Combined high-resolution rainfall and wind data collected for 3 months on a wind farm 110 km southeast of Paris (France)

Gires, Auguste; Jose, Jerry; Tchiguirinskaia, Ioulia; Schertzer, Daniel

doi:10.5194/essd-14-3807-2022

Cited by 6 publications

(12 citation statements)

References 20 publications

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“…In order to account for the temporal variability in this study, we used data collected on a meteorological mast located on Pays d'Othe wind farm which is about 110 km southeast of Paris (France). An in-depth presentation of the whole data set as well as a link to download it are available in [8]. Only the required elements are summarized here.…”

Section: Rainfall and Wind Datamentioning

confidence: 99%

“…The effective sampling area is slightly dependant on the drop size to account for side effects and is denoted S eff (D i ). All the values can be found in [8]. Rainfall related quantities can be derived from the matrix n i,j .…”

Section: Rainfall and Wind Datamentioning

confidence: 99%

“…This yields: where ρ w is the water density. The discrete DSD for a time step Δt is computed as [8]:…”

Section: Model To Compute Collected Mass Of Watermentioning

confidence: 99%

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Should you walk, run or sprint in the rain to get less wet?

Zaegel,

Vehils-Vinals,

Guastalla

et al. 2023

Eur. J. Phys.

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We have all wondered once whether we should walk, run or sprint under the rain in order to stay as dry as possible. Previous publications already addressed this subject using simple models, as for the shape of the body and the description of the rain and wind. This paper presents a detailed approach which relies on a more realistic 'human body' shape and accounts of the variability in time of both the wind and the rain drop size and velocity distributions. It appears that in some seldom cases with tailwind and light rain, there is an optimum velocity, but in general it is better to run as fast as possible. While 'running' instead of 'walking' yields significant gain, the extra effort required to 'sprint' is not always worth it.

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Section: Rainfall and Wind Datamentioning

confidence: 99%

Section: Rainfall and Wind Datamentioning

confidence: 99%

See 1 more Smart Citation

Should you walk, run or sprint in the rain to get less wet?

Zaegel,

Vehils-Vinals,

Guastalla

et al. 2023

Eur. J. Phys.

Self Cite

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“…The wind farm is roughly 120 km south-east of Paris on a slightly sloppy area. More details can be found in the data paper under discussion at ESSD (Gires et al, 2022) and in the data set (Gires et al, 2021). Two wind series with very different average horizontal wind speeds (i.e.…”

Section: Wind Datamentioning

confidence: 99%

“…Wind data used in this paper can be found at https://doi.org/10.5281/zenodo.5801900 (Gires et al, 2021). The description of the data set can be found in Gires et al (2022Gires et al ( , https://doi.org/10.5194/essd-14-3807-2022.…”

mentioning

confidence: 99%

3D trajectories and velocities of rainfall drops in a multifractal turbulent wind field

2022

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Abstract. Weather radars measure rainfall in altitude, whereas hydro-meteorologists are mainly interested in rainfall at ground level. During their fall, drops are advected by the wind, which affects the location of the measured field. The governing equation of a rain drop's motion relates the acceleration to the forces of gravity and buoyancy along with the drag force. It depends non-linearly on the instantaneous relative velocity between the drop and the local wind, which yields complex behaviour. Here, the drag force is expressed in a standard way with the help of a drag coefficient expressed as a function of the Reynolds number. Corrections accounting for the oblateness of drops greater than 1–2 mm are suggested and validated through a comparison of the retrieved “terminal fall velocity” (i.e. without wind) with commonly used relationships in the literature. An explicit numerical scheme is then implemented to solve this equation for a 3+1D turbulent wind field, and hence analyse the temporal evolution of the velocities and trajectories of rain drops during their fall. It appears that multifractal features of the input wind are simply transferred to the drop velocity with an additional fractional integration whose level depends on the drop size, and a slight time shift. Using an actual high-resolution 3D sonic anemometer and a scale invariant approach to simulate realistic fluctuations of wind in space, trajectories of drops of various sizes falling form 1500 m are studied. For a strong wind event, drops located within a radar gate in altitude during 5 min are spread on the ground over an area of the size of a few kilometres. The spread for drops of a given diameter is found to cover a few radar pixels. Consequences on measurements of hydro-meteorological extremes that are needed to improve the resilience of urban areas are discussed.

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Part 1: Multifractal analysis of wind turbine power and the associated biases

Jose,

Gires,

Roustan

et al. 2024

Preprint

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Abstract. The inherent variability in atmospheric fields, which extends over a wide range of temporal and spatial scales, also gets transferred to energy fields extracted off them. In the specific case of wind power generation, this can be seen in the theoretical power available for extraction in the atmosphere as well as the empirical power produced by turbines. Further the power produced by turbines are affected by atmospheric turbulence as well as other fields it interact with. For modelling as well as analyzing them, quantification of their variability, intermittency and correlations with other interacting fields is important. To understand the uncertainties involved in power production, power outputs from four 2MW turbines are analyzed from an operational wind farm at Pay d’Othe, 110 km southeast of Paris, France. Using simultaneously measured wind velocity from the same location, the variability in power available at the wind farm, and power produced by wind turbines were analyzed. To account for the intermittency and variability in said fields, the framework of Universal Multifractals (UM) is used. UM is a widely used, physically based, scale invariant framework for characterizing and simulating geophysical fields over a wide range of scales. While statistically analysing the power produced by the turbine, rated power acts like an upper threshold resulting in biased estimators. This is identified and quantified here using the theoretical framework of UM along with the actual sampling resolution of instruments under study. The validity of this bias in framework is further tested and illustrated using numerical simulations of fields with the same multifractal properties. Understanding instrumental thresholds and their effect in analysis is important in retrieving actual fields and modelling them, more so, in the case of power production where the uncertainties due to turbulence are already a leading challenge. This is further expanded in the second part where the influence of rainfall in power production is studied using scale invariant tools of UM and joint multifractals.

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Combined high-resolution rainfall and wind data collected for 3 months on a wind farm 110 km southeast of Paris (France)

Cited by 6 publications

References 20 publications

Should you walk, run or sprint in the rain to get less wet?

Should you walk, run or sprint in the rain to get less wet?

3D trajectories and velocities of rainfall drops in a multifractal turbulent wind field

Part 1: Multifractal analysis of wind turbine power and the associated biases

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