Orals: Thu, 1 May | Room -2.32
Extreme events have a significant impact on nature, industry, agriculture, and society as a whole. From life-threatening heat waves and spring frosts that devastate crops in orchards and vineyards to other extremes such as epileptic seizures or financial market crashes, these phenomena remain a focus of intense scientific investigation.
The identification of causal relationships, specifically distinguishing cause from effect, is a rapidly advancing area of scientific research. Experts from various disciplines, including mathematics, physics, computer science, and others, are developing computational methods and algorithms to uncover causal links from experimental data.
Despite growing interest in these scientific fields, surprisingly few research teams integrate the study of causality with the analysis of extreme phenomena. Building on the information-theoretic generalization of Granger causality, Paluš et al. (2024) propose Rényi information transfer as a method for determining which of two or more potential causal variables gives rise to extreme values in an effect variable. Their study identifies the Siberian High as a key driver of increased probabilities of cold extremes in winter and spring surface air temperatures in Europe, while the North Atlantic Oscillation and blocking events are shown to induce shifts in the entire temperature probability distribution.
In this contribution we will employ Rényi information transfer to investigate the underlying causes of heat waves or warm extremes in summer surface air temperature in Europe. We will highlight the role of blocking events and examine the contribution of other relevant circulation phenomena, accounting for varying spatial and temporal scales as well as non-Gaussian probability distributions.
This research was supported by the Johannes Amos Comenius Programme (P JAC), project No. CZ.02.01.01/00/22_008/0004605, Natural and anthropogenic georisks, and by the Czech Academy of Sciences, Praemium Academiae awarded to M. Paluš.
Paluš, M., Chvosteková, M., & Manshour, P. (2024). Causes of extreme events revealed by Rényi information transfer. Science Advances, 10(30), eadn1721.
How to cite: Paluš, M.: Behind extreme variability: Unveiling causes using information theory beyond Shannon, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14307, https://doi.org/10.5194/egusphere-egu25-14307, 2025.
Climate network (CN) analysis has demonstrated significant potential and is widely applied in climate research. However, revealing the underlying mechanisms behind the results obtained from CN analysis remains challenging. One possible reason for this difficulty lies in the method used to determine the links between nodes in the climate network. The commonly used Pearson correlation analysis may not be able to fully capture the complex dynamics of the climate system. In particular, the multi-scale interactions among multiple processes may induce scaling behaviors in the climate system, which further lead to long-term climate memory. The presence of such memory may influence CN analysis outcomes. In this work, we aim to identify the climate memory impacts on the CN analysis. Combining with the Fractional Integral Statistical Model (FISM), we proposed a new approach named as CN-FISM. The FISM model allows for the extraction of the climate memory component, enabling the modification of time series to preserve a specified length of memory. By conducting CN analysis on these adjusted series, one thus can quantify the impacts of climate memory. This approach has been successfully employed to a recent CN analysis on the Pacific Decadal Oscillation (PDO) phase change. Compared with the current Pearson correlation-based CN approach, the CN-FISM may enhance the interpretability of CN results.
How to cite: Yuan, N. and Wei, Z.: Identifying climate memory impacts on climate network analysis using fractional integral techniques, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15394, https://doi.org/10.5194/egusphere-egu25-15394, 2025.
In 1870, Prof. Paey, President of the Anthropological Society of Cuba, underlined that no one can ignore that studying clouds is one of the most practical needs of meteorology (1). More than 150 years later, the long-term stability of the Earth's atmosphere and climate (2) is recognized as sensitive to cloud dynamics (3), especially cloud thinning, relating it directly to climate change (4). The critical conclusion, documented in numerous studies (5), is that climate change is also a health crisis (6). The general panorama and the need to classify the clouds (7) to create a reliable Library for Machine Learning. Graph Geometric Algebra networks for graph representation learning (8) can become the decisive moment for cloud studies and modeling passing from classification to physics-informed Turing-like patterns recognition inside the diurnal variations of clouds and corresponding humidity profiles of the atmosphere. Multifractal and p-adic forecasting (9) of Big Data patterning is envisaged as the New Science of Complexity based on the physics of atmosphere, clouds, and climate (10, 11). Based on the physics-informed approach, we focus on original numbers systems and their multiscale pattering, fusing, and unifying Big Geo Data inside the probability-embedded medium, introducing the new methodology for Turning-type patterning quantifier of cloud system multiscale structure complexity extracted from physics-informed and statistics-informed raw data and images with moving space-temporal boundaries. Muuk'il Kaab (MIK) agile, bio-inspired (bees-type) software was designed and calibrated multiscale images from smartphones to high-precision photo cameras on clouds. This contribution shows more than ten years of testing as a new Metacomplexity Universal Quantitative Attribute (MCUQA) for complex pattern recognition, measurement, multiscale visualization, and skeletonization. Our research aims to optimize the fusion of multidimensional multiphysical raw data sets by the same nature-inspired bee-type software through data visualization, image analytics, virtualization, and the unification and forecasting of physics-informed measures with number theory.
