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Normalized frequency of synchronization, F p ( λ < 0 ) , for system of environmental interfaces passively coupled (12)–(14) as a function of parameter of exchange p . An averaging was done over all values of coupling parameter c and logistic parameter r .
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Some issues which are relevant for the recent state in climate modeling have been considered. A detailed overview of literature related to this subject is given. The concept in modeling of climate, as a complex system, seen through Gödel’s theorem and Rosen’s definition of complexity and predictability is discussed. Occurrence of chaos in computing...
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In the present study, a range of regional climate models have been used to test approaches to Bayesian model averaging (BMA), particularly the quantification of model weights/Bayesian priors. The results of skill assessments were used to inform probabilistic future projections of Irish climate using a BMA approach in order to evaluate how different...
Citations
... Dynamical chaos has produced many fascinating applications across various scientific fields, e.g., quantum mechanics and nonlinear optics [1]; physical phenomena described by nonlinear Schrödinger equations [2]; chaotic communication systems [3,4]; celestial mechanics [5]; weather and climate investigation [6]; fluid dynamics [7] and chemical reactions [8]; and biological systems [9] and population dynamics [10]. One group of applications is of special interest and includes cryptography and trajectory-planning algorithms based on pseudorandom bit generation and chaos. ...
Developing efficient path-planning algorithms is an essential topic in modern robotics and control theory. Autonomous rovers and wheeled and tracked robots require path generators that can efficiently cover the explorable space with minimal redundancy. In this paper, we present a new path-planning algorithm based on the chaotic behavior of the Courbage-Nekorkin neuron model with a coverage control parameter. Our study aims to reduce the number of iterations required to cover the chosen investigated area, which is a typical efficiency criterion for this class of algorithms. To achieve this goal, we implemented a pseudorandom bit generator (PRBG) based on a Courbage-Nekorkin chaotic map, which demonstrates chaotic behavior and successfully passes all statistical tests for randomness. The proposed PRBG generates a bit sequence that can be used to move the tracked robot in four or eight directions in an operation area of arbitrary size. Several statistical metrics were applied to evaluate the algorithm's performance, including the percentage of coverage of the study area and the uniformity of coverage. The performance of several competing path-planning algorithms was analyzed using the chosen metrics when exploring two test areas of the sizes 50 × 50 cells and 100 × 100 cells, respectively, in four and eight directions. The experimental results indicate that the proposed algorithm is superior compared to known chaotic path-planning methods, providing more rapid and uniform coverage with the possibility of controlling the covered area using tunable parameters. In addition, this study revealed the high dependence of the coverage rate on the starting point. To investigate how the coverage rate depends on the choice of chaotic map, we implemented six different PRBGs using various chaotic maps. The obtained results can be efficiently used for solving path-planning tasks in both real-life and virtual (e.g., video games) applications.
... Nonetheless, this is an increase in computational cost and one that would be larger in a model where a high-order closure is not already being implemented. Even besides the issue of computational cost, the inclusion of equations with more terms used to prognose u h w increases the complexity of the model, thereby increasing the risk of introducing artifacts and increasing the difficulty of understanding model behavior (Mihailović et al., 2014). ...
Improving the prediction of clouds in shallow-cumulus regimes via turbulence parameterization in the planetary boundary layer (PBL) will likely increase the global skill of global climate models (GCMs) because this cloud regime is common over tropical oceans where low-cloud fraction has a large impact on Earth's radiative budget. This study attempts to improve the prediction of PBL structure in tropical trade wind regimes in the Community Atmosphere Model (CAM) by updating its formulation of momentum flux in CLUBB (Cloud Layers Unified by Binormals), which currently does not by default allow for upgradient momentum fluxes. Hindcast CAM output from custom CLUBB configurations which permit countergradient momentum fluxes are compared to in situ observations from weather balloons collected during the ElUcidating the RolE of Cloud–Circulation Coupling in ClimAte and Atlantic Tradewind Ocean–Atmosphere Mesoscale Interaction Campaign (EUREC4A/ATOMIC) field campaign in the tropical Atlantic in early 2020. Comparing a version with CAM–CLUBB with a prognostic treatment of momentum fluxes results in vertical profiles that better match large-eddy simulation results. Countergradient fluxes are frequently simulated between 950 and 850 hPa over the EUREC4A/ATOMIC period in CAM–CLUBB. Further modification to the planetary boundary layer (PBL) parameterization by implementing a more generalized calculation of the turbulent length scale reduces model bias and root mean squared error (RMSE) relative to sounding data when coupled with the prognostic momentum configuration. Benefits are also seen in the diurnal cycle, although more systematic model errors persist. A cursory budget analysis suggests the buoyant production of momentum fluxes, both above and below the jet maximum, significantly contributes to the frequency and depth of countergradient vertical momentum fluxes in the study region. This paper provides evidence that higher-order turbulence parameterizations may offer pathways for improving the simulation of trade wind regimes in global models, particularly when evaluated in a process study framework.
