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Downscaling ERA5 wind speed data: a machine learning approach considering topographic influences

Hu, Wenxuan and Scholz, Yvonne and Yeligeti, Madhura and von Bremen, Lueder and Deng, Ying (2023) Downscaling ERA5 wind speed data: a machine learning approach considering topographic influences. Environmental Research Letters, 18 (9). Institute of Physics (IOP) Publishing. doi: 10.1088/1748-9326/aceb0a. ISSN 1748-9326.

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Official URL: https://iopscience.iop.org/article/10.1088/1748-9326/aceb0a/meta


Energy system modeling and analysis can provide comprehensive guidelines to integrate renewable energy sources into the energy system. Modeling renewable energy potential, such as wind energy, typically involves the use of wind speed time series in the modeling process. One of the most widely utilized datasets in this regard is ERA5, which provides global meteorological information. Despite its broad coverage, the coarse spatial resolution of ERA5 data presents challenges in examining local-scale effects on energy systems, such as battery storage for small-scale wind farms or community energy systems. In this study, we introduce a robust statistical downscaling approach that utilizes a machine learning approach to improve the resolution of ERA5 wind speed data from around 31 km × 31 km to 1 km × 1 km. To ensure optimal results, a comprehensive preprocessing step is performed to classify regions into three classes based on the quality of ERA5 wind speed estimates. Subsequently, a regression method is applied to each class to downscale the ERA5 wind speed time series by considering the relationship between ERA5 data, observations from weather stations, and topographic metrics. Our results indicate that this approach significantly improves the performance of ERA5 wind speed data in complex terrain. To ensure the effectiveness and robustness of our approach, we also perform thorough evaluations by comparing our results with the reference dataset COSMO-REA6 and validating with independent datasets.

Item URL in elib:https://elib.dlr.de/196694/
Document Type:Article
Title:Downscaling ERA5 wind speed data: a machine learning approach considering topographic influences
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Scholz, YvonneUNSPECIFIEDhttps://orcid.org/0000-0002-1633-3825UNSPECIFIED
Yeligeti, MadhuraUNSPECIFIEDhttps://orcid.org/0000-0002-9643-465XUNSPECIFIED
von Bremen, LuederUNSPECIFIEDhttps://orcid.org/0000-0002-7072-0738UNSPECIFIED
Date:10 August 2023
Journal or Publication Title:Environmental Research Letters
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
Publisher:Institute of Physics (IOP) Publishing
Keywords:downscaling, wind speed, time series, machine learning, reanalysis, ERA5
HGF - Research field:Energy
HGF - Program:Energy System Design
HGF - Program Themes:Energy System Transformation
DLR - Research area:Energy
DLR - Program:E SY - Energy System Technology and Analysis
DLR - Research theme (Project):E - Systems Analysis and Technology Assessment
Location: Stuttgart
Institutes and Institutions:Institute of Networked Energy Systems > Energy Systems Analysis, ST
Deposited By: Yeligeti, Madhura
Deposited On:25 Sep 2023 15:31
Last Modified:19 Oct 2023 09:45

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