Dr. Annette Möller

Research Interests

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

Short CV

Scientific Network Ensemble Postprocessing

In the DFG-funded Scientific Network on "Statistical post-processing of ensemble forecasts for different weather variables", statistical models for the probabilistic prediction of different weather variables will be developed and implemented in collaboration with an international group of scientists.

More information about the project and recent publications can be found on the internal project page of the working group.

Publications

Published papers (peer-reviewed)

• Baran, S. andMöller, 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 Covariates. Journal 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 copulas. Quarterly Journal of the Royal Meteorological Society, 139 (673), 982-991.

Book chapters

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

Conference Proceedings with referee process

• Möller, A. and Gertheiss, J. (2018). A classification tree for functional data. In Wood, S. (ed.): Proceedings of the 33rd International Workshop on Statistical Modelling, 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 Modelling, 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 31th International Workshop on Statistical Modelling, 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 Modelling 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 Modelling, 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. URL github.com/JuGross/ensAR

Teaching

Self dependend teaching, TU Clausthal

• Statistical Methods in Machine Learning (Summer term 2017, Summer term 2018, Winter term 2018/2019)
• Pre-course in mathematics (Winter term 2017/2018, Summer term 2018)
• Introduction to Probability Theory and Statistics (Winter term 2018/2019)
• Engineering Statistics I (Winter term 2018/2019)

Teaching joint with Jan Gertheiss, TU Clausthal

• Statistics I for engineering sciences (Winter term 2016/2017)