Articles | Volume 27, issue 1
https://doi.org/10.5194/npg-27-133-2020
https://doi.org/10.5194/npg-27-133-2020
Research article
 | 
19 Mar 2020
Research article |  | 19 Mar 2020

Approximate multifractal correlation and products of universal multifractal fields, with application to rainfall data

Auguste Gires, Ioulia Tchiguirinskaia, and Daniel Schertzer

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Part 1: Multifractal analysis of wind turbine power and the associated biases
Jerry Jose, Auguste Gires, Yelva Roustan, Ernani Schnorenberger, Ioulia Tchiguirinskaia, and Daniel Schertzer
Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npg-2024-5,https://doi.org/10.5194/npg-2024-5, 2024
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Part 2: Joint multifractal analysis of available wind power and rain intensity from an operational wind farm
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3D trajectories and velocities of rainfall drops in a multifractal turbulent wind field
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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Combined high-resolution rainfall and wind data collected for 3 months on a wind farm 110 km southeast of Paris (France)
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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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Measurements of the water balance components of a large green roof in the greater Paris area
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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Subject: Scaling, multifractals, turbulence, complex systems, self-organized criticality | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Theory
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Part 1: Multifractal analysis of wind turbine power and the associated biases
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Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npg-2024-5,https://doi.org/10.5194/npg-2024-5, 2024
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Cited articles

Battaglia, A., Rustemeier, E., Tokay, A., Blahak, U., and Simmer, C.: PARSIVEL Snow Observations: A Critical Assessment, J. Atmos. Ocean. Tech., 27, 333–344, https://doi.org/10.1175/2009JTECHA1332.1, 2010. a
Bertol, I., Schick, J., Bandeira, D. H., Paz-Ferreiro, J., and Vázquez, E. V.: Multifractal and joint multifractal analysis of water and soil losses from erosion plots: A case study under subtropical conditions in Santa Catarina highlands, Brazil, Geoderma, 287, 116–125, https://doi.org/10.1016/j.geoderma.2016.08.008, 2017. a, b
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, Nonlinear Process. Geophys., 21, 379–392, https://doi.org/10.5194/npg-21-379-2014, 2014. a, b
Douglas, E. M. and Barros, A. P.: Probable maximum precipitation estimation using multifractals: Application in the eastern United States, J. Hydrometeorol., 4, 1012–1024, 2003. a
Gires, A., Tchiguirinskaia, I., and Schertzer, D.: Multifractal comparison of the outputs of two optical disdrometers, Hydrol. Sci. J., 61, 1641–1651, https://doi.org/10.1080/02626667.2015.1055270, 2016. a, b
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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.