@article{RudolphMohrGottfriedLamshoeftetal.2015,
  author    = {Rudolph-Mohr, Nicole and Gottfried, Sebastian and Lamsh{\"o}ft, Marc and Z{\"u}hlke, Sebastian and Oswald, Sascha and Spiteller, Michael},
  title     = {Non-invasive imaging techniques to study O-2 micro-patterns around pesticide treated lupine roots},
  series = {Geoderma : an international journal of soil science},
  volume    = {239},
  journal   = {Geoderma : an international journal of soil science},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0016-7061},
  doi       = {10.1016/j.geoderma.2014.10.022},
  pages     = {257 -- 264},
  year      = {2015},
  abstract  = {The soil root interface is a highly heterogeneous system, e.g. in terms of O-2 and pH distribution. The destructive character of conventional methods disturbs the natural conditions of those biogeochemical gradients. Therefore, experiments aiming to control these influences and study pesticide kinetics under given O-2 and pH conditions suffer from a large uncertainty of the "real" O-2/pH at a certain position. Our approach with two different imaging techniques will examine the soil-root interface as well as the dissipation of the applied pesticide at a high spatial resolution. The obtained outcomes show directly that the pH has an influence on enantioselective dissipation of the acetanilide fungicide metalaxyl. In areas with high pH from an applied racemic mixture, the R-enantiomer dissipates faster than the S-enantiomer. Moreover, we found significantly reduced oxygen values in the bulk soil and vicinity of metalaxyl treated roots compared to control plant roots. The combination of matrix-assisted laser desorption/ionization mass spectrometry (MALDI) and fluorescence imaging indicated the oxygen-dependent behavior of metalaxyl at the root surface. The results presented here underline the great potential of combining different imaging methods to examine the soil-root interfaces as well as the dissipation of organic pollutants in small soil compartments. (C) 2014 Elsevier B.V. All rights reserved.},
  language  = {en}
}
@article{HastermannReinhardtKleinetal.2021,
  author    = {Hastermann, Gottfried and Reinhardt, Maria and Klein, Rupert and Reich, Sebastian},
  title     = {Balanced data assimilation for highly oscillatory mechanical systems},
  series = {Communications in applied mathematics and computational science : CAMCoS},
  volume    = {16},
  journal   = {Communications in applied mathematics and computational science : CAMCoS},
  number    = {1},
  publisher = {Mathematical Sciences Publishers},
  address   = {Berkeley},
  issn      = {1559-3940},
  doi       = {10.2140/camcos.2021.16.119},
  pages     = {119 -- 154},
  year      = {2021},
  abstract  = {Data assimilation algorithms are used to estimate the states of a dynamical system using partial and noisy observations. The ensemble Kalman filter has become a popular data assimilation scheme due to its simplicity and robustness for a wide range of application areas. Nevertheless, this filter also has limitations due to its inherent assumptions of Gaussianity and linearity, which can manifest themselves in the form of dynamically inconsistent state estimates. This issue is investigated here for balanced, slowly evolving solutions to highly oscillatory Hamiltonian systems which are prototypical for applications in numerical weather prediction. It is demonstrated that the standard ensemble Kalman filter can lead to state estimates that do not satisfy the pertinent balance relations and ultimately lead to filter divergence. Two remedies are proposed, one in terms of blended asymptotically consistent time-stepping schemes, and one in terms of minimization-based postprocessing methods. The effects of these modifications to the standard ensemble Kalman filter are discussed and demonstrated numerically for balanced motions of two prototypical Hamiltonian reference systems.},
  language  = {en}
}