Can dynamically downscaled windstorm footprints be improved by observations through a probabilistic approach?

Haas, Rabea; Pinto, Joaquim G.; Born, K.

doi:10.1002/2013jd020882

Cited by 16 publications

(15 citation statements)

References 36 publications

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“…Several studies have investigated the performance of both RCMs (CCLM and RCA4, or its preceding version RCA3) to represent near surface wind speeds, typically focusing on individual countries with available wind observations. For example, Haas and Pinto (), Born et al (), Haas et al (), and Reyers et al () evaluated wind speeds and wind gusts from ERA‐Interim‐driven CCLM simulations against station‐based observational data over Germany. These studies generally agree on a good representation of wind speed distributions in CCLM compared to observations.…”

Section: Methodsmentioning

confidence: 99%

“…Since these biases may influence the climate change signal, in particular when analyzing wind speed thresholds relevant for wind energy production, a bias correction was applied to the three‐hourly 10 m wind speeds from both the historical and the scenario runs. In a first step, theoretical Weibull distributions are fitted to the wind speed time series of the historical runs and the ERA‐Interim evaluation runs (following, e.g., Haas et al, ). The cumulative distribution function:

F ((), x) = 1 - exp ([], - {(\frac{true}{x})}^{β})

is used to estimate the scale ( α ) and shape ( β ) parameters.…”

Section: Methodsmentioning

confidence: 99%

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Future Changes of Wind Speed and Wind Energy Potentials in EURO‐CORDEX Ensemble Simulations

et al. 2018

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Renewable energy production is strongly influenced by weather and climate. Regional climate projections can be useful to quantify climate change impacts on renewable energies. With this aim, we analyze future changes of wind speed and wind energy potentials using a multimodel ensemble of EURO-CORDEX simulations at 12 km and three-hourly resolution, considering nine different global and regional climate model chains. A comparison between modeled historical 10 m wind speeds and ERA-Interim-driven evaluation runs for the same regional climate models uncovers some substantial model biases. The bias-corrected 10 m wind speeds are extrapolated to the hub height of a wind turbine to derive gridded wind energy output (Eout). The ensemble mean responses project only small changes of mean annual and winter Eout for large parts of Europe in future decades, but a considerable decrease for summer Eout. In terms of variability, increasing intraannual and interdaily variabilities are projected for large parts of northern, central, and eastern Europe. While the ensemble spread is quite large for interdaily variability, results are more robust for intraannual variability. With respect to wind speed characteristics relevant for wind energy production, a robust increase in the occurrence of low wind speeds (<3 m/s) is detected. Due to a combination of higher annual mean Eout and lower intraannual variability, climate change could be beneficial for regions like Baltic and Aegean Sea. For large parts of Germany, France, and Iberia, a lower mean Eout and increased intraannual variability may imply larger temporal/spatial fluctuations in future wind energy production and therefore a more challenging wind energy management.

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Section: Methodsmentioning

confidence: 99%

F ((), x) = 1 - exp ([], - {(\frac{true}{x})}^{β})

is used to estimate the scale ( α ) and shape ( β ) parameters.…”

Section: Methodsmentioning

confidence: 99%

Future Changes of Wind Speed and Wind Energy Potentials in EURO‐CORDEX Ensemble Simulations

et al. 2018

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“…The Weibull distribution-based quantile mapping method (Haas et al, 2014) assumes that the probability distributions of both observed and simulated wind speeds can be approximated using a Weibull distribution:…”

Section: Quantile Mapping Based On Weibull Distributionmentioning

confidence: 99%

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

Feng

et al. 2019

Earth and Space Science

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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.

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“…Since the transfer function is monotone, this relationship is also shared by the quantiles of the log-transformed variables (Stewart and Essenwanger 1978); a similar result was demonstrated by Haas et al (2014). The validity of this relationship can be tested by making quantilequantile plots of the log-transformed variable.…”

Section: Transforming Variables To Have Different Weibull Distribumentioning

confidence: 61%

“…Haas et al (2014) used a Weibull distribution probability mapping to adjust model-simulated daily maximum wind speeds and gust speeds to point observations, finding a significant improvement in wind speed estimates. Zhou and Smith (2013) identified considerable regional variability in the shape parameter, with values ranging from 1 to 4; however, they suggest that a comparison of Weibull distribution parameters may provide a useful way to capture differences between observed and simulated wind speeds.…”

Section: A Weibull Distributionmentioning

confidence: 99%

A Weibull Approach for Improving Climate Model Projections of Tropical Cyclone Wind-Speed Distributions

et al. 2014

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Reliable estimates of future changes in extreme weather phenomena, such as tropical cyclone maximum wind speeds, are critical for climate change impact assessments and the development of appropriate adaptation strategies. However, global and regional climate model outputs are often too coarse for direct use in these applications, with variables such as wind speed having truncated probability distributions compared to those of observations. This poses two problems: How can model-simulated variables best be adjusted to make them more realistic? And how can such adjustments be used to make more reliable predictions of future changes in their distribution?This study investigates North Atlantic tropical cyclone maximum wind speeds from observations (1950-2010) and regional climate model simulations (1995-2005 and 2045-55 at 12-and 36-km spatial resolutions). The wind speed distributions in these datasets are well represented by the Weibull distribution, albeit with different scale and shape parameters.A power-law transfer function is used to recalibrate the Weibull variables and obtain future projections of wind speeds. Two different strategies, bias correction and change factor, are tested by using 36-km model data to predict future 12-km model data (pseudo-observations). The strategies are also applied to the observations to obtain likely predictions of the future distributions of wind speeds. The strategies yield similar predictions of likely changes in the fraction of events within Saffir-Simpson categories-for example, an increase from 21% (1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005) to 27%-37% (2045-55) for category 3 or above events and an increase from 1.6% (1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005) to 2.8%-9.8% (2045-55) for category 5 events.

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Can dynamically downscaled windstorm footprints be improved by observations through a probabilistic approach?

Cited by 16 publications

References 36 publications

Future Changes of Wind Speed and Wind Energy Potentials in EURO‐CORDEX Ensemble Simulations

Future Changes of Wind Speed and Wind Energy Potentials in EURO‐CORDEX Ensemble Simulations

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

A Weibull Approach for Improving Climate Model Projections of Tropical Cyclone Wind-Speed Distributions

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