Keywords: Big Data; data fusion; algebra of images; physics-informed 3D signals visualization; networks images geometrization; Complexity quantitative attributes; thermodynamic, multifractal, and p-adic forecasting.
References:
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- Henderson-Sellers, A. Clouds and the long-term stability of the Earth's atmosphere and climate. Nature, 1979, 279260786-260788.
- Bony, S., Stevens, B., Frierson; D.M.W., Jakob, Ch., Kageyama, M., Pincus, R., Shepherd; T.G., Sherwood, S.C., Siebesma, A.P., Sobel, A.,M. and Webb, M. Clouds, circulation and climate sensitivity. Nature Geoscience, 2015, 261- 268.
- Sokol, A., Wall, C., & Hartmann, D.L. Greater climate sensitivity implied by anvil cloud thinning. Nature Geoscience, 2024, 17, 398-403.
- What happens when climate and mental health crises collide? Nature, 628, 235.
- Wong, C. Climate change is also the health crisis: These graphics explain Why. Nature, 624, 14-16.
- Schirber, M. Nobel prize: Complexity, from atoms to atmospheres. 2021, Physics 14, 141.
- Zhong, J., Cao, W. Graph Geometric Algebra networks for graph representation learning. 2025, Nature, Scientific Reports, 15, 170.
- Dubrulle, B. 2022. Multifractality, Universality and Singularity in Turbulence. 2022. Fractal and Fractional, 6, 613.
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How to cite: Patiño, P. and Oleschko, K.: One day in the life of clouds , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14433, https://doi.org/10.5194/egusphere-egu25-14433, 2025.
Quantities such as discharge, flow velocity, water depth, and pollutant loads are essential for understanding pollutant emissions and for effective hydrological management, including flood control, water supply, and ecosystem preservation. Despite their critical importance and advances in data collection and modeling, the challenge of predicting these quantities in ungauged or poorly monitored basins persists. In the case of pollutants, this issue is complexified by the growing number of pollutants requiring evaluation. For example, within the EU, more than 100,000 chemical compounds require assessment.
Scaling relationships, which relate system characteristics to basin size metrics, offer a promising stepping stone to address this challenge. For instance, river discharge - one of the most fundamental hydrological metrics - has been shown to scale with basin size through power-law relationships. This scaling is also influenced by additional factors such as climatic conditions, land use, and geomorphology, underscoring the need for integrated approaches to characterize the scaling relationship. Similarly, pollutant loads are often expressed through models such as the Concentration-Discharge (C-Q) relationship, which links pollutant concentrations (C) to discharge rates (Q). While such models provide valuable insights, their applicability requires robust scaling principles to account for variability in pollutant sources, and transport mechanisms.
In this contribution, we present our framework to derive scaling relationships for key quantities in the hydrological cycle and pollutant loads within river basins, focusing on their dependence on size-related indicators of river basins. Scaling principles of metrics such as discharge, flow velocity, water depth, and groundwater volume are derived using observational datasets, such as Global Runoff Data Center and SWOT river database. For pollutant loads and emissions, where monitoring is limited to a few key indicators, scaling principles offer promising avenues to predict emissions across diverse systems. By linking hydrological and pollutant-related variables through consistent scaling principles, we aim to provide a unified approach to understanding variability across river basins of different sizes. This work underscores the value of scaling relationships in bridging theoretical insights and practical applications, offering tools for improved management of water resources and pollutant impacts.
How to cite: Schreyers, L. and Hendriks, A. J.: Scaling relationships in hydrological quantities and pollutant loads across river basins, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17717, https://doi.org/10.5194/egusphere-egu25-17717, 2025.
