Ocean Modelling, Data Assimilation and Forecasting

Develop operational oceanography aiming at meeting research and societal needs related to monitoring and management of the marine environment, marine resources, marine safety, as well as weather and seasonal climate forecasts.

Description & Objectives

Research Description

The research comprises development and validation of modelling of ocean circulation, sea ice dynamics and the marine ecosystem. Advanced data assimilation methods, based on the Ensemble Kalman Filter (EnKF) and originally introduced at Nansen Center, are applied to realistic forecasts of ocean and sea-ice state variables. The main research activities relates to the development, validation and interpretation of the ToPAZ ocean forecasting system (http://topaz.nersc.no) covering the North Atlantic and Arctic oceans.

The ToPAZ ocean modelling system are transferable to other oceans and has also been implemented for Barents, Norwegian and Greenland Seas, the Indian Ocean, the Agulhas Current and the Southern Ocean, South China Sea and Gulf of Mexico.

The Nansen Center is co-organizing the yearly international EnKF workshop together with Uni Centre for Integrated Petroleum Research (CIPR) and International Research Institute of Stavanger (IRIS).

Specific Research Objectives

  • To advance and validate the TOPAZ data assimilation and marine forecasting system for the North Atlantic, the Nordic Seas and the Arctic Oceans.
  • To study past seasonal to interannual ocean variability by reanalyses of the TOPAZ assimilative system.
  • To diversify the TOPAZ system for studies of coupled ocean, sea-ice and ecosystem processes, with joint assimilation of new types of satellite, ocean acoustics and in situ data.
  • To implement and validate nested versions of the TOPAZ system in various oceans such as the Barents, Norwegian and Greenland Seas, the Indian Ocean, the Agulhas Current and the Southern Ocean, South China Sea and Gulf of Mexico, with special emphasis on meso-scale processes.
  • To develop new data assimilation methods related to the Ensemble Kalman Filter, and apply the methods in new fields of applications.

Staff

Name Area of Expertise
Abhishek S. Shah mathematics
Alfatih Ali
mathematics
oceanography
Ali Aydoğdu
mathematics
oceanography
Bjørn Backeberg oceanography
Brian David Dushaw
acoustics
oceanography
Çağlar Yumruktepe oceanography
Colin Grudzien mathematics
Francois Counillon
mathematics
oceanography
Gaute Hope
acoustics
geo-sciences
geophysics
Geir Evensen
computer science
geo-sciences
mathematics
Hans Wackernagel
geo-sciences
statistics
Johnny A. Johannessen
oceanography
remote sensing
Madlen Kimmritz sea ice
Maxime Beauchamp
geo-sciences
mathematics
statistics
Mika Malila oceanography
Mostafa Bakhoday-Paskyabi oceanography
Patrick N. Raanes mathematics
Yanchun He oceanography
Yiguo Wang data assimilation
Yongqi Gao oceanography

Projects

Blue-Action

  • Climate Dynamics and Prediction

    Blue-Action is a 5-year European project of Horizon 2020 Blue Growth coordinated by the Danish Meteorological Institute with 41 partners. Prof. Y. Gao is a key partner and is leading work package 3.

INTAROS

  • Climate Dynamics and Prediction, Polar Acoustics and Oceanography, Scientific Data Management

    INTAROS is a research and innovation action under the H2020-BG-09 call in 2016 and will run from 2016 to 2021.

REDDA

iNcREASE

  • Climate Dynamics and Prediction, Ocean and Sea Ice Remote Sensing

    InCREASE seeks to improve projections of future sea level in the North Sea and along the Norwegian coast

ARC MFC

  • Data Assimilation, Ocean Modeling

    The Arctic Marine Forecasting Center will provide operational forecasts of ocean currents, temperature, salinity, primary production, sea ice and waves out to 10 days ahead as well as reanalyses over the past 25 years.

PARADIGM

  • Climate Dynamics and Prediction

    Establish a framework for generating, evaluating and improving regional predictions of climate on seasonal-to-decadal time scale, by combining regionally focused analyses of predictive potential, and dynamical downscaling of climate predictions

EVA

EPOCASA

EmblA