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.
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:
- Examing the interaction of serial observation processing and localization that leads to a de-stabilization of the filter analysis step in filters with serial observation processing like EAKF and EnSRF (Nerger 2015)
- Contributing to sea-ice data assimilation in cooperation with the National Marine Environmental Forecasting Center in Beijing, China (Yang et al., 2014, 2015, 2016, Liang et al. 2017, 2019, Mu et al. 2018, Liu et al. 2019)
- Assessment of nonlinear filters or high-dimensional data assimilation into ocean models (Tödter et al 2016, Kirchgessner et al, 2017)
- Reviewing and assessing nonlinear and linear filter methods (Vetra-Carvalho et al., 2018, van Leeuwen et al., 2019)
- Strongly and weakly coupled data assimilation for ocean-biogeochemistry in the North and Baltic Seas (Goodliff et al., 2019)
- Assessment of global chlorophyll assimilation into a biogeochemical model with multiple phytoplankton functional groups (Pradhan et al., 2019, Pradhan et al., 2020)
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).
We participate in different research projects:
The project ESM - Advanced Earth System Modeling Capacity is a cooperation project of 8 research centers f the Helmholtz Association. In the project we develop data assimilation capability for coupled Earth system models. Further we perform 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 utilize the software framework PDAF. For the assessment of the influence of data assimilation we apply the data assimilation to the coupled atmosphere-ocean model AWI-CM.
More information can be found on the web site of the ESM project.
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.
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 cooperates with the groups Marine Biogeosciences and Phytooptics. The project aims at improving the simulation of plankton dynamics and carbon fluxes in the Southern Ocean by enhancing the ecosystem model REcoM. This will be achieved by applying data assimilation with PDAF for improve the parameters and parameterizations of REcoM and by extending the model to account for light availability in several spectral bands as well photoprotection and photophysiological effects of iron limitation.
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. The project was funded by the German Ministry for Transport and Digital Infrastructure.
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.