Multifractal processes describe complex systems characterized by variability that spans across multiple scales and intensities, governed by scale-invariant distributions of extreme values. Universal Multifractals (UM) provide a robust framework for modelling and understanding the inherent extreme variability and scaling properties of various geophysical phenomena. It is a parsimonious framework that relies on only 3 parameters with physical interpretation, C1 the mean intermittency, α the multifractality index and H the non-conservation parameter.
Rainfall, inherently variable across spatial and temporal domains, has been widely studied in the framework of UM, with techniques like Trace Moment (TM) and Double Trace Moment (DTM) applied to characterize its scaling properties. Based on this framework, this study aims to assess the correlation between rainfall scaling features and extremes, and temperature ones, relying on multifractal analysis such as DTM and TM. High resolution simultaneously collected rainfall data from disdrometers and temperature data from meteorological stations is used. Data was collected during various measurement campaigns operated by the TARANIS observatory of HM&Co laboratory of Enpc (https://hmco.enpc.fr/portfolio-archive/taranis-observatory/). Data collected both in an urban area and on a meteorological mast located on a wind farm is used. For the disdrometer data, it was collected with 30 seconds time steps, As for the temperature, the meteorological station measures the temperature at 1Hz, so to match their time series it was necessary to take averages of the temperature data at each 30s.
Initially, the study explores the correlation between the primary multifractal parameters (C1, α, H) of rainfall and the average temperature at the rainfall event scale. Subsequently, a comparative analysis was conducted between these rainfall parameters and their counterparts derived from temperature fluctuations. This two-step approach aimed to uncover not only direct correlations between rainfall and temperature but also the extent to which the multifractal properties of rainfall mirror those observed in temperature dynamics. In a second part of the study, similar analysis on longer periods of typically one month are used to complement event based analysis by accounting for dry periods.
Authors acknowledge the ANR PRCI Ra2DW project supported by the French National Research Agency – ANR-23-CE01-0019-01 for partial financial support.
How to cite: Torres Guimarães, Y. and Gires, A.: Multifractal correlation of rainfall extremes and temperature, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1093, https://doi.org/10.5194/egusphere-egu25-1093, 2025.
The intermittent nature of turbulence introduces significant variability and extreme events, which profoundly complicates efforts to accurately measure, model, and predict its behavior. In the context of the atmospheric boundary layer, this intermittent turbulence can lead to localized bursts of wind shear, which poses risks to wind energy operations. These fluctuations directly impact the operational efficiency and structural integrity of wind turbines. Specifically, turbulence influences fatigue loads on the turbines and is essential for accurately modeling wake effects that occur within wind farms, which can affect the performance of adjacent turbines and forecasting energy production. Research suggests that a multifractal framework characterized by complex patterns across various scales enables one to properly model the intermittency of turbulence. To investigate this phenomenon, the present study analyzes wind data collected using a state-of-the-art lidar (Light Detection and Ranging) system profiler. This profiler was deployed on an offshore measurement mast situated near an offshore wind farm located 13 kilometers off the coast of Fécamp, France. Employing a universal multifractal (UM) framework, this study seeks to simulate and analyze the extreme variability inherent in the collected data. In the first step, The UM framework will be used to quantify the effects of intermittency on standard metrics such as turbulence intensity (TI) and spectral slopes, also accounting for the resolution at which they are computed and the frequency of data. Empirical estimates of TI and spectral slope in homogeneous turbulence often deviate from theoretical scaling, which can be theoretically and empirically quantified. In the second step, the results of the UM analysis of the measured time series will be discussed. Additionally, this study will delve into the instrumental biases introduced by the lidar instrument used in the measurement of turbulence. These biases can significantly impact the accuracy of data interpretation and reliability of results, making it essential to explore and address them thoroughly. This research not only addresses the theoretical aspects of turbulence but also has practical implications for optimizing wind energy operations in the face of unpredictable environmental conditions. Finally, the authors would like to acknowledge the partial financial support of the French Government, managed by the Agence Nationale de la Recherche under the Investissements d’Avenir program, with the reference ANR-10-IEED-0006-34. This work was carried out in the framework of the NEMO project.
How to cite: Maurya, S., Gires, A., Tchiguirinskaia, I., Schertzer, D., and Thiébaut, M.: An investigation on the impact of intermittency on wind Lidar profiler data utilizing a Universal Multifractal framework , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12865, https://doi.org/10.5194/egusphere-egu25-12865, 2025.
