Articles | Volume 31, issue 4
https://doi.org/10.5194/npg-31-603-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-31-603-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Multifractal analysis of wind turbine power and rainfall from an operational wind farm – Part 2: Joint analysis of available wind power and rain intensity
HM&Co, École nationale des ponts et chaussées, Institut Polytechnique de Paris, Champs-sur-Marne, France
Auguste Gires
HM&Co, École nationale des ponts et chaussées, Institut Polytechnique de Paris, Champs-sur-Marne, France
Ernani Schnorenberger
Boralex, Lyon, France
Yelva Roustan
CEREA, École nationale des ponts et chaussées, Institut Polytechnique de Paris, EDF R&D, Île-de-France, France
Daniel Schertzer
HM&Co, École nationale des ponts et chaussées, Institut Polytechnique de Paris, Champs-sur-Marne, France
Department of Civil and Environmental Engineering, Imperial College London, London, UK
Department of Complexity Science, Potsdam Institute for Climate Impact Research, Potsdam, Germany
Ioulia Tchiguirinskaia
HM&Co, École nationale des ponts et chaussées, Institut Polytechnique de Paris, Champs-sur-Marne, France
Related authors
Jerry Jose, Auguste Gires, Yelva Roustan, Ernani Schnorenberger, Ioulia Tchiguirinskaia, and Daniel Schertzer
Nonlin. Processes Geophys., 31, 587–602, https://doi.org/10.5194/npg-31-587-2024, https://doi.org/10.5194/npg-31-587-2024, 2024
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Wind energy exhibits extreme variability in space and time. However, it also shows scaling properties (properties that remain similar across different times and spaces of measurement). This can be quantified using appropriate statistical tools. In this way, the scaling properties of power from a wind farm are analysed here. Since every turbine is manufactured by design for a rated power, this acts as an upper limit on the data. This bias is identified here using data and numerical simulations.
Auguste Gires, Jerry Jose, Ioulia Tchiguirinskaia, and Daniel Schertzer
Earth Syst. Sci. Data, 14, 3807–3819, https://doi.org/10.5194/essd-14-3807-2022, https://doi.org/10.5194/essd-14-3807-2022, 2022
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The Hydrology Meteorology and Complexity laboratory of École des Ponts ParisTech (https://hmco.enpc.fr) has made a data set of high-resolution atmospheric measurements (rainfall, wind, temperature, pressure, and humidity) available. It comes from a campaign carried out on a meteorological mast located on a wind farm in the framework of the Rainfall Wind Turbine or Turbulence project (RW-Turb; supported by the French National Research Agency – ANR-19-CE05-0022).
Jerry Jose, Auguste Gires, Yelva Roustan, Ernani Schnorenberger, Ioulia Tchiguirinskaia, and Daniel Schertzer
Nonlin. Processes Geophys., 31, 587–602, https://doi.org/10.5194/npg-31-587-2024, https://doi.org/10.5194/npg-31-587-2024, 2024
Short summary
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Wind energy exhibits extreme variability in space and time. However, it also shows scaling properties (properties that remain similar across different times and spaces of measurement). This can be quantified using appropriate statistical tools. In this way, the scaling properties of power from a wind farm are analysed here. Since every turbine is manufactured by design for a rated power, this acts as an upper limit on the data. This bias is identified here using data and numerical simulations.
Adarsh Jojo Thomas, Jürgen Kurths, and Daniel Schertzer
EGUsphere, https://doi.org/10.5194/egusphere-2024-2793, https://doi.org/10.5194/egusphere-2024-2793, 2024
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We have developed a systematic approach to study the climate system at multiple scales using climate networks, which have been previously used to study correlations between time series in space at only a single scale. This new approach is used here to upscale precipitation climate networks to study the Indian Monsoon and analyse strong dependencies between spatial regions, which change with changing scale.
Marc Bocquet, Pierre J. Vanderbecken, Alban Farchi, Joffrey Dumont Le Brazidec, and Yelva Roustan
Nonlin. Processes Geophys., 31, 335–357, https://doi.org/10.5194/npg-31-335-2024, https://doi.org/10.5194/npg-31-335-2024, 2024
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A novel approach, optimal transport data assimilation (OTDA), is introduced to merge DA and OT concepts. By leveraging OT's displacement interpolation in space, it minimises mislocation errors within DA applied to physical fields, such as water vapour, hydrometeors, and chemical species. Its richness and flexibility are showcased through one- and two-dimensional illustrations.
Alexis Squarcioni, Yelva Roustan, Myrto Valari, Youngseob Kim, Karine Sartelet, Lya Lugon, Fabrice Dugay, and Robin Voitot
EGUsphere, https://doi.org/10.5194/egusphere-2024-1043, https://doi.org/10.5194/egusphere-2024-1043, 2024
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This study highlights the interest of using a street network model to estimate pollutant concentrations of NOx, NO2, and PM2.5 in heterogeneous urban areas, particularly those adjacent to highways, compared with the Subgrid approach embedded in the 3D eulerian model CHIMERE. However, the study also reveals comparable performance between the two approaches for the aforementioned pollutants in areas near the city centre, where urban characteristics are more uniform.
Pierre-Antoine Versini, Leydy Alejandra Castellanos-Diaz, David Ramier, and Ioulia Tchiguirinskaia
Earth Syst. Sci. Data, 16, 2351–2366, https://doi.org/10.5194/essd-16-2351-2024, https://doi.org/10.5194/essd-16-2351-2024, 2024
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Nature-based solutions (NBSs), such as green roofs, have appeared as relevant solutions to mitigate urban heat islands. The evapotranspiration (ET) process allows NBSs to cool the air. To improve our knowledge about ET assessment, this paper presents some experimental measurement campaigns carried out during three consecutive summers. Data are available for three different (large, small, and point-based) spatial scales.
