordic Centre of Excellence for ensemble-based data assimilation (DA). It supports Nordic users of DA methods with instruction and training, state-of-the-art open-source codes, and works for R&D in several applications.

Our environment is undergoing major anthropogenic and natural changes that we need to understand, attribute and predict. To be able to use complex 3-dimensional models of our environment to generate reliable predic- tions with documented accuracy is therefore a major challenge for e-Sciences.

Project Details

Funding Agency:

NordForsk

Project Deputy Leader at NERSC:

Laurent Bertino

Coordinating Institute:

Nansen Environmental and Remote Sensing Center

Project Status:

Completed

New Nordic Center of Excellence on Ensemble-based Methods for Environmental Monitoring and Prediction

Submitted by Laurent Bertino on 13. November 2013 - 10:23

A Nordic Center of Excellence on ensemble-based methods for environmental monitoring and prediction has been awarded by NordForsk to a consortium lead by NERSC.

Advanced data assimilation in oceanography

Submitted by Laurent Bertino on 16. July 2012 - 14:48

Bertino, L., Samuelsen, A., Counillon, F.S., Simon, E. & Sakov, P. Advanced data assimilation in oceanography. META 5, 8-12 (2012).

Nansen Environmental and Remote Sensing Center	Nansen-Bangladesh International Centre for Coastal, Ocean and Climate Studies	Nansen International Environmental and Remote Sensing Center
Nansen Environmental Research Center – India	Nansen–Zhu International Research Centre	Nansen-Tutu Centre for Marine Environmental Research