Efficient ensemble data assimilation for coupled models with the Parallel Data Assimilation Framework: example of AWI-CM (AWI-CM-PDAF 1.0)

Data assimilation integrates information from observational measurements with numerical models. When used with coupled models of Earth system compartments, e.g., the atmosphere and the ocean, consistent joint states can be estimated. A common approach for data assimilation is ensemble-based methods which utilize an ensemble of state realizations to estimate the state and its uncertainty. These methods are far more costly to compute than a single coupled model because of the required integration of the ensemble. However, with uncoupled models, the ensemble methods also have been shown to exhibit a particularly good scaling behavior. This study discusses an approach to augment a coupled model with data assimilation functionality provided by the Parallel Data Assimilation Framework (PDAF). Using only minimal changes in the codes of the different compartment models, a particularly efficient data assimilation system is generated that utilizes parallelization and in-memory data transfers between the models and the data assimilation functions and hence avoids most of the file reading and writing, as well as model restarts during the data assimilation process. This study explains the required modifications to the programs with the example of the coupled atmosphere–sea-ice–ocean model AWI-CM (AWI Climate Model). Using the case of the assimilation of oceanic observations shows that the data assimilation leads only to small overheads in computing time of about 15 % compared to the model without data assimilation and a very good parallel scalability. The model-agnostic structure of the assimilation software ensures a separation of concerns in which the development of data assimilation methods can be separated from the model application

Scalable Coupled Ensemble Data Assimilation with AWI-CM and PDAF

We discuss a strategy to build a highly scalable and flexible data assimilation system on the basis of the Parallel Data Assimilation Framework (PDAF, http://pdaf.awi.de) using the example of the coupled climate model AWI-CM (Sidorenko et al., Climate Dynamics, 44 (2015) 757-780). AWI-CM consists of the finite-element sea ice-ocean model FESOM, which uses an unstructured model grid, and the model ECHAM6 for the atmosphere. The model compartments are coupled using OASIS3-MCT. The model system consists of two separate executable programs for the ocean and atmosphere. The assimilation system is generated by online-coupling of AWI-CM and PDAF. This modifies AWI-CM to perform ensemble forecasting and data assimilation and allows to fully keep the ensemble information in memory avoiding costly file operations and model restarts. The resulting assimilation system supports to apply the assimilation both in-compartment (i.e. weakly-coupled) as well as cross-compartment (i.e. strongly-coupled). Discussed are the structure and computational performance of the assimilation system as well as results from the assimilation of sea surface temperature and ocean profile data sets into a realistic configuration of AWI-CM

Ensemble Data Assimilation for Coupled Models of the Earth System

Coupled models simulate different compartments of the Earth system as well as their interactions. For example coupled atmosphere-ocean models like the AWI Climate Model (AWI-CM), simulate the physics in both compartments and fluxes in between then. Data assimilation is used with coupled models to generate model fields to initialize model predictions, for computing a model state over time as a reanalysis, to optimize model parameters, and to assess model deficiencies. Ensemble data assimilation methods can be applied with these model systems, but have a high high computing cost. To allow us to efficiently perform the data assimilation, the parallel data assimilation framework (PDAF) has been developed. I will discuss the application and challenges of coupled ensemble data assimilation on the example of the data assimilative model system AWI-CM coupled to PDAF

Coupled Ensemble Data Assimilation with the Climate Model AWI-CM

The coupled atmosphere-ocean model AWI-CM has been augmented for ensemble data assimilation using the parallel data assimilation framework (PDAF). AWI-CM consists of the atmosphere model ECHAM6 and the unstructured grid finite element ocean model FESOM. PDAF provides the environment for ensemble forecasts and the ensemble filters for the assimilation. The work aims at strongly-coupled data assimilation, hence using cross-covariances between the atmosphere and ocean in the analysis step of the data assimilation process. As a first step oceanic observations are assimilated into the coupled model system in a setup of weakly coupled data assimilation and the effect one the coupled model state is assessed. We discuss the setup of the system, which is generic and hence also applicable for other coupled, but also uncoupled models. Further, challenges of the assimilation into the coupled system and initial results from strongly-coupled assimilation are discussed

Building an Efficient Ensemble Data Assimilation System for Coupled Models with the Parallel Data Assimilation Framework

We discuss how to build an ensemble data assimilation system using a direct connection between a coupled model system and the ensemble data assimilation software PDAF (Parallel Data Assimilation Framework, http://pdaf.awi.de). The direct connection results in a data assimilation program with high flexibility, efficiency, and parallel scalability. For this we augment the source code of the coupled model by data assimilation routines and hence create an online-coupled assimilative model. This first modifies the coupled model to be able to simulate an ensemble. Using a combination of in-memory access and parallel communication with the Message Passing Interface (MPI) standard we can further add the analysis step of ensemble-based filter methods, which compute the assimilation of observations, without the need to stop and restart the whole coupled model system. Instead, the analysis step is performed in between time steps and is independent of the actual model coupler that couples the different model compartments. This strategy to build the assimilation system allows us to perform both weakly coupled (in-compartment) and strongly coupled (cross-compartment) assimilation. The assimilation frequency can be kept flexible, so that the assimilation of observations from different compartments can be performed at different intervals. Further, the reading and writing of disk files is minimized. The resulting assimilative model can be run in the same way as the regular coupled model, but with additional parameters controlling the assimilation and with a higher number of processors to simulate the ensemble. Using the example of the coupled climate model AWI-CM that contains the FESOM model for the ocean and sea ice and ECHAM6 for the atmosphere, both coupled through the OASIS-MCT coupler, we discuss the features of the online assimilation coupling strategy and the performance of the resulting assimilative model