Hai Zhou, Daniel Schertzer, and Ioulia Tchiguirinskaia
EGUsphere, https://doi.org/10.5194/egusphere-2023-2710, https://doi.org/10.5194/egusphere-2023-2710, 2024
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The hybrid VMD-RNN model provides a reliable one-step-ahead prediction, with better performance in predicting high and low values than the pure LSTM model. The universal multifractals technique is also introduced to evaluate prediction performance, thus validating the usefulness and applicability of the hybrid model.
Thibaud Sarica, Alice Maison, Yelva Roustan, Matthias Ketzel, Steen Solvang Jensen, Youngseob Kim, Christophe Chaillou, and Karine Sartelet
Geosci. Model Dev., 16, 5281–5303, https://doi.org/10.5194/gmd-16-5281-2023, https://doi.org/10.5194/gmd-16-5281-2023, 2023
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A new version of the Model of Urban Network of Intersecting Canyons and Highways (MUNICH) is developed to represent heterogeneities of concentrations in streets. The street volume is discretized vertically and horizontally to limit the artificial dilution of emissions and concentrations. This new version is applied to street networks in Copenhagen and Paris. The comparisons to observations are improved, with higher concentrations of pollutants emitted by traffic at the bottom of the street.
Pierre J. Vanderbecken, Joffrey Dumont Le Brazidec, Alban Farchi, Marc Bocquet, Yelva Roustan, Élise Potier, and Grégoire Broquet
Atmos. Meas. Tech., 16, 1745–1766, https://doi.org/10.5194/amt-16-1745-2023, https://doi.org/10.5194/amt-16-1745-2023, 2023
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Instruments dedicated to monitoring atmospheric gaseous compounds from space will provide images of urban-scale plumes. We discuss here the use of new metrics to compare observed plumes with model predictions that will be less sensitive to meteorology uncertainties. We have evaluated our metrics on diverse plumes and shown that by eliminating some aspects of the discrepancies, they are indeed less sensitive to meteorological variations.
Joffrey Dumont Le Brazidec, Marc Bocquet, Olivier Saunier, and Yelva Roustan
Geosci. Model Dev., 16, 1039–1052, https://doi.org/10.5194/gmd-16-1039-2023, https://doi.org/10.5194/gmd-16-1039-2023, 2023
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When radionuclides are released into the atmosphere, the assessment of the consequences depends on the evaluation of the magnitude and temporal evolution of the release, which can be highly variable as in the case of Fukushima Daiichi.
Here, we propose Bayesian inverse modelling methods and the reversible-jump Markov chain Monte Carlo technique, which allows one to evaluate the temporal variability of the release and to integrate different types of information in the source reconstruction.
Arun Ramanathan, Pierre-Antoine Versini, Daniel Schertzer, Remi Perrin, Lionel Sindt, and Ioulia Tchiguirinskaia
Hydrol. Earth Syst. Sci., 26, 6477–6491, https://doi.org/10.5194/hess-26-6477-2022, https://doi.org/10.5194/hess-26-6477-2022, 2022
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Reference rainfall scenarios are indispensable for hydrological applications such as designing storm-water management infrastructure, including green roofs. Therefore, a new method is suggested for simulating rainfall scenarios of specified intensity, duration, and frequency, with realistic intermittency. Furthermore, novel comparison metrics are proposed to quantify the effectiveness of the presented simulation procedure.
Auguste Gires, Ioulia Tchiguirinskaia, and Daniel Schertzer
Atmos. Meas. Tech., 15, 5861–5875, https://doi.org/10.5194/amt-15-5861-2022, https://doi.org/10.5194/amt-15-5861-2022, 2022
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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. Governing equation linking acceleration, gravity, buoyancy, and drag force is updated to account for oblateness of drops. Then multifractal wind is used as input to explore velocities and trajectories of drops. Finally consequence on radar rainfall estimation is discussed.
Youngseob Kim, Lya Lugon, Alice Maison, Thibaud Sarica, Yelva Roustan, Myrto Valari, Yang Zhang, Michel André, and Karine Sartelet
Geosci. Model Dev., 15, 7371–7396, https://doi.org/10.5194/gmd-15-7371-2022, https://doi.org/10.5194/gmd-15-7371-2022, 2022
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This paper presents the latest version of the street-network model MUNICH, v2.0. The description of MUNICH v1.0, which models gas-phase pollutants in a street network, was published in GMD in 2018. Since then, major modifications have been made to MUNICH. The comprehensive aerosol model SSH-aerosol is now coupled to MUNICH to simulate primary and secondary aerosol concentrations. New parameterisations have also been introduced. Test cases are defined to illustrate the new model functionalities.
Auguste Gires, Jerry Jose, Ioulia Tchiguirinskaia, and Daniel Schertzer
Earth Syst. Sci. Data, 14, 3807–3819, https://doi.org/10.5194/essd-14-3807-2022, https://doi.org/10.5194/essd-14-3807-2022, 2022
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The Hydrology Meteorology and Complexity laboratory of École des Ponts ParisTech (https://hmco.enpc.fr) has made a data set of high-resolution atmospheric measurements (rainfall, wind, temperature, pressure, and humidity) available. It comes from a campaign carried out on a meteorological mast located on a wind farm in the framework of the Rainfall Wind Turbine or Turbulence project (RW-Turb; supported by the French National Research Agency – ANR-19-CE05-0022).