... Notably, E.N. Lorenz in 1963 conducted a study on the convective model of the atmosphere, establishing climate as a chaotic system (3) . Various studies have analyzed chaotic systems through nonlinear differential equations (3) , logistic maps (4) , and chaotic time series analysis (5)(6)(7)(8) . Examples include the work of P. Indira, S.S.R Ibanathan, R.S. Selvaraj, and A.A. Suresh (5) on daily maximum temperature in Chennai, India, and A.T. Adewole, E.O. ...
... Nonetheless, this is an increase in computa-540 tional cost and one that would be larger in a model where a high-order closure is not already being implemented. Even besides the issue of computational cost, the inclusion of equations with more terms used to prognose u ′ h w ′ increases the complexity of the model, thereby increasing the risk of introducing artifacts and increasing the difficulty of understanding model behavior (Mihailović et al., 2014). ...
Improving the prediction of clouds in shallow cumulus regimes via turbulence parameterization in the planetary boundary layer (PBL) will likely increase the global skill of global climate models (GCMs) because this cloud regime is common over tropical oceans where low cloud fraction has a large impact on Earth’s radiative budget. This study attempts to improve the prediction of PBL structure in tropical trade-wind regimes in the Community Atmosphere Model (CAM) by updating its formulation of momentum flux in CLUBB (Cloud Layers Unified by Binormals), which currently does not by default allow for upgradient momentum fluxes. Hindcast CAM output from custom CLUBB configurations which permit countergradient momentum fluxes are compared to in-situ observations from weather balloons collected during the ElUcidating the RolE of Cloud–Circulation Coupling in ClimAte and Atlantic Tradewind Ocean–Atmosphere Mesoscale Interaction Campaign (EUREC4A/ATOMIC) field campaign in the Tropical Atlantic in early 2020. Comparing a version with CAM-CLUBB with a prognostic treatment of momentum fluxes results in vertical profiles that better match previously published LES results. Countergradient fluxes are frequently simulated between 950 hPa and 850 hPa over the EUREC4A/ATOMIC period in CAM-CLUBB. Further modification to the PBL parameterization by implementing a more generalized calculation of the turbulent length scale reduces model bias and RMSE relative to sounding data. Benefits are also seen in the diurnal cycle, although more systematic model errors persist. A cursory budget analysis suggests the buoyant production of momentum fluxes, both above and below the jet maximum, significantly contributes to the frequency and depth of countergradient vertical momentum fluxes in the study region. This paper provides evidence that higher-order turbulence parameterizations may offer pathways for improving the simulation of trade-wind regimes in global models.
... Understanding CTs provides insight into regional climate phenomena, as well as broader atmospheric dynamics, which are critical for weather forecasting, climatic modeling, and environmental risk assessments [1]. However, the complex, non-linear nature of atmospheric systems [2,3] poses signi cant challenges to accurate CT classi cation. A variety of CT classi cation methods have been adopted over time, aiming to decipher complex atmospheric systems [4,5,6,7]. ...
This study presents a novel approach that employs Autoencoders (AE) - a type of artificial neural network
- for the temporal decomposition of sea level pressure (SLP) data represented in a T-mode structure where
the columns or variables are time series and rows or observations are grid points. The analysis aims to
explore the effectiveness of non-linear AE in overcoming potential limitations of linear models like
Principal Component Analysis (PCA) in terms of classifying atmospheric circulation types (CTs) within
the southern region of Africa. After applying both PCA and AE for the time decomposition of daily SLP
data from 1950 to 2022, the resulting spatial composite maps generated by each method were compared
to assess consistency. The findings reveal consistency between the spatial maps from the two methods,
with 58% of the maps showing congruence matches greater than 0.94. However, when examining the
correctly classified dates (i.e., the true positives) with a threshold of 0.8 congruence coefficient between
the spatial composite map representing the CT and the dates grouped under the CT, AE outperformed
PCA with an average improvement of 29.2%. Hence given the flexibility of AE and capacity to model
complex non-linear relationships, this study suggests that AE could be a potent tool for climate data
decomposition and CT classification, leading to a more accurate representation of the actual circulation variability
... Concrete applications of complexity sciences include weather forecasts (Mihailović et al., 2014), the analysis of ecological systems (Biggs et al., 2021) and accident investigations (Dekker et al., 2011). Resilience theory in ecology is based on complexity science (Folkeis, 2010) while complexity science is being suggested as an alternative approach to traditional economics (Chen, 2019), especially for forecasting (Speigel, 2021) as well as a crucial element in order to reach the goals of the UN 2030 Agenda (Gentili, 2021). ...
