Thesis supervisor: Sándor Baran
Location of studies (in Hungarian): Debreceni Egyetem Informatikai Kar Abbreviation of location of studies: DE IK
Description of the research topic:
Statistical post-processing of ensmble forecasts. Verification scores. Bayesian Model Averaging and non-homogeneous regression models, Ensemble Copula Coupling. Development, implementation and testing of new probabilistic models.
Bibliography
1. Gneiting, T. (2014) Calibration of medium-range weather forecasts. ECMWF Technical Memorandum No. 719.
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. Baran, S. (2014) Probabilistic wind speed forecasting using Bayesian model averaging with truncated normal components. Comput. Stat. Data. Anal. 75, 227-238.
5. Baran, S., Lerch, S. (2015) Log-normal distribution based EMOS models for probabilistic wind speed forecasting. Q. J. R. Meteorol. Soc., doi: 10.1002/qj.2521.
Recommended language skills (in Hungarian): angol Number of students who can be accepted: 1
Deadline for application: 2018-02-16
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).
Login