Svetlana Tsyro, Wenche Aas, Augustin Colette, Camilla Andersson, Bertrand Bessagnet, Giancarlo Ciarelli, Florian Couvidat, Kees Cuvelier, Astrid Manders, Kathleen Mar, Mihaela Mircea, Noelia Otero, Maria-Teresa Pay, Valentin Raffort, Yelva Roustan, Mark R. Theobald, Marta G. Vivanco, Hilde Fagerli, Peter Wind, Gino Briganti, Andrea Cappelletti, Massimo D'Isidoro, and Mario Adani
Atmos. Chem. Phys., 22, 7207–7257, https://doi.org/10.5194/acp-22-7207-2022, https://doi.org/10.5194/acp-22-7207-2022, 2022
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Particulate matter (PM) air pollution causes adverse health effects. In Europe, the emissions caused by anthropogenic activities have been reduced in the last decades. To assess the efficiency of emission reductions in improving air quality, we have studied the evolution of PM pollution in Europe. Simulations with six air quality models and observational data indicate a decrease in PM concentrations by 10 % to 30 % across Europe from 2000 to 2010, which is mainly a result of emission reductions.
Joffrey Dumont Le Brazidec, Marc Bocquet, Olivier Saunier, and Yelva Roustan
Atmos. Chem. Phys., 21, 13247–13267, https://doi.org/10.5194/acp-21-13247-2021, https://doi.org/10.5194/acp-21-13247-2021, 2021
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The assessment of the environmental consequences of a radionuclide release depends on the estimation of its source. This paper aims to develop inverse Bayesian methods which combine transport models with measurements, in order to reconstruct the ensemble of possible sources.
Three methods to quantify uncertainties based on the definition of probability distributions and the physical models are proposed and evaluated for the case of 106Ru releases over Europe in 2017.
Yangzi Qiu, Igor da Silva Rocha Paz, Feihu Chen, Pierre-Antoine Versini, Daniel Schertzer, and Ioulia Tchiguirinskaia
Hydrol. Earth Syst. Sci., 25, 3137–3162, https://doi.org/10.5194/hess-25-3137-2021, https://doi.org/10.5194/hess-25-3137-2021, 2021
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Our original research objective is to investigate the uncertainties of the hydrological responses of nature-based solutions (NBSs) that result from the multiscale space variability in both the rainfall and the NBS distribution. Results show that the intersection effects of spatial variability in rainfall and the spatial arrangement of NBS can generate uncertainties of peak flow and total runoff volume estimations in NBS scenarios.
Pierre-Antoine Versini, Filip Stanic, Auguste Gires, Daniel Schertzer, and Ioulia Tchiguirinskaia
Earth Syst. Sci. Data, 12, 1025–1035, https://doi.org/10.5194/essd-12-1025-2020, https://doi.org/10.5194/essd-12-1025-2020, 2020
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The Blue Green Wave of Champs-sur-Marne (1 ha, France) has been converted into a full-scale monitoring site devoted to studying the uses of green infrastructure in storm-water management. For this purpose, the components of the water balance have been monitored: rainfall, water content in the substrate, and discharge. These measurements are useful to better understand the processes (infiltration and retention) in hydrological performance and spatial variability.
Auguste Gires, Philippe Bruley, Anne Ruas, Daniel Schertzer, and Ioulia Tchiguirinskaia
Earth Syst. Sci. Data, 12, 835–845, https://doi.org/10.5194/essd-12-835-2020, https://doi.org/10.5194/essd-12-835-2020, 2020
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The Hydrology, Meteorology and Complexity Laboratory of École des Ponts ParisTech (hmco.enpc.fr) and the Sense-City consortium (http://sense-city.ifsttar.fr/) make available a dataset of optical disdrometer measurements stemming from a campaign that took place in September 2017 under the rainfall simulator of the Sense-City climatic chamber, which is located near Paris.
Auguste Gires, Ioulia Tchiguirinskaia, and Daniel Schertzer
Nonlin. Processes Geophys., 27, 133–145, https://doi.org/10.5194/npg-27-133-2020, https://doi.org/10.5194/npg-27-133-2020, 2020
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This paper aims to analyse and simulate correlations between two fields in a scale-invariant framework. It starts by theoretically assessing and numerically confirming the behaviour of renormalized multiplicative power law combinations of two fields with known scale-invariant properties. Then a new indicator of correlation is suggested and tested on rainfall data to study the correlation between the common rain rate and drop size distribution features.
Rosa Vicari, Ioulia Tchiguirinskaia, Bruno Tisserand, and Daniel Schertzer
Nat. Hazards Earth Syst. Sci., 19, 1485–1498, https://doi.org/10.5194/nhess-19-1485-2019, https://doi.org/10.5194/nhess-19-1485-2019, 2019
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Today, when extreme weather affects an urban area, huge numbers of digital data are spontaneously produced by the population on the Web. These
digital trailscan provide insight into the relation between climate-related risks and the social perception of these risks. The experiments presented in this paper show that big data exploration techniques can amplify debated issues and actors and explore how social media users behave.
Yangzi Qiu, Abdellah Ichiba, Igor Da Silva Rocha Paz, Feihu Chen, Pierre-Antoine Versini, Daniel Schertzer, and Ioulia Tchiguirinskaia
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-347, https://doi.org/10.5194/hess-2019-347, 2019
Manuscript not accepted for further review
Rosa Vicari, Ioulia Tchiguirinskaia, and Daniel Schertzer
Geosci. Commun., 2, 25–38, https://doi.org/10.5194/gc-2-25-2019, https://doi.org/10.5194/gc-2-25-2019, 2019
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The resilience of our cities to climate risks relies on the capacity of urban communities to communicate. This paper presents a study aimed at understanding how to assess the impact of public outreach campaigns on urban resilience. The paper reviews resilience assessment methods, highlights those frameworks that consider communication impacts, and presents a range of experiments aimed at testing novel
resilience communication indicators.
