Welcome to the collaborative Research Center TRR 181 ”Energy transfers in Atmosphere and Ocean“
The seamless integration of large data sets into sophisticated computational models provides one of the central research challenges for the mathematical sciences in the 21st century. When the computational model is based on evolutionary equations and the data set is time-ordered, the process of combining models and data is called data assimilation. The assimilation of data into computational models serves a wide spectrum of purposes ranging from model calibration and model comparison all the way to the validation of novel model design principles.
The field of data assimilation has been largely driven by practitioners from meteorology, hydrology and oil reservoir exploration; but a theoretical foundation of the field is largely missing. Furthermore, many new applications are emerging from, for example, biology, medicine, and the neurosciences, which require novel data assimilation techniques. The goal of the proposed CRC is therefore twofold: First, to develop principled methodologies for data assimilation and, second, to demonstrate computational effectiveness and robustness through their implementation for established and novel data assimilation application areas.
While most current data assimilation algorithms are derived and analyzed from a Bayesian perspective, the CRC will view data assimilation from a general statistical inference perspective. Major challenges arise from the high-dimensionality of the inference problems, nonlinearity of the models and/or non-Gaussian statistics. Targeted application areas include the geoscience as well as emerging fields for data assimilation such as biophysics and cognitive neuroscience.
Prof. Dr. Sebastian Reich, University of Potsdam, Department of Mathematics
Dr. Liv Heinecke, University of Potsdam, Department of Mathematics
Interested in what we do in the SFB 1294 and what concepts are behind the research in the different projects? Our RingVL will start this week, with...
From the 19th until 21st of September the PhD students and PostDocs of the SFB1294 assembled at Kiez Bollmannsruh in Brandenburg for their first...
We are pleased to announce that the 1st Kalman Lecture will take place on the 24th of August, 2018.
The Kalman Lecture will be established as an...
Die Webseite www.kabinettwatch.de dient der Vorhersage, welche Minister und Ministerinnen in das Bundeskabinett kommen werden. Jeden Morgen...more ›
Particle filters hold the promise of fully nonlinear data assimilation. They used to have a bad reputation in the geosciences because of the...more ›
Aseev, N. A., Shprits, Y. Y., Drozdov A. Y., Kellerman, A. C., Usanova, M. E., Wang, D., Zhelavskaya, I. S. (2017) Signatures of Ultrarelativistic Electron Loss in the Heart of the Outer Radiation Belt Measured by Van Allen Probes, Journal of Geop. Res., 10, 122. doi: 10.1002/2017JA024485
Ni, BB, X. Cao, YY. Shprits, D. Summers, XD. Gu, S. Fu, YQ. Lou, (2018) Hot Plasma Effects on the Cyclotron-Resonant Pitch-Angle Scattering Rates of Radiation Belt Electrons Due to EMIC Waves, Geophys. Res. Lett., 1, 45. doi: 10.1002/2017GL07602
Makowski, Silvia, Jäger, Lena, Abdelwahab, Ahmed, Landwehr, Niels, Scheffer, Tobias (2018), A discriminative model for identifying readers and assessing text comprehension from eye movements. Proceedings of the European Conference on Machine Learning, 2018