Changes in ecohydrological systems are driven by emergent patterns of organization that arise through internal differentiation, reflected in the variability of ecosystem components and shifts in the strengths of positive and negative feedbacks. This phenomenon, known as self-organization, allows ecosystems to transition between self-organized states in response to external perturbations, leading to new dynamic regimes. The resulting overall emergent properties represent a balance between the loss of stability and shifts toward equilibrium within ecosystems. However, it remains unclear whether ecosystem self-organization is guided by a convergence of states and feedbacks toward an optimal state and, if so, what such an optimal state might look like.
Using information-theoretic approaches, we characterize ecosystem variability and feedbacks as entropy changes based on observations. To do so, we concentrate on eddy-covariance measurements from global FLUXNET stations. Our findings reveal potential optimal states toward which ecosystems tend to transition and identify the conditions that govern these transitions, shaping the evolutionary trajectories of ecosystems. These results also provide a framework for assessing ecosystem resilience to major perturbations, such as droughts and heatwaves, and emphasize the critical role of hydrological variability in improving predictions of ecosystem changes and extreme events that pose risks to water and food security.
How to cite: Hagan, D. F. T., Ruddell, B. L., Hsu, H., Zhang, Y., and Miralles, D. G.: What guides regime transitions in ecohydrological systems?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8573, https://doi.org/10.5194/egusphere-egu25-8573, 2025.
International projections indicate that extreme climatic events will become more frequent and intense, leading to significant disruptions in water cycle patterns. At the same time, water remains the only irreplaceable natural resource. As a result, human economies must be prepared to confront a range of socio-economic challenges stemming from changes in the water cycle. These issues cannot be resolved through incremental improvements to existing measures. Consequently, there is a growing call for "transformative change" — a comprehensive, system-wide restructuring at various scales, akin to what physicists describe as a non-equilibrium phase transition in a complex, nonlinear system — to tackle the interconnected and persistent challenges.
In recent years, there has been a growing global emphasis on funding research with greater "transformative impact." This often leads to a focus on the outcomes and content of transformative change, when the real focus should be on the underlying physics, as achieving transformative change depends fundamentally on the interactions of these underlying processes. The scientific challenge common to both socio-economic and hydrological systems lies in their pronounced spatio-temporal heterogeneity and variability within urban environments. This variability arises from the highly nonlinear interactions among the relevant variables, which produce extreme multiscale fluctuations and complex causal chains, beginning with the fact that responses are not proportional to the initial stimuli or forces.
Urban geosciences introduce additional complexity compared to traditional geosciences: their physical scales are much smaller, requiring not only higher-resolution observation technologies, which is already a significant challenge, but also involve much shorter interaction times. This shorter timescale is particularly crucial for prediction, as it limits the predictability of these systems. In this context, universal multifractals (multiplicative stochastic processes) likely provide the most effective framework for establishing a common foundation that supports more diverse and collectively potent approaches to transformative environmental change. Gaining a deeper understanding of multifractal phase transitions and their practical application, alongside alternative innovations, is key to fostering transformative change.
To promote such transitions, this presentation will focus on non-trivial symmetries to address much of the complexity outlined earlier. A key example is scale symmetries, which allow for the definition of scale-independent observables, in contrast to classical observables that are heavily dependent on scale. This scale dependence creates several challenges, starting with the fact that the models based on these observables are also scale-dependent. Scale-independent observables, often referred to as singularities, are significant because they capture the divergence of classical observables as resolution increases, or as we look at progressively smaller scales. The strength of this approach lies in its application to urban geosciences, specifically for: (i) defining environmental indicators for cities and their characteristics, (ii) monetizing the amenities provided by blue-green solutions in urban areas and contextualizing them socio-economically on a large scale, and (iii) developing a new form of multifractal evaluation for environmental balance - altogether enabling "transformative chift" towards the sheared value economy.
The authors sincerely acknowledge the partial financial support provided by the TIGA CfHf project (https://hmco.enpc.fr/portfolio-archive/tiga/).
How to cite: Tchiguirinskaia, I., Drouen, G., Qiu, Y., Versini, P.-A., Gires, A., and Schertzer, D.: Multifractal Phase Transitions for the “Transformative Shift” Towards a Shared Value Economy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20554, https://doi.org/10.5194/egusphere-egu25-20554, 2025.