Auguste Gires, Ioulia Tchiguirinskaia, and Daniel Schertzer
Earth Syst. Sci. Data, 10, 941–950, https://doi.org/10.5194/essd-10-941-2018, https://doi.org/10.5194/essd-10-941-2018, 2018
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The Hydrology, Meteorology, and Complexity laboratory of École des Ponts ParisTech (hmco.enpc.fr) has made a data set of optical disdrometer measurements available that come from a campaign involving three collocated devices from two different manufacturers, relying on different underlying technologies (one Campbell Scientific PWS100 and two OTT Parsivel2 instruments). The campaign took place in January–February 2016 in the Paris area (France).
Youngseob Kim, You Wu, Christian Seigneur, and Yelva Roustan
Geosci. Model Dev., 11, 611–629, https://doi.org/10.5194/gmd-11-611-2018, https://doi.org/10.5194/gmd-11-611-2018, 2018
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A new multi-scale model of urban air pollution is presented. This model combines a regional chemical transport model (CTM) with spatial scales down to 1 km and a street-network model. The street-network model MUNICH is coupled to the Polair3D CTM to constitute the Street-in-Grid (SinG) model. SinG and MUNICH are used to simulate the concentrations of NOx and ozone in a Paris suburb. SinG shows better performance than MUNICH for NO2 measured at monitoring stations within a street canyon.
Abdellah Ichiba, Auguste Gires, Ioulia Tchiguirinskaia, Daniel Schertzer, Philippe Bompard, and Marie-Claire Ten Veldhuis
Hydrol. Earth Syst. Sci., 22, 331–350, https://doi.org/10.5194/hess-22-331-2018, https://doi.org/10.5194/hess-22-331-2018, 2018
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This paper proposes a two-step investigation to illustrate the extent of scale effects in urban hydrology. First, fractal tools are used to highlight the scale dependency observed within GIS data inputted in urban hydrological models. Then an intensive multi-scale modelling work was carried out to confirm effects on model performances. The model was implemented at 17 spatial resolutions ranging from 100 to 5 m. Results allow the understanding of scale challenges in hydrology modelling.
Sébastien Ars, Grégoire Broquet, Camille Yver Kwok, Yelva Roustan, Lin Wu, Emmanuel Arzoumanian, and Philippe Bousquet
Atmos. Meas. Tech., 10, 5017–5037, https://doi.org/10.5194/amt-10-5017-2017, https://doi.org/10.5194/amt-10-5017-2017, 2017
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This study presents a new concept for estimating the pollutant emission rates of a site combining the tracer release method, local-scale atmospheric transport modelling and a statistical atmospheric inversion approach. The potential of this new concept is evaluated with a practical implementation based on a series of inversions of controlled methane and tracer point sources in different spatial configurations to assess the efficiency of the method in comparison with the classic tracer method.
Daniel Wolfensberger, Auguste Gires, Ioulia Tchiguirinskaia, Daniel Schertzer, and Alexis Berne
Atmos. Chem. Phys., 17, 14253–14273, https://doi.org/10.5194/acp-17-14253-2017, https://doi.org/10.5194/acp-17-14253-2017, 2017
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Precipitation intensities simulated by the COSMO weather prediction model are compared to radar observations over a range of spatial and temporal scales using the universal multifractal framework. Our results highlight the strong influence of meteorological and topographical features on the multifractal characteristics of precipitation. Moreover, the influence of the subgrid parameterizations of COSMO is clearly visible by a break in the scaling properties that is absent from the radar data.
Augustin Colette, Camilla Andersson, Astrid Manders, Kathleen Mar, Mihaela Mircea, Maria-Teresa Pay, Valentin Raffort, Svetlana Tsyro, Cornelius Cuvelier, Mario Adani, Bertrand Bessagnet, Robert Bergström, Gino Briganti, Tim Butler, Andrea Cappelletti, Florian Couvidat, Massimo D'Isidoro, Thierno Doumbia, Hilde Fagerli, Claire Granier, Chris Heyes, Zig Klimont, Narendra Ojha, Noelia Otero, Martijn Schaap, Katarina Sindelarova, Annemiek I. Stegehuis, Yelva Roustan, Robert Vautard, Erik van Meijgaard, Marta Garcia Vivanco, and Peter Wind
Geosci. Model Dev., 10, 3255–3276, https://doi.org/10.5194/gmd-10-3255-2017, https://doi.org/10.5194/gmd-10-3255-2017, 2017
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The EURODELTA-Trends numerical experiment has been designed to assess the capability of chemistry-transport models to capture the evolution of surface air quality over the 1990–2010 period in Europe. It also includes sensitivity experiments in order to analyse the relative contribution of (i) emission changes, (ii) meteorological variability, and (iii) boundary conditions to air quality trends. The article is a detailed presentation of the experiment design and participating models.
Auguste Gires, Ioulia Tchiguirinskaia, Daniel Schertzer, Susana Ochoa-Rodriguez, Patrick Willems, Abdellah Ichiba, Li-Pen Wang, Rui Pina, Johan Van Assel, Guendalina Bruni, Damian Murla Tuyls, and Marie-Claire ten Veldhuis
Hydrol. Earth Syst. Sci., 21, 2361–2375, https://doi.org/10.5194/hess-21-2361-2017, https://doi.org/10.5194/hess-21-2361-2017, 2017
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Data from 10 urban or peri-urban catchments located in five EU countries are used to analyze the imperviousness distribution and sewer network geometry. Consistent scale invariant features are retrieved for both (fractal dimensions can be defined), which enables to define a level of urbanization. Imperviousness representation in operational model is also found to exhibit scale-invariant features (even multifractality). The research was carried out as part of the UE INTERREG IV RainGain project.
