Data Assimilation

Data assimilation combines observational information with numerical models to improve the model state and its prediction. The most common application of data assimilation is weather forecasting. However, also the state and prediction of ocean models can be improved by data assimilation, for example by utilizing satellite observations of sea surface temperature or sea surface height. Similarly, ocean-biogeochemical models can profit from the incorporation of satellite ocean chlorophyll data by correcting the values of biogeochmical fields or by estimating the parameters that control the processes in the model.


Dr. Lars Nerger
Nabir Mamnun

Former members

Dr. Qi Tang
Dr. Michael Goodliff
Dr. Himansu K. Pradhan
Paul Kirchgessner
Dr. Svetlana Losa

Sequential Data Assimilation

The research work in the Scientific Computing group at AWI focuses on sequential data assimilation methods. Parallel ensemble-based Kalman filter algorithms are well suited for parallel high-performance computers as they are highly scalable. Recent research includes:


Parallel Data Assimilation Framework - PDAF

Related to the research projects in which we studied and developed filter algorithms, the data assimilation framework PDAF (Parallel Data Assimilation Framework) was developed. Initially, the framework allowed to easily compare different filter algorithms under identical conditions. Today, its main task is to simplify the implementation of parallelized data assimilation systems based on existing numerical models. PDAF provides complete implementations of sequential data assimilation algorithms, which are optimized for application on parallel computers (see Nerger and Hiller, 2013).

More information on PDAF can be found on the AWI web page on PDAF and on the project web pages of PDAF where also the source code package can be downloaded.



We participate in different research projects:


The project SEAMLESS - Services based on Ecosystem data AssiMiLation: Essential Science and Solutions - is funded by the EU Horizon-2020 program. SEAMLESS aims at improving the current European capability to simulate and predict the state of marine ecosystems. The project focuses on state indicators that are currently are monitored and/or simulated routinely by observatories and models of the European Copernicus Marine Services (“CMEMS”). SEAMLESS will improve the current CMEMS data assimilation methods that integrate the information from monitored and simulated indicators. We build a 1-dimensional prototype that uses PDAF for the data assimilation. Further, at AWI we will apply the data assimilation with PDAF with a focus on the Baltic Sea.

Further information is available on the web site of SEAMLESS.


The project UQ - Uncertainty Quantification: From Models to Reliable Information is a cross-disciplinary project of the Helmholtz Association with the focus un quantifications of uncertainty, which is ubiquituous across the research fields of the Helmholtz Association. At AWI we collaborate with the Section Marine Biogeosciences and focus at the quantification of uncertainty in ecosystem modeling. In particular we will asess the uncertainty of the parameterizations of the ecosystem model REcoM and will apply data assimilation methods provided by PDAF to reduce the uncertainty.

More information on the project is available on the project website of UQ.


Completed Projects

ESM (2017-2020)

The project ESM - Advanced Earth System Modeling Capacity was a cooperation project of 8 research centers of the Helmholtz Association. In the project we developed data assimilation capability for coupled Earth system models. Further we performed research in the optimal application of data assimilation for coupled model e.g. accounting for the different temporal and spatial scales of model compartments. For the data assimilation component, we applied the software framework PDAF to the coupled atmosphere-ocean model AWI-CM. The implementation approach was published in Nerger et al. (2020), while Tang et al. (2020) described the effects of weakly coupled data assimilation onto both the ocean and atmopshere, while Mu et al. (2020) focuses on effect on the sea ice for building an sealess sea ice prediction system.

More information can be found on the web site of the ESM project.

IPSO (2016-2019)

In the project IPSO (Improving the prediction of photophysiology in the Southern Ocean by accounting for iron limitation, optical properties and spectral satellite data information) the data assimilation group cooperated with the groups Marine Biogeosciences and Phytooptics at AWI. The project aimed at improving the simulation of plankton dynamics and carbon fluxes in the Southern Ocean by enhancing the ecosystem model REcoM. This was achieved by applying data assimilation with PDAF for improving the state representation of REcoM (Pradhan et al., 2019, 2020) and by extending the model to account for light availability in several spectral bands as well photoprotection and photophysiological effects of iron limitation. Further model parameterizations for the photophysiology were improved.

MeRamo (2016-2018)

In the project MeRamo (Supporting the authorities that implement the EU Marine Strategy Framework Directive using an assimilative ecosystem model) we developed a data assimilation components for the coupled ocean-biogeochemical forecast model of the German Maritime and Hydrographic Agency (BSH) in the North and Baltic Seas. The data assimilation system uses PDAF and the operational model HBM coupled to the ecosystem model ERGOM and focused on the assimialtion of sea surface temperature data. The effect of strongly-coupled assimilation is published in Goodliff et al. (2019). The project was funded by the German Ministry for Transport and Digital Infrastructure.

DeMarine (2012-2015)

In the project DeMarine-2 we continued to develop a data assimilation data system for the North and Baltic Seas for the German Maritime and Hydrographic Agency (BSH). The data assimilation system uses PDAF and the operational model HBM of the BSH. Initial work has been done in the previous project DeMarine Environment (Losa et al. 2012, Losa et al. 2013).

More information on DeMarine is available on the web pages of DeMarine.

Sangoma (EU FP7, years 2011-2015)

We participated in the EU-funded project SANGOMA (Stochastic Assimilation for the Next Generation Ocean Model Applications). In project unified tools for data assimilation, new assimilation algorithms and data assimilation benchmark applications were developed to support future operational systems with state-of-the-art data assimilation and related analysis tools.

More information is available on the web site of Sangoma.