Ensemble Data Assimilation for Coupled Models of the Earth System

Data assimilation combines observational information with numerical models taking into account the errors in both the observations and the model. In ensemble data assimilation the errors in the model state are dynamically estimated using an ensemble of model states. Data assimilation is used with coupled models to generate model fields to initialize model predictions, for computing a model state over time as a reanalysis, to optimize model parameters, and to assess model deficiencies. The coupled models simulate different compartments of the Earth system as well as their interactions. For example coupled atmosphere-ocean models like the AWI Climate Model (AWI-CM), simulate the physics in both compartments and fluxes in between then. Data assimilation is used with coupled models to generate model fields to initialize model predictions, for computing a model state over time as a reanalysis, to optimize model parameters, and to assess model deficiencies. Ensemble data assimilation methods can be applied with these model systems, but have a high high computing cost. To allow us to efficiently perform the data assimilation, the parallel data assimilation framework (PDAF) has been developed. I will discuss the application and challenges of coupled ensemble data assimilation on the examples of the data assimilative model system of AWI-CM coupled to PDAF and a coupled ocean-biogeochemical model consistent of the ocean circulation model MITgcm and the ecosystem model REcoM2

Efficient Ensemble Data Assimilation For Earth System Models with the Parallel Data Assimilation Framework (PDAF)

We discuss how to build an ensemble data assimilation system using a direct connection between a coupled Earth system model (ESM) and the ensemble data assimilation software PDAF (Parallel Data Assimilation Framework, http://pdaf.awi.de). The direct connection results in a data assimilation program with high flexibility, efficiency, and parallel scalability. For this we augment the source code of the coupled model by data assimilation routines and hence create an online-coupled assimilative model. This first modifies the coupled model to be able to simulate an ensemble. Using a combination of in-memory access and parallel communication with the Message Passing Interface (MPI) standard we can further add the analysis step of ensemble-based assimilation methods. Thus the assimilation of observations is computed without the need to stop and restart the whole coupled model system. Instead, the analysis step is performed in between time steps and is independent of the actual model coupler that couples the different model compartments. This strategy to build the assimilation system allows us to perform both weakly coupled (in-compartment) and strongly coupled (cross-compartment) assimilation. The assimilation frequency can be kept flexible, so that the assimilation of observations from different compartments of the ESM can be performed at different intervals. Further, the reading and writing of disk files is minimized. The resulting assimilative model can be run in the same way as the regular ESM, but with additional parameters controlling the assimilation and with a higher number of processors to simulate the ensemble. Using the example of the coupled climate model AWI-CM that contains the FESOM model for the ocean and sea ice and ECHAM6 for the atmosphere, both coupled through the OASIS-MCT coupler, we discuss the features of the online assimilation coupling strategy and the performance of the resulting assimilative model

Metal Ion-Induced Lateral Aggregation of Filamentous Viruses fd and M13

We report a detailed comparison between calculations of inter-filament interactions based on Monte-Carlo simulations and experimental features of lateral aggregation of bacteriophages fd and M13 induced by a number of divalent metal ions. The general findings are consistent with the polyelectrolyte nature of the virus filaments and confirm that the solution electrostatics account for most of the experimental features observed. One particularly interesting discovery is resolubilization for bundles of either fd or M13 viruses when the concentration of the bundle-inducing metal ion Mg2+ or Ca2+ is increased to large (\u3e100 mM) values. In the range of Mg2+ or Ca2+ concentrations where large bundles of the virus filaments are formed, the optimal attractive interaction energy between the virus filaments is estimated to be on the order of 0.01 kT per net charge on the virus surface when a recent analytical prediction to the experimentally defined conditions of resolubilization is applied. We also observed qualitatively distinct behavior between the alkali-earth metal ions and the divalent transition metal ions in their action on the charged viruses. The understanding of metal ions-induced reversible aggregation based on solution electrostatics may lead to potential applications in molecular biology and medicine

Multiscale resolution continuum theory for elastic plastic material with damage, an implicit 3D implementation

The multiscale resolution continuum theory (MRCT) [1] is a higher order continuum theory in which additional kinematic variables are added to account for the size effect at several distinct length scales. This remedies the deficiency of the conventional continuum approach when predicting both strain softening and strain hardening materials and resolves the microstructure details without extremely fine mesh in the localization zone, however additional nodal degrees of freedom are needed and the requirement of element size at the length scale somewhat adds to the computational burden. This paper is an extension of the simplified 1D multiscale implementation presented in Complas XI 2011 [14]. A 3D elastic-plastic multiscale element, with one additional subscale in which the damage is applied, is implemented implicitly in the general purpose finite element analysis program FEAP

Simplified multiscale resolution theory for elastic material with damage

The multiscale resolution continuum theory (MRCT) is a higher order continuum mechanics. A particle is represented by a point that is deformable. This enables the possibility to include the effect of microstructure features in the continuum model on the deformation behavior through additional nodal variables for the higher order scale. This reduces the need for a very fine mesh in order to resolve microstructure details. It is possible to further reduce the computational effort by keeping the additional degree of freedoms to a minimum by tailoring the theory to specific phenomena. The latter is illustrated in a simplified context for an elastic material with damage