Auguste Gires, Catherine L. Muller, Marie-Agathe le Gueut, and Daniel Schertzer
Hydrol. Earth Syst. Sci., 20, 1751–1763, https://doi.org/10.5194/hess-20-1751-2016, https://doi.org/10.5194/hess-20-1751-2016, 2016
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Educational activities are now a common channel to increase impact of research projects. Here, we present innovative activities for young children that aim to help them (and their teachers) grasp some of the complex underlying scientific issues in environmental fields. The activities developed are focused on rainfall: observation and modeling of rain drop size and the succession of dry and rainy days, and writing of a scientific book. All activities were implemented in classrooms.
N. Cherin, Y. Roustan, L. Musson-Genon, and C. Seigneur
Geosci. Model Dev., 8, 893–910, https://doi.org/10.5194/gmd-8-893-2015, https://doi.org/10.5194/gmd-8-893-2015, 2015
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Atmospheric dry deposition is classically modelled using an average roughness
length. This approach cannot account for the spatial variability of dry deposition in urban areas. We extend here the urban canyon concept, previously introduced to parametrise momentum and heat transfer to mass transfer. This approach provides spatially distributed dry deposition fluxes that depend on surfaces (streets, walls, roofs) and flow regimes (recirculation and ventilation) within the urban area.
S. Lovejoy, D. Schertzer, and D. Varon
Earth Syst. Dynam., 4, 439–454, https://doi.org/10.5194/esd-4-439-2013, https://doi.org/10.5194/esd-4-439-2013, 2013
M. R. Koohkan, M. Bocquet, Y. Roustan, Y. Kim, and C. Seigneur
Atmos. Chem. Phys., 13, 5887–5905, https://doi.org/10.5194/acp-13-5887-2013, https://doi.org/10.5194/acp-13-5887-2013, 2013
A. Gires, I. Tchiguirinskaia, D. Schertzer, and S. Lovejoy
Nonlin. Processes Geophys., 20, 343–356, https://doi.org/10.5194/npg-20-343-2013, https://doi.org/10.5194/npg-20-343-2013, 2013
Related subject area
Subject: Scaling, multifractals, turbulence, complex systems, self-organized criticality | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Theory
Multifractal analysis of wind turbine power and rainfall from an operational wind farm – Part 1: Wind turbine power and the associated biases
A global analysis of the fractal properties of clouds revealing anisotropy of turbulence across scales
Stieltjes functions and spectral analysis in the physics of sea ice
Review article: Scaling, dynamical regimes, and stratification. How long does weather last? How big is a cloud?
Brief communication: Climate science as a social process – history, climatic determinism, Mertonian norms and post-normality
Characteristics of intrinsic non-stationarity and its effect on eddy-covariance measurements of CO2 fluxes
How many modes are needed to predict climate bifurcations? Lessons from an experiment
Non-linear hydrologic organization
The impact of entrained air on ocean waves
Approximate multifractal correlation and products of universal multifractal fields, with application to rainfall data
Jerry Jose, Auguste Gires, Yelva Roustan, Ernani Schnorenberger, Ioulia Tchiguirinskaia, and Daniel Schertzer
Nonlin. Processes Geophys., 31, 587–602, https://doi.org/10.5194/npg-31-587-2024, https://doi.org/10.5194/npg-31-587-2024, 2024
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Wind energy exhibits extreme variability in space and time. However, it also shows scaling properties (properties that remain similar across different times and spaces of measurement). This can be quantified using appropriate statistical tools. In this way, the scaling properties of power from a wind farm are analysed here. Since every turbine is manufactured by design for a rated power, this acts as an upper limit on the data. This bias is identified here using data and numerical simulations.
Karlie N. Rees, Timothy J. Garrett, Thomas D. DeWitt, Corey Bois, Steven K. Krueger, and Jérôme C. Riedi
Nonlin. Processes Geophys., 31, 497–513, https://doi.org/10.5194/npg-31-497-2024, https://doi.org/10.5194/npg-31-497-2024, 2024
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The shapes of clouds viewed from space reflect vertical and horizontal motions in the atmosphere. We theorize that, globally, cloud perimeter complexity is related to the dimension of turbulence also governed by horizontal and vertical motions. We find agreement between theory and observations from various satellites and a numerical model and, remarkably, that the theory applies globally using only basic planetary physical parameters from the smallest scales of turbulence to the planetary scale.
Kenneth M. Golden, N. Benjamin Murphy, Daniel Hallman, and Elena Cherkaev
Nonlin. Processes Geophys., 30, 527–552, https://doi.org/10.5194/npg-30-527-2023, https://doi.org/10.5194/npg-30-527-2023, 2023
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Our paper tours powerful methods of finding the effective behavior of complex systems, which can be applied well beyond the initial setting of sea ice. Applications include transport properties of porous and polycrystalline media, such as rocks and glacial ice, and advection diffusion processes that arise throughout geophysics. Connections to random matrix theory establish unexpected parallels of these geophysical problems with semiconductor physics and Anderson localization phenomena.
