Data Assimilation

Data assimilation combines observational information with numerical models to improve the model state and parameters that control model processes. It can also be used to assess model deficiencies, which is important knowledge to improve the model predictions. The most common application of data assimilation is to initialize forecasts, e.g. for 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 and other observations by correcting the values of biogeochmical fields or by estimating the parameters that control the biogeochemical processes represented by the model.

Sequential Data Assimilation

The research work in the Scientific Computing group at AWI focuses on ensemble-based data assimilation methods and their application. Parallel ensemble filter algorithms, like ensemble Kalman or particle filters, are highly scalable and hence well suited for data assimilation with complex models using parallel high-performance computers. Recent research includes:

Development of strategies to implement strongly coupled data assimilation for Earth system models (Nerger et al. 2020) and the application of data assimilation into the coupled atmosphere-ocean model AWI-CM (Tang et al. 2020, 2021, Mu et al. 2020)
Strongly and weakly coupled data assimilation for ocean-biogeochemistry in the North and Baltic Seas (Nerger et al., 2023, Goodliff et al., 2019)
Assimilation and uncertainty quantification in biogeochemical modeling with multiple phytoplankton functional groups (Mamnun et al, 2022, 2023, Pradhan et al., 2019, 2020)
Contributing to sea-ice data assimilation in cooperation with the National Marine Environmental Forecasting Center in Beijing, China and the Sun Yat-sen University in Zhuhai, China (Min et al, 2023, Luo et al., 2023, 2020, Liu et al. 2019, Mu et al. 2018, Liang et al. 2017, 2019, Yang et al., 2014, 2015, 2016)
Nonlinear and linear filter methods: method developments like the LKNETF (Nerger 2022); review and assessment (van Leeuwen et al., 2019, Vetra-Carvalho et al., 2018).
Assessment of nonlinear filters for high-dimensional data assimilation into ocean models (Kirchgessner et al, 2017, Tödter et al 2016)

Group leader
Dr. Lars Nerger

The Team
Anna Broschke
Frauke Bunsen
Dr. Anju Sathyanarayanan

Former members

Ahmadreza Masoum (guest)
Sophie Vliegen
Dr. Changliang Shao (guest)
Nabir Mamnun
Dr. Yuchen Sun
Chao Min (guest)
Dr. Farshid Daryabor
Imke Sievers (guest)
Xiaoyu Liu (guest)
Dr. Qi Tang
Dr. Michael Goodliff
Dr. Himansu K. Pradhan
Paul Kirchgessner
Dr. Svetlana Losa

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Parallel Data Assimilation Framework - PDAF

Related to our research projects we developed the data assimilation framework PDAF (Parallel Data Assimilation Framework). PDAF is one of the most widely used software frameworks for data assimilation.

PDAF simplifies the implementation of data assimilation systems based on existing numerical models so that one can faster get to the point to actually apply data assimilation. Further, PDAF allows to easily assess different data assimilation algorithms under identical conditions, which supports the development of new data assimilation methods. PDAF provides complete implementations of data assimilation algorithms, in particular ensemble Kalman filters, particle filters and 3-dimensional variational methods, which are optimized for application on parallel computers (see Nerger and Hiller, 2013 and Nerger et al., 2020).

PDAF is available as free open-source software and is continuously further advanced with new assimilation methods, and tools for data assimilation. Further PDAF was coupled to various different models and many of these coupling codes are available as open source code. More information on PDAF can be found on the AWI web page on PDAF and on the project web pages of PDAF where PDAF can also be downloaded.

Projects

We participate in different research projects:

UQ

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 website of the UQ project.

SOCRA

The project SOCRA - A Surface Ocean CO2 ReAnalysis aims at producing a novel global ocean CO2 reanalysis product by combining the benefits of (i) ocean circulation and biogeochemical model and (ii) observational data by applying data assimilation. We apply the coupled model FESOM-REcoM for the modeling and PDAF for the data assimilation. We conduct this project in a jointly with the junior research group MarESys, and with the AWI Sections Marine Bioeosciences and Physical Oceanography.

Completed Projects

SEAMLESS (2021-2023)

The project SEAMLESS - Services based on Ecosystem data AssiMiLation: Essential Science and Solutions - was funded by the EU Horizon-2020 program. SEAMLESS aimed at improving the current European capability to simulate and predict the state of marine ecosystems. The project focused on state indicators that are currently are monitored and/or simulated routinely by observatories and models of the European Copernicus Marine Services (“CMEMS”). SEAMLESS improved the CMEMS data assimilation methods that integrate the information from monitored and simulated indicators. We have built a 1-dimensional prototype that uses PDAF for the data assimilation. Further, at AWI we will applied the data assimilation with PDAF using the operational model system of the CMEMS monitoring and forecasting center for the Baltic Sea and assessed the effects of coupled physics/biogeochemical data assimilation.

Further information is available on the web site of SEAMLESS.

InfoWas (2021-2023)

The project InfoWas - Development of a model-based information system for the water quality in the North- and Baltic Seas was a collaborative project with the German Federal Maritime and Hydrographic Agency (BSH). The project focused on concentrations of algae and oxygen in the North Sea and Baltic Sea and developed an oxygen deficit index. We contributed to the project with the development of data assimilation functionality, based on PDAF, for ocean-biogeochemical modeling in order to improve predictions of water quality.

ESM (2017-2021)

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.