Research Interests

• Probabilistic weather forecasting
• Spatial statistics
• Copulas
• High-Dimensional Data
• Functional data analysis
• Statistical learning methods
• Time series analysis

Short resume

since 08/2016	Research Assistant, Institute for Applied Stochastics and Operations Research, Clausthal University of Technology
10/2013 - 08/2016	Research Assistant, Biometry & Bioinformatics, University of Göttingen
07/2010 - 09/2013	PhD student in RTG 1653 Spatio/Temporal Graphical Models, University of Heidelberg
2010	Diploma in Statistics, TU Dortmund

 

Scientific Network Ensemble Postprocessing

In the DFG-funded scientific network on the topic "Statistical post-processing of ensemble forecasts for different weather variables", statistical models for the probabilistic prediction of different weather variables are to be developed and implemented in cooperation with an international group of scientists.

More information on the project and current publications can be found on the internalproject pageof the working group.

Publications

Refereed articles

• Baran, S. and Möller, A. (2017):
  Bivariate ensemble model output statistics approach for joint forecasting of wind speed and temperature.
  Meteorology and Atmospheric Physics 129 (1), 99-112. DOI: 10.1007/s00703-016-0467-8
• Möller, A., Tutz, G. and Gertheiss, J. (2016):
  Random Forests for functional CovariatesJournal of Chemometrics 30 (12), 715-725. DOI: 10.1002/cem.2849

• Möller, A., Groß, J. (2016):
  Probabilistic temperature forecasting based on an ensemble AR modification.Quarterly Journal of the Royal Meteorological Society, 142 (696), 1385-1394. DOI:10.1002/qj.2741

• Hasenbeck, F., Reiser, D., Ghendrih, P., Marandet, Y., Tamain, P., Möller, A. and Reiter, D. (2015):
  Multiscale modeling approach for radial particle transport in large-scale simulations of the tokamak plasma edge.
  Procedia Computer Science, 51, 1128-1137.

• Baran, S. and Möller, A. (2015):Joint probabilistic forecasting of wind speed and temperature using Bayesian model averaging.
  Environmetrics, 26 (2), 120-132.

• Möller, A., Lenkoski, A. and Thorarinsdottir, T.L. (2013):
  Multivariate probabilistic forecasting using Bayesian model averaging and copulasQuarterly Journal of the Royal Meteorological Society, 139 (673), 982-991.

Book contributions

• Schefzik, R. and Möller, A. (2018): Multivariate ensemble postrpocessing, in: Vannitsem, S., Wilks, D. and Messner, J. (eds.)Statistical Postprocessing of Ensemble Forecasts, Elsevier.

Refereed Conference Proceedings

• Möller, A. and Gertheiss, J. (2018). A classification tree for functional data. In Wood, S. (ed.): Proceedings of the 33rd International Workshop on Statistical Modeling, 219-224.
• Möller, A. and Groß, J. (2017): A heteroscedastic probabilistic temperature forecasting model incorporating spread-error correlation and high-resolution forecasts, in Grzegorczyk, M. and Ceoldo, G. (eds.): Proceedings of the 32nd International Workshop on Statistical Modeling, 131-136.
• Möller, A. and Groß, J. (2016): Probabilistic Temperature forecasting based on an AR model fitted to forecast errors, in Dupuy, J.-F. and Josse, J. (eds.): Proceedings of the 31st International Workshop on Statistical Modeling, 225-230.
• Möller, A. (2015): Spatially adaptive probabilistic temperature forecasting using Markovian EMOS, in Friedl, H. and Wagner, H. (eds.): Proceedings of the 30th International Workshop on Statistical Modeling Volume II, 175-178.
• Möller, A., Tutz, G. and Gertheiss, J. (2014): Random Forests for Functional Covariates, in T. Kneib, F. Sobotka, J. Fahrenholz, and H. Irmer (eds.): Proceedings of the 29th International Workshop on Statistical Modeling, 219-223.

Preprints

• Möller, A., Spazzini, L., Kraus, D., Nagler, T. and Czado, C. (2018): Vine copula based postprocessing of ensemble forecasts for temperature.
• Möller, A. and Gertheiss, J. (2018): A classification tree for functional data.
• Möller, A., Groß, J. (2018): Probabilistic temperature forecasting with a heteroscedastic ensemble postprocessing model.
• Möller, A., Gertheiss, J. and Hessel, E.F. (2016): Clustering pigs according to their RFID registrations: A functional data approach.
• Möller, A., Thorarinsdottir, T.L., Lenkoski, A., and Gneiting T. (2016): Spatially adaptive, Bayesian estimation for probabilistic temperature forecasts. arXiv:1507.05066.

Software

• Groß, J. and Möller, A. (2018): ensAR: Autoregressive postprocessing methods for ensemble forecasts. R package version 0.0.0.9000, 2016. URLgithub.com/JuGross/ensAR

Teaching

Autonomous teaching TU Clausthal

• Statistical methods of machine learning (SS 2017, SS 2018, WS 2018/2019)
• Mathematical preliminary course (WS 2017/2018, SS 2018)
• Introduction to probability theory and statistics (WS 2018/2019)
• Engineering Statistics I (WS 2018/2019)

Accompanying teaching TU Clausthal

• Engineering Statistics I (WS 2016/2017)