As climate change accelerates, the frequency and intensity of flood events are rising, creating significant challenges for critical infrastructure systems worldwide. Transportation, energy, and communication networks are particularly vulnerable, and their resilience to such disasters
is crucial for minimizing long-term impacts. This study examines five recent flood events—Germany (2021), Belgium (2021), Sydney (2022), Auckland (2023), and Italy (2023)—to explore the effects of these floods on critical infrastructure and identify best practices for enhancing resilience. The research focuses on answering the central question:
How do recent flood events impact critical infrastructure, and what best practices can be identified for improving resilience?
Due to the recent nature of these floods, data collection was a pivotal aspect of the study, with information sourced from public news reports, research journals, government reports, and interviews. A Multi-Criteria Decision Making (MCDM) method- the Vikor, was employed to rank hazards, vulnerabilities, and the resilience of critical infrastructure in each case study. This approach provided a systematic evaluation of shared vulnerabilities and region-specific
differences in disaster response and infrastructure resilience.
The findings highlight the importance of multi-stakeholder collaboration, early warning systems, and adaptive infrastructure solutions in mitigating flood impacts. Best practices were identified across all phases of disaster management—pre-disaster preparedness, immediate emergency response, and long-term recovery. These practices emphasize the need for innovative infrastructure adaptations, community engagement, and coordinated
governance to build more resilient systems.
This research offers valuable insights for policymakers, urban planners, and disaster management professionals. By analyzing these five flood events, the study provides transferable lessons on how to enhance infrastructure resilience and integrate adaptive strategies into policy frameworks. Ultimately, this research contributes to the broader global discourse on climate adaptation and disaster risk reduction, aiming to strengthen preparedness
for future flood events.
Keywords: Flood resilience, critical infrastructure, case study analysis, MCDM, disaster management, data collection, best practices
How to cite: Chakraborty, S., El Faouzi, N. E., and Furno, A.: From Crisis to Adaptation: The Resilience of Critical Infrastructure in Recent Flood Events, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11882, https://doi.org/10.5194/egusphere-egu25-11882, 2025.
Posters on site: Fri, 2 May, 10:45–12:30 | Hall X4
Typhoon wind dynamics are inherently nonlinear, exhibiting complex interactions between large-scale trajectory shifts and small-scale variability. This study employs variational retrieval techniques and multifractal analysis to investigate altitude-specific wind field patterns and their connections to trajectory and intensity changes. Using radar observations and numerical model data, high-resolution 3D wind fields were constructed to explore the structural and statistical characteristics of wind components (U, V) across different altitudes and typhoon trajectories.
Our analysis focuses on two distinct trajectory types: northward-moving typhoons (e.g., Nakri, Lingling, Bavi) and northeastward-moving typhoons (e.g., Chaba, Kong-Rey). Results indicate that northward trajectories exhibit crescent-shaped wind patterns dominated by northerly wind components, while northeastward trajectories show circular wind structures. Notably, multifractal analysis revealed abrupt decreases in the multifractal parameter α for northerly winds at 1–2 km altitude during trajectory transitions, suggesting nonlinear structural reorganization within the typhoon system. For example, during Typhoon Chaba (2016) and Typhoon Kong-Rey (2018), α values for northerly winds dropped sharply by 1.5–2.2 units, coinciding with significant directional shifts and rapid changes in typhoon directions.
In addition to wind field analysis, we quantified variability in rainfall fields using radar reflectivity and rainfall intensity data. Northeastward-moving typhoons demonstrated broader and more intense rainfall bands, with higher vertical reflectivity profiles up to 8 km altitude, compared to the narrower and more localized patterns observed in northward-moving cases. This suggests a strong coupling between wind field variability and rainfall distribution, driven by nonlinear interactions.
By integrating multifractal techniques with variational retrieval methods, this study bridges small-scale turbulence with large-scale trajectory dynamics, offering new insights into the inherent complexity of typhoon systems. These findings contribute to the development of advanced prediction systems, enabling more accurate trajectory and intensity forecasts. Such approaches could significantly mitigate the impacts of typhoons on the Korean Peninsula and beyond.
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No.RS-2024-00460019)
How to cite: Lee, J., Hwang, S.-J., and Lee, D.-I.: Decoding Typhoon Wind Patterns: Variational Retrieval and Multifractal Insights, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-735, https://doi.org/10.5194/egusphere-egu25-735, 2025.
