High Spatial Resolution Simulation of Annual Wind Energy Yield Using Near-Surface Wind Speed Time Series

Jung, Christopher

doi:10.3390/en9050344

Cited by 27 publications

(18 citation statements)

References 56 publications

(61 reference statements)

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“…List of predictor variables available for maximum gust speed (U max,mod ) models building. For detailed information on predictor variables see Jung andSchindler (2015, 2016), Jung (2016). Table S3.…”

Section: Discussionmentioning

confidence: 99%

“…The orographic features relative elevation ( Φ ) (Jung, ), orographic sheltering ( τ ) (Wilson, ; Quine and White, ), curvature ( ϕ ) and aspect ( η ) were computed from a digital terrain model with a resolution of 50 × 50 m using the ArcGIS® 10.2 software.…”

Section: Methodsmentioning

confidence: 99%

“…The Wakeby distribution is defined by its quantile function (Houghton, ; Jung and Schindler, , ; Jung, )

\begin{matrix} U_{normalmax} (F) = & ϵ + \frac{α}{β} \cdot [1 - {((), 1 - F)}^{β}] \\ - \frac{γ}{δ} \cdot [1 - {((), 1 - F)}^{- δ}] \end{matrix}

where F is the non‐exceedance probability and U max ( F ) is the associated gust speed value. The parameters α , β , γ and δ are distribution parameters and ϵ is the location parameter.…”

Section: Methodsmentioning

confidence: 99%

“…The orographic features relative elevation ( ) (Jung, 2016), orographic sheltering ( ) (Wilson, 1984;Quine and White, 1998), curvature ( ) and aspect ( ) were computed from a digital terrain model with a resolution of 50 × 50 m using the ArcGIS ® 10.2 software. For analysis and model building all available data were mapped from their original resolution to a 50 × 50 m resolution grid.…”

Section: Study Area and Datamentioning

confidence: 99%

“…The Wakeby distribution is defined by its quantile function (Houghton, 1978;Jung andSchindler, 2015, 2016;Jung, 2016)…”

Section: Extreme Value Analysismentioning

confidence: 99%

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Using highly resolved maximum gust speed as predictor for forest storm damage caused by the high‐impact winter storm Lothar in Southwest Germany

Schindler

Jung

Buchholz

2016

Atmospheric Science Letters

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Results from a newly available empirical maximum gust speed model were evaluated for their predictive power of forest storm damage caused by the high-impact winter storm 'Lothar' in the German federal state of Baden-Wuerttemberg. In this state, Lothar was the most severe storm event of the last decades, causing nearly 30 million m 3 of damaged timber. By applying a least squares boosting procedure, daily maximum gust speed values measured at 28 meteorological stations were used to empirically model highly resolved (50 × 50 m) near-surface gust speed fields associated with Lothar. Gust speed was modelled using terrainand roughness-related variables as predictors. The modelled gust speed fields were then used as input to an empirical forest storm damage model. To build the damage model, the machine learning method random forests was applied. Results from this study demonstrate that the empirically modelled maximum gust speed field associated with Lothar was the most important predictor variable for forest storm damage at the landscape scale. Modelled maximum gust speed was nearly twice as important as all other available predictor variables. The medians of classified proportions of storm-damaged timber quasi-linearly increased with increasing maximum gust speed.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…The Wakeby distribution is defined by its quantile function (Houghton, ; Jung and Schindler, , ; Jung, )

\begin{matrix} U_{normalmax} (F) = & ϵ + \frac{α}{β} \cdot [1 - {((), 1 - F)}^{β}] \\ - \frac{γ}{δ} \cdot [1 - {((), 1 - F)}^{- δ}] \end{matrix}

where F is the non‐exceedance probability and U max ( F ) is the associated gust speed value. The parameters α , β , γ and δ are distribution parameters and ϵ is the location parameter.…”