Shaun Lovejoy
Nonlin. Processes Geophys., 30, 311–374, https://doi.org/10.5194/npg-30-311-2023, https://doi.org/10.5194/npg-30-311-2023, 2023
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How big is a cloud?and
How long does the weather last?require scaling to answer. We review the advances in scaling that have occurred over the last 4 decades: (a) intermittency (multifractality) and (b) stratified and rotating scaling notions (generalized scale invariance). Although scaling theory and the data are now voluminous, atmospheric phenomena are too often viewed through an outdated scalebound lens, and turbulence remains confined to isotropic theories of little relevance.
Hans von Storch
Nonlin. Processes Geophys., 30, 31–36, https://doi.org/10.5194/npg-30-31-2023, https://doi.org/10.5194/npg-30-31-2023, 2023
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Climate science is, as all sciences, a social process and as such conditioned by the zeitgeist of the time. It has an old history and has attained different political significances. Today, it is the challenge of anthropogenic climate change – and societies want answers about how to deal with it. In earlier times, it was mostly the ideology of climate determinism which led people to construct superiority and eventually colonialism.
Lei Liu, Yu Shi, and Fei Hu
Nonlin. Processes Geophys., 29, 123–131, https://doi.org/10.5194/npg-29-123-2022, https://doi.org/10.5194/npg-29-123-2022, 2022
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We find a new kind of non-stationarity. This new kind of non-stationarity is caused by the intrinsic randomness. Results show that the new kind of non-stationarity is widespread in small-scale variations of CO2 turbulent fluxes. This finding reminds us that we need to handle the short-term averaged turbulent fluxes carefully, and we also need to re-screen the existing non-stationarity diagnosis methods because they could make a wrong diagnosis due to this new kind of non-stationarity.
Bérengère Dubrulle, François Daviaud, Davide Faranda, Louis Marié, and Brice Saint-Michel
Nonlin. Processes Geophys., 29, 17–35, https://doi.org/10.5194/npg-29-17-2022, https://doi.org/10.5194/npg-29-17-2022, 2022
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Present climate models discuss climate change but show no sign of bifurcation in the future. Is this because there is none or because they are in essence too simplified to be able to capture them? To get elements of an answer, we ran a laboratory experiment and discovered that the answer is not so simple.
Allen Hunt, Boris Faybishenko, and Behzad Ghanbarian
Nonlin. Processes Geophys., 28, 599–614, https://doi.org/10.5194/npg-28-599-2021, https://doi.org/10.5194/npg-28-599-2021, 2021
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The same power law we previously used to quantify growth of tree roots in time describes equally the assemblage of river networks in time. Even the basic length scale of both networks is the same. The one difference is that the basic time scale is ca. 10 times shorter for drainage networks than for tree roots, since the relevant flow rate is 10 times faster. This result overturns the understanding of drainage networks and forms a basis to organize thoughts about surface and subsurface hydrology.
Juan M. Restrepo, Alex Ayet, and Luigi Cavaleri
Nonlin. Processes Geophys., 28, 285–293, https://doi.org/10.5194/npg-28-285-2021, https://doi.org/10.5194/npg-28-285-2021, 2021
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A homogenization of Navier–Stokes to wave scales allows us to determine that air bubbles suspended near the ocean surface modify the momentum equation, specifically enhancing the vorticity in the flow. A model was derived that relates the rain rate to the production of air bubbles near the ocean surface. At wave scales, the air bubbles enhance the wave dissipation for small gravity or capillary waves.
Auguste Gires, Ioulia Tchiguirinskaia, and Daniel Schertzer
Nonlin. Processes Geophys., 27, 133–145, https://doi.org/10.5194/npg-27-133-2020, https://doi.org/10.5194/npg-27-133-2020, 2020
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This paper aims to analyse and simulate correlations between two fields in a scale-invariant framework. It starts by theoretically assessing and numerically confirming the behaviour of renormalized multiplicative power law combinations of two fields with known scale-invariant properties. Then a new indicator of correlation is suggested and tested on rainfall data to study the correlation between the common rain rate and drop size distribution features.
Cited articles
Al, B., C., C. K., and Hann, D.: Effect of Rain on Vertical Axis Wind Turbines, in: Proceedings of the International Conference on Renewable Energies and Power Quality, Las Palmas de Gran Canaria (Spain), 13 to 15 April 2011, https://doi.org/10.24084/repqj09.618, 1986. a
Bringi, V. N., Chandrasekar, V., Hubbert, J., Gorgucci, E., Randeu, W. L., and Schoenhuber, M.: Raindrop Size Distribution in Different Climatic Regimes from Disdrometer and Dual-Polarized Radar Analysis, J. Atmos. Sci., 60, 354–365, https://doi.org/10.1175/1520-0469(2003)060<0354:RSDIDC>2.0.CO;2, 2003. a
