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 |
| Ali Aydoğdu |
mathematics oceanography |
| Bjørn Backeberg | oceanography |
| Çağlar Yumruktepe | oceanography |
| Francois Counillon |
data assimilation oceanography sea ice |
| Geir Evensen |
computer science geo-sciences mathematics |
| Hans Wackernagel |
geo-sciences statistics |
| Jiping Xie | data assimilation |
| Johnny A. Johannessen |
oceanography remote sensing |
| Julien Brajard |
computer science data assimilation geo-sciences mathematics oceanography remote sensing statistics |
| Madlen Kimmritz |
data assimilation sea ice |
| Mostafa Bakhoday-Paskyabi | oceanography |
| Nadine Steiger | oceanography |
| Sukun Cheng | statistics |
| Tsuyoshi Wakamatsu |
data assimilation geophysics oceanography |
| 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
-
Data Assimilation
Big Data methods in geosciences
EmblA
-
Data Assimilation
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.
NMDC
-
Scientific Data Management
16 Norwegian institutions are establishing a research infrastructure NMDC that will provide seamless access to marine data, greatly increase the efficiency of marine science in Norway and facilitate the generation of high quality research.
