Sensitivity of Simulated Wind Speed to Spatial Resolution over Complex Terrain

Giovannini, Lorenzo; Antonacci, Gianluca; Zardi, Dino; Laiti, Lavinia; Panziera, Luca

doi:10.1016/j.egypro.2014.10.384

Cited by 40 publications

(40 citation statements)

References 14 publications

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“…4). The outcomes indicate the use of coarser WRF input data from 4 km grid instead of 1 km grid may be a better solution, since the WRF model does not always produce better results for finer resolution grids in the areas of complex orography and without additional data assimilation [21]. This is confirmed by the values of statistical model evaluation parameters (Tab.…”

Section: Similarity Assessment Of the 1-hour Concentration Distributionssupporting

confidence: 69%

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Assessment of the AERMOD dispersion model over complex terrain with different types of meteorological data: Tracy Power Plant experiment

et al. 2017

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Abstract. The accuracy of air pollutants dispersion modelling results depends on the quality of the input data, including the representativeness of the meteorological data. The paper presents the results of the AERMOD model validation using data from Tracy Power Plant experiment (Nevada, USA) with various meteorological data sources, including WRF modelling system outputs. The highest efficiency of the AERMOD model performance was found using site-specific meteorological data and the results from the WRF model. In general, the AERMOD modelling system inadequately represents the concentration levels of tracer gas (SF6) at receptors situated below the emitter height in areas of complex topography.

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Section: Similarity Assessment Of the 1-hour Concentration Distributionssupporting

confidence: 69%

“…slope and valley winds) induced by cyclic changes in the amount of energy reaching the surface of the earth [20,21]. High variability of surface wind speed and direction during the experiment in the area of study is shown in Fig.…”

Section: Comparison Of Meteorological Datamentioning

confidence: 99%

Assessment of the AERMOD dispersion model over complex terrain with different types of meteorological data: Tracy Power Plant experiment

et al. 2017

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“…All available data were quality‐controlled following the procedures reported in Jiménez et al (2010) and Chávez‐Arroyo and Probst (2013) (see Giovannini et al , 2014a for more details).…”

Section: Study Area and Datasetmentioning

confidence: 99%

The thermally driven diurnal wind system of the Adige Valley in the Italian Alps

Giovannini

Laiti

Serafin

et al. 2017

Quart J Royal Meteoro Soc

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The Adige Valley is one of the major corridors connecting the Po Plain with the inner Alps. A series of permanent weather stations and one wind profiler provide regular monitoring of air temperature, atmospheric pressure, global solar radiation, wind speed and direction over the 140 km valley length and in the adjacent plain. Data from these stations are analyzed for a subset of days on which weather conditions favoured full development of diurnal valley winds in the period 2012–2014. The analysis highlights typical features in the alternating patterns of diurnal up‐valley winds and nocturnal down‐valley winds. In particular, the wind intensity depends linearly on the along‐valley pressure gradient, supporting the concept of a quasi‐steady balance between the pressure gradient and surface friction. Also, in agreement with previous investigations, the amplitude of the surface pressure cycle increases in the up‐valley direction, causing the reversal of the horizontal pressure gradient twice per day. In contrast, no appreciable along‐valley variation in the diurnal temperature range is found. The analysis of surface temperature and pressure measurements suggests that the larger pressure perturbations found far into the valley are caused by the increased depth of the atmospheric layer subject to heating and cooling. Local inhomogeneities in the valley cross‐section, in particular in the vicinity of a large basin, cause temperature and pressure perturbations that are strong enough to alter the typical cycle of down‐ and up‐valley winds. Similarly, local wind convergence over the major cities during the night is explained in terms of the urban heat island effect.

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“…Since the air is a continuous media, the continuity equality is to be added [3] To complete this mathematical foundation, the equality of state [4] and the adiabatic equality have to be added ( ) [5] to get six equations with six unknown , , , , , u v w p T  . In the case of the heat flux / dQ dt , the (5) is replaced by [6] where and are given by [7] If the wind blows horizontally in average, upon the supposition of air incompressibility ( /0 d dt   ), but pressure bias ' p p p  , the eq.…”

Section: Fractal Characteristic Of Wind Speed Fluctuationmentioning

confidence: 99%

“…There are some data-driven standardization schemes for wind speed estimation for different terrains [3]. There is large influence of the terrain on the wind speed fluctuation [4,5]. Artificial neural networks (ANN) were applied to determine annual wind speed for some complex terrain [6].…”

Section: Introductionmentioning

confidence: 99%

Estimation of fractal representation of wind speed fluctuation by artificial neural network with different training algorothms

Petković

Nikolić

Mitić

et al. 2017

Flow Measurement and Instrumentation

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Cite this article as: Dalibor Petković, Vlastimir Nikolić, Vojislav V. Mitić and Ljubiša Kocić, Estimation of fractal representation of wind speed fluctuation by artificial neural network with different training algorothms, Flow Measurement and Instrumentation, http://dx. AbstractSince the wind speed fluctuation could cause large instability in wind energy systems it is crucial to develop a method for precise estimation of the wind speed fluctuation. Fractal interpolation of the wind speed could be used to improve the accuracy of the estimation of the wind speed fluctuation. Based on the self-similarity feature, the fractal interpolation could be established from internal to external interval. In this article fractal interpolation was used to improve the wind speed fluctuation estimation by soft computing methods. Artificial neural network (ANN) with different training algorithms were used in order to estimate the wind speed fluctuation based on the fractal interpolation. Highlights The fluctuation of wind speed affects energy conversion of wind energy systems.  Fractal characteristics of the wind speed series were analyzed.  An improved fractal interpolation to predict the wind speed series.  Prediction of the wind speed fractal representation was established.

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Sensitivity of Simulated Wind Speed to Spatial Resolution over Complex Terrain

Cited by 40 publications

References 14 publications

Assessment of the AERMOD dispersion model over complex terrain with different types of meteorological data: Tracy Power Plant experiment

Assessment of the AERMOD dispersion model over complex terrain with different types of meteorological data: Tracy Power Plant experiment

The thermally driven diurnal wind system of the Adige Valley in the Italian Alps

Estimation of fractal representation of wind speed fluctuation by artificial neural network with different training algorothms

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