Thesis supervisor: Sándor Baran
Location of studies (in Hungarian): University of Debrecen Faculty of Informatics Abbreviation of location of studies: DE IK
Description of the research topic:
Syllabus
Statistical post-processing of ensemble forecasts. Verification scores.
Univariate models: Bayesian Model Averaging, non-homogeneous regression, quantile regression.
Multivariate approaches: Ensemble Copula Coupling, parametric copula models.
Spatial approaches.
Machine learning approaches in probabilistic weather forecasting.
Estimation techniques.
Dual-resolution ensemble forecasts.
Development, implementation and testing of new probabilistic models for various weather and hydrological quantities.
Bibliography
1. Wannitsem, S., Wilks, D. S., Messner, J. W. (2018) Statistical Postprocessing of Ensemble Forecasts. Elsevier.
2. Wilks, D. S. (2011) Statistical Methods in the Atmospheric Sciences (3rd ed.). Elsevier, Amsterdam.
3. Fraley, C., Raftery, A. E., Gneiting, T., Sloughter, J. M., Berrocal, V. J. (2011) Probabilistic weather forecasting in R. The R Journal 3, 55-63.
4. Lerch, S., Baran, S. (2017) Similarity-based semi-local estimation of EMOS models. J. R. Stat. Soc. Ser. C Appl. Stat. 66, 29–51.
5. Baran, S. (2014) Probabilistic wind speed forecasting using Bayesian model averaging with truncated normal components. Comput. Stat. Data. Anal. 75, 227-238.
Deadline for application: 2019-09-15
2024. IV. 17. ODT ülés Az ODT következő ülésére 2024. június 14-én, pénteken 10.00 órakor kerül sor a Semmelweis Egyetem Szenátusi termében (Bp. Üllői út 26. I. emelet).
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