Statistical Bias Correction for Simulated Wind Speeds Over CORDEX‐East Asia

Abstract

Surface wind is significant for ocean state climate, ocean mixing, and viability of wind energy techniques. However, surface wind simulated from the regional climate model generally features substantial bias from observation. For the first time, this study compares the performance of five bias correction techniques, (1) linear scaling, (2) variance scaling, (3) quantile mapping based on empirical distribution, (4) quantile mapping based on Weibull distribution, and (5) cumulative distribution functions transformation, in reducing the statistical bias of a regional climate model wind output, which was downscaled from a global climate model CNRM‐CM5 during 1991–2000. The surface wind of JRA55 reanalysis data is used as reference. Results show that all bias correction methods are consistent in reducing the climatological mean bias in spatial patterns and intensities. The linear scaling method always performs the worst among all methods in correcting higher‐order statistical biases such as skewness, kurtosis, and wind power density. The other four bias correction methods are generally similar in reducing the statistical biases of different measures based on spatial distribution maps. However, when it comes to spatial averaged mean of statistical measures over CORDEX‐East Asia in January and July, the quantile mapping based on Weibull distribution generally shows the best skills among all methods in bias reduction.

Historical Evaluation and Future Projections of 100‐m Wind Energy Potentials Over CORDEX‐East Asia

“…RCMs generally feature nonnegligible biases in simulating wind speeds, which is a result of a nonlinear combination of inherited systematic bias from the forcing data set and the bias generated by the RCMs (Li, von Storch, & Geyer, 2016). Although quantile mapping based on Weibull distribution has been used and was shown to be robust to correct simulated wind bias over Europe or East Asia (Li et al, 2019;Moemken et al, 2018), this method can only correct univariate wind speed time series; it does not consider the dependence between u and v wind components, and thus, it does not correct wind direction. Alternatively, multivariate bias adjustment methods have been developed by Bürger et al (2011), Vrac and Friederichs (2015) and Cannon (2016Cannon ( , 2018.…”

“…Therefore, the direct use of wind outputs at hub height is suggested for wind energy estimations (Geyer et al, 2015). Second, both GCMs and RCMs feature substantial biases in simulating wind speeds (Chen et al, 2012; Li et al, 2019), which may impact the climate change estimation of projected wind energy production. However, most studies did not apply bias adjustment before estimating the change in wind energy.…”

COSMO-CLM Regional Climate Simulations in the CORDEX framework: a review

“…The standard aerosol dataset was replaced with Tegen (Tegen et al, 1997) aerosol climatology. These simulations have been applied in scientific studies focusing on model evaluation or projected change in surface temperature, precipitation and wind speed/energy over CORDEX-EAS (Li et al, 2018(Li et al, , 2019(Li et al, , 2020.…”

Calibration of Reanalysis Data against Wind Measurements for Energy Production Estimation of Building Integrated Savonius-Type Wind Turbine

“…This technique seemed appropriate for energy output estimation purposes because it makes the PDF of the reanalysis data more akin to that of the measurements. It has already been used by the authors in a wider context of renewable energies, such as the study of historical wave energy trends in Ireland, Chile, or the Bay of Biscay [13][14][15]27], and by other researchers in the context of solar [16,17] and wind energy [18][19][20].…”

“…Then, those functions can be used to come up with forecasts when observations are not available. Apart from its extensive use in the field of meteorology, it has already been used in the context of wave [13][14][15], solar [16,17], and wind energy [18][19][20].…”