Cai, M., Abbasi, E., and Arastoopour, H.: Analysis of the Performance of a Wind-Turbine Airfoil under Heavy-Rain Conditions Using a Multiphase Computational Fluid Dynamics Approach, Ind. Eng. Chem. Res., 52, 3266–3275, https://doi.org/10.1021/ie300877t, 2013. a
Cai, Y. and Bréon, F.-M.: Wind power potential and intermittency issues in the context of climate change, Energ. Convers. Manage., 240, 114276, https://doi.org/10.1016/j.enconman.2021.114276, 2021. a
Calif, R. and Schmitt, F. G.: Multiscaling and joint multiscaling description of the atmospheric wind speed and the aggregate power output from a wind farm, Nonlin. Processes Geophys., 21, 379–392, https://doi.org/10.5194/npg-21-379-2014, 2014. a, b
Cohan, A. C. and Arastoopour, H.: Numerical simulation and analysis of the effect of rain and surface property on wind-turbine airfoil performance, Int. J. Multiphas. Flow, 81, 46–53, https://doi.org/10.1016/j.ijmultiphaseflow.2016.01.006, 2016. a
Corrigan, R. and Demiglio, R.: Effect of Precipitation on Wind Turbine Performance, NASA TM-86986, https://ntrs.nasa.gov/api/citations/19850019074/downloads/19850019074.pdf (last access: 26 November 2024), 1985. a
Corten, G. P. and Veldkamp, H. F.: Insects can halve wind-turbine power, Nature, 412, 41–42, https://doi.org/10.1038/35083698, 2001. a
Engie, S.: Offshore wind power is on the rise in France, https://www.engie.com/en/news/offshore-wind-france (last access: 26 November 2024), 2022. a
EWEA: Wind energy's frequently asked questions (FAQ), https://www.ewea.org/wind-energy-basics/faq/ (last access: 26 November 2024), 2012. a
Fitton, G.: Multifractal analysis and simulation of wind energy fluctuations, Theses, Université Paris-Est, https://pastel.archives-ouvertes.fr/tel-00962318 (last access: 26 November 2024), 2013. a
Fitton, G., Tchiguirinskaia, I., Schertzer, D., and Lovejoy, S.: Scaling Of Turbulence In The Atmospheric Surface-Layer: Which Anisotropy?, J. Phys. Conf. Ser., 318, 072008, https://doi.org/10.1088/1742-6596/318/7/072008, 2011. a
Fitton, G., Tchiguirinskaia, I., Schertzer, D., and Lovejoy, S.: Torque Fluctuations In The Framework Of A Multifractal 23/9-Dimensional Turbulence Model, J. Phys. Conf. Ser., 555, 012038, https://doi.org/10.1088/1742-6596/555/1/012038, 2014. a
García Gago, Á., Gires, A., Veers, P., Schertzer, D., and Tchiguirinskaia, I.: Transfer of small scales space-time fluctuations of wind fields to wind turbines torque computation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10583, https://doi.org/10.5194/egusphere-egu22-10583, 2022. a
Gires, A., Tchiguirinskaia, I., and Schertzer, D.: Approximate multifractal correlation and products of universal multifractal fields, with application to rainfall data, Nonlin. Processes Geophys., 27, 133–145, https://doi.org/10.5194/npg-27-133-2020, 2020a. a, b, c, d
Gires, A., Tchiguirinskaia, I., and Schertzer, D.: Data for “Approximate multifractal correlation and products of universal multifractal fields, with application to rainfall data” by Auguste Gires, Ioulia Tchiguirinskaia, and Daniel Schertzer, NPG 2020, Zenodo [data set], https://doi.org/10.5281/zenodo.3707904, 2020b. a, b
Gires, A., Jose, J., Tchiguirinskaia, I., and Schertzer, D.: Data for “Three months of combined high resolution rainfall and wind data collected on a wind farm”, Zenodo [data set], https://doi.org/10.5281/zenodo.5801900, 2021. a
Houze Jr., R. A.: Cloud dynamics, Academic press, 2014. a
IEA: Global Energy Review 2020, IEA, Paris https://www.iea.org/reports/global-energy-review-2020 (last access: 26 November 2024), 2020. a
IRENA: 10 Years, Progress to Action, International Renewable Energy Agency, https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2020/Jan/IRENA_10_years_2020.pdf (last access: 26 November 2024), 2020. a
Johnson, K. E.: Adaptive torque control of variable speed wind turbines, Ph.D. thesis, University of Colorado, Boulder, https://www.proquest.com/openview/d225ccedb9ce600bfbdeac5f1790b121/1?pqorigsite=gscholar&cbl=18750&diss=y (last access: 26 November 2024), 2004. a
Jørgensen, B. H. and Holttinen, H.: IEA Wind TCP Annual Report 2021, IEA, https://ieawind.org/wp-content/uploads/2022/12/IEA_Wind_TCP_Annual_Report_2021.pdf (last access: 26 November 2024), 2022. a
Jose, J., Gires, A., Tchiguirinskaia, I., Roustan, Y., and Schertzer, D.: Scale invariant relationship between rainfall kinetic energy and intensity in Paris region: An evaluation using universal multifractal framework, J. Hydrol., 609, 127715, https://doi.org/10.1016/j.jhydrol.2022.127715, 2022. a
Jose, J., Gires, A., Roustan, Y., Schnorenberger, E., Tchiguirinskaia, I., and Schertzer, D.: Multifractal analysis of wind turbine power and rainfall from an operational wind farm – Part 1: Wind turbine power and the associated biases, Nonlin. Processes Geophys., 31, 587–602, https://doi.org/10.5194/npg-31-587-2024, 2024. a, b
Jung, C. and Schindler, D.: The role of air density in wind energy assessment – A case study from Germany, Energy, 171, 385–392, https://doi.org/10.1016/j.energy.2019.01.041, 2019. a
Keegan, M. H., Nash, D. H., and Stack, M. M.: On erosion issues associated with the leading edge of wind turbine blades, J. Phys. D Appl. Phys., 46, 383001, https://doi.org/10.1088/0022-3727/46/38/383001, 2013. a