The transport properties of porous media exhibit complex multiscale behaviours, which are governed by nonlinear interaction of structural heterogeneity, and which present significant challenges for theoretical understanding and practical modelling. To address this complexity, we propose a fractal-based framework to quantitatively link structural parameters with transport behaviours, focusing specifically on electrical current flow in porous media. Our approach develops a tortuosity model based on self-similarity principles in order to describe the geometric structure, and to assess the transport properties, such as permeability and electrical conductivity.
At the single-capillary level, key microstructural properties, such as pore geometry and connectivity, and transport properties, including permeability and electrical conductivity, can be quantified using metrics such as fractal dimension, tube number, and characteristic length. These parameters capture both structural complexity and scaling behaviour. Taking electrical conductivity as an example, a two-dimensional porous medium with a grid resolution of 16,384 × 16,384 is generated using the Quartet Structure Generation Set (QSGS) method and partitioned into smaller scales (e.g., 1024, 512, 256, and 128) to explore multiscale behaviour and scaling effects. Finite difference methods are employed to calculate the electrical field distributions and derive the effective electrical conductivity. These results are then mapped to the parameters of the self-similar tortuosity model, providing insights into its ability to capture the complex relationships between structure and transport properties.
Statistical analysis reveals that the measured fractal dimensions follow a Weibull distribution across scales, characterised by its distinctive shape and scale parameters. By contrast, characteristic length and tube number values exhibit scale-dependent variations that influence their respective distribution patterns. Tube number conforms to a lognormal distribution, reflecting its intrinsic variability. These findings enable the development of more accurate and computationally efficient multiscale models, with potential applications in areas such as fluid flow, heat transfer, and the design of advanced porous materials.
How to cite: Wei, W., Glover, P., and Lorinczi, P.: Multiscale Statistical Distribution of Porous Media Transport Behaviour: A Fractal Geometry Approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4129, https://doi.org/10.5194/egusphere-egu25-4129, 2025.
We present a statistical approach to estimate the significance of the intermittency of solar wind magnetic field fluctuations. We analyze nine days of magnetic field data provided by Parker Solar Probe (PSP) at about 0.17 AU from the Sun during the probe’s first perihelion (Encounter 1). Intermittency is estimated based on flatness, the normalized fourth-order moment of the probability distribution functions. When we divide the signal in sub-intervals of 3 to 24 hours length, we find that flatness/intermittency varies from interval to interval. Sub-intervals showing very low levels of intermittency, with flatness values close to three at all scales, alternated with highly intermittent sub-intervals where flatness reaches values close to 60.
In order to understand the observed variability of the intermittency level, we applied a statistical test based on data surrogates (Theiler et al., 1992) tailored to identify nonlinear dynamics in a time series. The aim is to falsify a null hypothesis that is a-priori known to be invalid, i.e. the intermittency observed in PSP data results from a linear Gaussian-like physical process, with the nonlinearity being due to the observation function.
The surrogates are generated such that all nonlinear correlations contained in the dynamics of the signal are eliminated. We find that the flatness computed for the original signal is significantly different from that computed for the ensemble of surrogates, i.e. the null hypothesis is falsified. Thus, the flatness is indeed a descriptor of the intermittency resulting from the inherent nonlinear dynamics of the process captured by the magnetic field observations of the PSP. We also discuss how the non-stationarity of a time series affects the flatness computed for both the PSP data and the surrogates, precluding the null hypothesis is falsified.
Further, a multi-order simultaneous fit of the structure functions revealed a decrease in flatness at scales smaller than a few seconds: intermittency is reduced in this scale range. This behavior was mirrored by the spectral analysis, which was suggestive of an acceleration of the energy cascade at the high frequency end of the inertial regime.
How to cite: Teodorescu, E., Echim, M., and Johnson, J.: Nonlinear dynamics and intermittency of the solar wind magnetic field fluctuations probed with surrogate data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20443, https://doi.org/10.5194/egusphere-egu25-20443, 2025.
Intermittency is a defining characteristic of rainfall, yet it is largely overlooked in most IA nowcasting models. We emphasise its theoretical significance at various stages of the prediction process, from training to assessing its accuracy, including its dispersion relative to the intrinsic limits of predictability.
