Validation of BARRA2 and comparison with MERRA-2 and ERA5 using historical wind power generation

Graham Palmer; Roger Dargaville; Chun-Hsu Su; Changlong Wang; Andrew Hoadley; Damon Honnery

doi:10.1071/ES24028

RESEARCH ARTICLE (Open Access)

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Validation of BARRA2 and comparison with MERRA-2 and ERA5 using historical wind power generation

Graham Palmer

^A ^* , Roger Dargaville ^A , Chun-Hsu Su

^C , Changlong Wang ^A ^B , Andrew Hoadley ^A and Damon Honnery ^A

+ Author Affiliations

- Author Affiliations

^A Monash University, Clayton, Vic. 3800, Australia.

^B The University of Melbourne, Parkville, Vic. 3010, Australia.

^C The Bureau of Meteorology, Docklands, Vic. 3008, Australia.

^* Correspondence to: graham.palmer@monash.edu

Handling Editor: Anthony Rea

Journal of Southern Hemisphere Earth Systems Science 75, ES24028 https://doi.org/10.1071/ES24028

Submitted: 25 July 2024 Accepted: 30 January 2025 Published: 18 March 2025

© 2025 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of the Bureau of Meteorology. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)

Abstract

Atmospheric reanalyses are a popular source of wind speed data for energy modelling but are known to exhibit biases. Such biases can have a significant impact on the validity of techno-economic energy assessments that include simulated wind power. This study assesses the Australian BARRA-R2 (Bureau of Meteorology Atmospheric Regional Reanalysis for Australia, version 2) atmospheric reanalysis, and compares it with MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, V2) and ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis, fifth generation). Simulated wind power is compared with observed power from 54 wind farms across Australia using site-specific wind turbine specifications. We find that all of the reanalyses replicate wind speed patterns associated with the passage of weather systems. However, modelled power can diverge significantly from observed power at times. Assessed by bias, correlation and error, BARRA-R2 gave the best results, followed by MERRA-2, then ERA5. Annual bias can be readily corrected by wind speed scaling; however, linear scaling will not narrow the error distribution, or reduce the associated error in the frequency distribution of wind power. At the level of a wind farm, site-specific factors and microscale wind behaviour are contributing to differences between simulated and observed power. Although the performance of all the reanalyses is good at times, variability is high and site-dependent. We recommend the use of confidence intervals that reflect the degree of uncertainty in wind power simulation, and the degree of confidence required in the energy system model.

Keywords: Australia, BARRA, energy system modelling, ERA5, MERRA-2, National Electricity Market, NEM, optimisation, reanalyses, South West Interconnected System, SWIS, wind farm, wind power, wind turbines.

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