Assessing wind dynamics and turbine power at Princess Elisabeth station, Antarctica, using Doppler wind LiDAR and vertical array anemometers

This study introduces a novel methodology to assess wind turbine energy production at the Princess Elisabeth Antarctica (PEA) station. The objective is to improve bias correction of reanalysis wind data by combining in situ measurements, ERA5(-Land) reanalysis, and a new statistical approach. Traditional Quantile Mapping (QM) requires overlapping observation-reanalysis periods, limiting its scope. The proposed Parameterized Quantile Mapping (PQM) applies an exponential mapping to the CDF relationship between observed and ERA5(-Land) data, enabling consistent bias correction beyond the calibration period. This two-parameter, physically-informed method produces realistic wind speed time series in data-scarce regions. Applied to ERA5-Land, PQM improves hub-height wind and power estimates, reducing annual wind energy production error at PEA to 12%, and strengthening confidence in wind energy assessments across Antarctica.