Specifically, we develop a hybrid framework based on:
- - The generative adversarial network (GAN), a recently developed technique for training IA models through an adversarial process;
- Universal multifractals (UM), stochastic models of intermittency that are physically based on the cascade paradigm. They are universal in the sense that they are statistically attractive to other processes and depend only on three scale-independent parameters that are physically meaningful.
In terms of physical relevance, we evaluate the nowcasting performance of the hybrid UM-GAN model and other baseline models (ConvLSTM, GAN) using continuous and categorical scores, as well as UM analysis in comparison to the observations. The results indicate that UM-GAN achieves the highest scores and accuracy, particularly demonstrating superior performance at lead times of 30 minutes and 60 minutes.
How to cite: Schertzer, D., Zhou, H., and Tchiguirinskaia, I.: Combining Artificial Intelligence and Multifractals for Precipitation Nowcasting: the UM-GAN Example., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20565, https://doi.org/10.5194/egusphere-egu25-20565, 2025.
Posters virtual: Fri, 2 May, 14:00–15:45 | vPoster spot 4
EGU25-16203 | ECS | Posters virtual | VPS20
Temporal and Spatial Dynamics of Urban Evapotranspiration in Paris: A Multiscale PerspectiveFri, 02 May, 14:00–15:45 (CEST) | vP4.14
The impacts of global change, such as extreme heat and water scarcity, are increasingly threatening urban populations. Evapotranspiration (ET) plays a vital role in mitigating urban heat islands and reducing the effects of heat waves. It also serves as a proxy for vegetation water use, making it a critical tool for designing resilient green cities. Despite its importance, high-resolution mapping of urban ET that captures both spatial and temporal dynamics remains limited. This study focuses on the Paris metropolitan area, analyzing ET variability across multiple spatial scales (from 10 m to 10 km) using Sentinel-2 data from the Copernicus system. The Normalized Difference Vegetation Index (NDVI) is calculated with observation scale of 10 m, and then used as a proxy for ET. Universal Multifractal analysis, which have been widely used to characterize and model geophysical fields extremely variable across wide range of space-time scales, are implemented on this new data set. This framework is parsimonious since it basically relies on three parameters only: the mean intermittency codimension C1, the multifractality index a and the non-conservation parameter H. Specifically, the multifractality index α (1.3–1.5) and the mean intermittency codimension C1 (~0.02) were derived to quantify the spatial and temporal heterogeneity of ET. The analysis, spanning 2019–2023, revealed noticeable temporal and spatial variability in ET. The study focuses on a square region of approximately 60 km × 60 km within the area around Paris. This region was further divided into multiple portions of size ranging from 2 to 10 km to assess potential variability over the studied areas. By incorporating both yearly and monthly data, the analysis captured seasonal trends as well as interannual variability, with higher variability observed during the summer months, driven by increased vegetation activity and water demand. Spatially, yearly data was analyzed and ET variability was most pronounced in densely populated areas, such as central Paris, where anthropogenic influences dominate. In contrast, forested areas and urban parks demonstrated significantly more stable ET patterns, underscoring the moderating effect of vegetation cover. These findings highlight the critical role of urban greening in mitigating extreme variability and stress on urban ecosystems.
How to cite: Zhu, S., Gires, A., Schertzer, D., Tchiguirinskaia, I., and Maksimovic, C.: Temporal and Spatial Dynamics of Urban Evapotranspiration in Paris: A Multiscale Perspective, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16203, https://doi.org/10.5194/egusphere-egu25-16203, 2025.
EGU25-19070 | Posters virtual | VPS20
Rainfall Dynamics in Wind Energy ScenariosFri, 02 May, 14:00–15:45 (CEST) | vP4.15
The presence of rain in wind farms involves several modeling challenges, as the momentum exchanges between turbulent wakes and the particle phase present subtle phenomena. For instance, rain droplets are typically large enough to exhibit inertia relative to the air carrier phase. Under these conditions, it has been found that the gravitational settling of particles in turbulent flows may be either enhanced or hindered compared to stagnant conditions. While this has significant implications for rainfall transport, ash pollutants, and pollen dispersion, very few studies have been conducted in field conditions. Moreover, the scaling laws and non-dimensional parameters governing this phenomenon have not yet been properly identified, and determining which configurations result in the enhancement or hindrance of settling velocity remains an open question.
