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Local Marine Renewable Energy Resource Assessment via bias correction of climate re-analysis datasets

Chao Guo

Local Marine Renewable Energy Resource Assessment via bias correction of climate re-analysis datasets.

Rel. Giuseppe Giorgi. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Meccanica (Mechanical Engineering), 2022

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This study focus on the assessment of climate simulation variables by exploiting different bias correction methods and analysis the comparison of different bias corrected simulation. Because measured models need to generate high resolution input data, global and regional climate models are often distorted and have poor resolution. The purpose of this study is to find an accurate bias correction approach for climate variables prediction which are used to estimate the potential of renewable energy in a local area. Three different bias correction methods are analyzed, one of them is simply to correct based on mean values of previous data while others are based on the choice of different quantiles. In order to ensure the accuracy of the study, an important assumption is that the statistical properties of the present climate bias are maintained in the future, which can guarantee the experimental results are useful in future climate predictions. The observation data are obtained using buoy measurement positioned in special locations and the original simulation data is simulated in ERA5 (the fifth generation ECMWF atmospheric reanalysis of the global climate). The main variables that are considered are (significant wave height), (peak period) and (wind speed), which are corrected using three common bias corrections: Delta method, The Empirical Quantile Mapping method (EQM) and the Empirical Gumbel Quantile Mapping method (EGQM). These three bias correction (BC) methods are used to correct original simulation data, making it as close to the observation as possible, and finally the most suitable method would be selected to correct the simulation data in the future, for which on the study of Spanish coast, is the EQM.

Relators: Giuseppe Giorgi
Academic year: 2022/23
Publication type: Electronic
Number of Pages: 69
Corso di laurea: Corso di laurea magistrale in Ingegneria Meccanica (Mechanical Engineering)
Classe di laurea: New organization > Master science > LM-33 - MECHANICAL ENGINEERING
Aziende collaboratrici: UNSPECIFIED
URI: http://webthesis.biblio.polito.it/id/eprint/24410
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