Lavallée, D., Lovejoy, S., Schertzer, D., and Ladoy, P.: Nonlinear variability and landscape topography: analysis and simulation, Fractals in Geography, edited by: De Cola, L. and Lam, N., PTR, Prentice Hall, 158–192, https://www.physics.mcgill.ca/~gang/eprints/eprintLovejoy/neweprint/topoall.pdf (last access: 26 November 2024), 1993. a
Mandelbrot, B. B.: The fractal geometry of nature, vol. 1, WH Freeman New York, ISBN 0-7167-1186-9, 1982. a
Manwell, J. F., McGowan, J. G., and Rogers, A. L.: Wind energy explained: theory, design and application, John Wiley & Sons, ISBN 978-0-470-01500-1 (Hbk), 2010. a
Marzano, F. S., Cimini, D., and Montopoli, M.: Investigating precipitation microphysics using ground-based microwave remote sensors and disdrometer data, Atmos. Res., 97, 583–600, https://doi.org/10.1016/j.atmosres.2010.03.019, 2010. a
Meneveau, C., Sreenivasan, K. R., Kailasnath, P., and Fan, M. S.: Joint multifractal measures: Theory and applications to turbulence, Phys. Rev. A, 41, 894–913, https://doi.org/10.1103/PhysRevA.41.894, 1990. a, b
Obligado, M., Cal, R. B., and Brun, C.: Wind turbine wake influence on the mixing of relative humidity quantified through wind tunnel experiments, J. Renew. Sustain. Ener., 13, 023308, https://doi.org/10.1063/5.0039090, 2021. a
Parisi, G. and Frisch, U.: On the singularity structure of fully developed turbulence, in: Turbulence and Predictability in Geophysical Fluid Dynamics and Climate Dynamics, North-Holland Publisher, Amsterdam, The Netherlands, 84–87, 1985. a
Picard, A., Davis, R. S., Gläser, M., and Fujii, K.: Revised formula for the density of moist air (CIPM-2007), Metrologia, 45, 149, https://doi.org/10.1088/0026-1394/45/2/004, 2008. a
Schertzer, D. and Lovejoy, S.: The dimension and intermittency of atmospheric dynamics, in: Turbulent Shear Flows 4: Selected Papers from the Fourth International Symposium on Turbulent Shear Flows, University of Karlsruhe, Karlsruhe, FRG, 12–14 September, Springer Berlin Heidelberg, 7–33, https://doi.org/10.1007/978-3-642-69996-2_2, 1983. a
Schertzer, D. and Lovejoy, S.: Multifractal simulations and analysis of clouds by multiplicative processes, Atmos. Res., 21, 337–361, https://doi.org/10.1016/0169-8095(88)90035-X, 1988. a, b, c, d
Schertzer, D. and Lovejoy, S.: Nonlinear Variability in Geophysics: Multifractal Simulations and Analysis, Springer US, Boston, MA, 49–79, ISBN 978-1-4899-3499-4, https://doi.org/10.1007/978-1-4899-3499-4_3, 1989. a
Schertzer, D. and Tchiguirinskaia, I.: A Century of Turbulent Cascades and the Emergence of Multifractal Operators, Earth and Space Science, 7, e2019EA000608, https://doi.org/10.1029/2019EA000608, 2020. a, b
Seuront, L. and Schmitt, F. G.: Multiscaling statistical procedures for the exploration of biophysical couplings in intermittent turbulence. Part I. Theory, Deep-Sea Res. Pt. II, 52, 1308–1324, https://doi.org/10.1016/j.dsr2.2005.01.006, 2005a. a
Seuront, L. and Schmitt, F. G.: Multiscaling statistical procedures for the exploration of biophysical couplings in intermittent turbulence. Part II. Applications, Deep-Sea Res. Pt. II, 52, 1325–1343, https://doi.org/10.1016/j.dsr2.2005.01.005, 2005b. a
Smith, S. E., Travis, K. N., Djeridi, H., Obligado, M., and Cal, R. B.: Dynamic effects of inertial particles on the wake recovery of a model wind turbine, Renew. Energ., 164, 346–361, https://doi.org/10.1016/j.renene.2020.09.037, 2021. a
Tessier, Y., Lovejoy, S., and Schertzer, D.: Universal Multifractals: Theory and Observations for Rain and Clouds, J. Appl. Meteorol. Clim., 32, 223–250, https://doi.org/10.1175/1520-0450(1993)032<0223:UMTAOF>2.0.CO;2, 1993. a
Tokay, A. and Short, D. A.: Evidence from Tropical Raindrop Spectra of the Origin of Rain from Stratiform versus Convective Clouds, J. Appl. Meteorol., 35, 355–371, https://doi.org/10.1175/1520-0450(1996)035<0355:EFTRSO>2.0.CO;2, 1996. a, b, c, d
Ulazia, A., Gonzalez-Rojí, S. J., Ibarra-Berastegi, G., Carreno-Madinabeitia, S., Sáenz, J., and Nafarrate, A.: Seasonal Air Density Variations over the East of Scotland and The Consequences for Offshore Wind Energy, in: Proceedings of the 2018 7th International Conference on Renewable Energy Research and Applications (ICRERA), Paris, France, 14–17 October 2018, 261–265, https://ieeexplore.ieee.org/document/8566716 (last access: 26 November 2026), 2018. a
UN: UN-Energy plan of action towards 2025, https://un-energy.org/wp-content/uploads/2022/05/UN-Energy-Plan-of-Action-towards-2025-2May2022.pdf (last access: 26 November 2024), 2022. a
Vestas Wind Systems A/S, V90: V90-2.0 MW™ IEC IIA/IEC S turbines, https://www.vestas.com/en/products/2-mw-platform/V90-2-0-MW (last access: 26 November 2024), 2023. a
Short summary
To understand the influence of rainfall on wind power production, turbine power and rainfall were measured simultaneously on an operational wind farm and analysed. The correlation between wind, wind power, air density, and other fields was obtained on various temporal scales under rainy and dry conditions. An increase in the correlation was observed with an increase in the rain; rain also influenced the correspondence between actual and expected values of power at various velocities.
To understand the influence of rainfall on wind power production, turbine power and rainfall...