We propose a hybrid experimental/numerical approach. Field data from a meteorological mast located at a wind farm in Pays d’Othe, 110 km South-East of Paris, France, were used to characterize the background turbulent flow through a set of sonic anemometers. Additionally, disdrometers were employed to characterize the settling velocity of raindrops, discriminating by particle size. Numerical simulations complement this data analysis. Specifically, 3D space and time vector fields that realistically reproduce the observed spatial and temporal variability of wind fields are generated using multifractal tools. Then, 3D trajectories of non-spherical particles are simulated and their settling velocity derived.
Our findings indicate that the presence of turbulence significantly hinders the settling velocity of raindrops in turbulent environments. Our study covers several distinct rainfall events, allowing us to analyze the influence of turbulent flow properties on this phenomenon.
How to cite: Obligado, M. and Gires, A.: Rainfall Dynamics in Wind Energy Scenarios, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19070, https://doi.org/10.5194/egusphere-egu25-19070, 2025.
EGU25-20653 * | Posters virtual | VPS20 | Highlight
The turbulence of solids: a multifractal plate tectonic model with Guttenberg-Richter plate “quakes”Fri, 02 May, 14:00–15:45 (CEST) | vP4.16
Over thirty years ago, Y. Kagan speculated that seismicity could fruitfully be considered as “the turbulence of solids”. Indeed, fluid turbulence and seismicity have many common features: they are both highly nonlinear with huge numbers of degrees of freedom. Beyond that, Kagan recognized that they are both riddled with scaling laws in space and in time as well as displaying power law extreme variability and – we could add – multifractal statistics.
Kagan was referring to seismicity as usually conceived, as a sudden rupture process occurring over very short time periods. We argue that even at one million year time scales, that the movement of tectonic plates is “quake-like” and is quantitatively close to seismicity, in spite of being caused by relatively smooth mantle convection.
To demonstrate this, we develop a multifractal model grounded in convection theory and the analysis of the GPlates data-base of 1000 point trajectories over the last 200 Myrs. We analyzed the statistics of the dynamically important vector velocity differences where Dr is the great circle distance between two points and Dt is the corresponding time lag. The longitudinal and transverse velocity components were analysed separately. The longitudinal scaling of the mean longitudinal difference follows the scaling law <Δv(Δr)> ≈ ΔrH with empirical H close to the mantle convection theory value H = 1. This high value implies that mean fluctuations vary smoothly with distance. Yet at the same time, the intermittency exponent C1 is extremely high (C1 ≈ 0.55) implying that from time to time there are enormous “jumps” in velocity: “Plate quakes”. For comparison, laminar (nonturbulent) flow has H = 1 but is not intermittent (C1 = 0), whereas fully developed isotropic fluid turbulence has the (less smooth) value H = 1/3 (Kolmolgorov) but with non-negligible intermittency C1 ≈ 0.07 and seismicity has very large C1 ≈ 1.3. Our study thus quantitatively shows how smooth fluid-like behaviour for the longitudinal velocity component can co-exist with highly intermittent quake-like behaviour.
Whereas the longitudinal component is well modelled by (highly intermittent) convection, the transverse velocity is well modelled by Brownian motion. In the temporal domain both components (including their strong correlations) display such diffusion behaviour (i.e. with classical exponent H = ½), but are highly intermittent (C1time = C1space/2 ≈ 0.27). Finally, the extreme velocity differences (that appear as occasional spikes in the velocities) have power law probability tails; the “Guttenberg-Richter” exponents in the seismology literature.
The advection - diffusion model is based on an underlying multifractal space-time cascade process. Using mantle convection theory, we show how the driving multifractal flux (ψ) is related to vertical heat fluxes, expansion coefficients, densities, viscosities and specific heats. Taking typical values predict driving fluxes very close to the observed mean <ψ> ≈ 1/(400 Myrs). Trace moment analysis shows that the outer space-time scales of the cascade process are ≈17000 km in space and ≈ 50Myrs in time. Whereas the former corresponds to half the Earth’s circumference, the latter is the typical time required for a plate to randomly “walk” the same distance.
How to cite: Lovejoy, S., Spiridonov, A., and Balakauskas, L.: The turbulence of solids: a multifractal plate tectonic model with Guttenberg-Richter plate “quakes” , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20653, https://doi.org/10.5194/egusphere-egu25-20653, 2025.