In this overview paper, we consider interactive digital narratives (IDN) as a means to represent and enable understanding of complex topics both at the public level (e.g. global warming, the COVID-19 pandemic, migration, or e-mobility) and at the personal level (trauma and other mental health issues, interpersonal relationships). We discuss scholarly, artistic, and non-fiction approaches to complexity, point out limitations of traditional media to represent complex issues, and describe the foundational advantages of IDN in this regard, using the SPP model as a conceptual lens. Then, we describe the problem space of IDN for complexity, and what aspects need further work in order to more fully realise the potential of IDN to represent complex topic in education and public communication.
... Chaos, as an interdisciplinary branch of physics, mathematics, life science, etc., is found in natural systems. For example, some chaotic attractors are found in the atmospheric dynamic model [11]; are observed in weather and climate systems [12], and liquid ow systems [13]. Furthermore, it is also explored in many arti cial systems, such as the stock market [14] and tra c roads [15]. ...
... When the system parameters are con gured, as shown in Table 2, it can determine the precise values of each component of the circuit in Fig. 14 by comparing Eq. (11) and Eq. (12). Let the circuit capacitance be 10nF for convenience of experiment and adjust, and the other resistance values are calculated, as listed in Table 3 and the remaining resistance values are all taken as 10kΩ. ...
This paper proposes a voltage-controlled memristor and constructs its simulation circuit by series-parallel connections with other electronic components. Based on it, a nonlinear system is designed and comprehensively analyzed. And it is found that the system has two Lyapunov exponents greater than zero and the sum of all Lyapunov exponents is approximately zero. This indicates that the system is a conservative hyperchaotic system. Phase diagrams, Lyapunov exponents, Poincare maps, power spectrums, etc. are used to analyze the complex dynamics of the system. Surprisingly, a type of quasiperiod-hyperchaos switching and an infinite number of coexistence attractors are explored. And the Multisim circuit is constructed. simulation of the system verifies the correctness of the theoretical analysis.
... Modelling and predictions of weather and climate are not new, they were initiated in the early sixties of the 20 th Century (Mihailović, et. al., 2014. [1]). Climate change models of first-order ODE with a known initial condition is referred to as an Initial Value '' , ; y f x y y x y (1) in which the underlying interpolant is a rational function. This is in contrast with the classical formulae which are in general based on polynomial approximation (Enoch, et.al., 2011(Enoc ...
... Climate change models of first-order ODE with a known initial condition is referred to as an Initial Value '' , ; y f x y y x y (1) in which the underlying interpolant is a rational function. This is in contrast with the classical formulae which are in general based on polynomial approximation (Enoch, et.al., 2011(Enoch, et.al., , 2017; [2,3]). With these formulae, the solution of (1) is locally represented by an interpolant that can possess a simple pole thus affording an improved numerical solution of (1). ...
We present a class of numerical schemes that are derived from a perturbated interpolant that has a varying order polynomial as the assumed solution to the Ordinary Differential Equation (ODE) models on climate change. These numerical integrators are capable of solving problems arising from chemical kinetics, population models, mechanical oscillations, planetary motions, electrical networks, nuclear reactor control, tunnel switching problems, reversible enzyme kinetics. But in this work, we desire to apply them to the numerical solutions of Autonomous Initial Value Problems (IVPs) in ODEs on climate change. At the end we conduct a numerical experiment, the resulting methods, algorithms, and solutions will be predictive tools for the study of climatic change models with applications to big data.
... One exemplar approach in complexity theory resulted from a multidisciplinary collaboration pursuing lines of enquiry characterized by emergent complex behaviors of multi-level systems [13]. Concrete applications of complexity sciences include weather forecasts [17], the analysis of ecological systems [18] and accident investigations [19]. Resilience theory in ecology is based on complexity science [20] while complexity science is being suggested as an alternative approach to traditional economics [21], especially for forecasting [22] as well as a crucial element in order to reach the goals of the UN 2030 Agenda [23]. ...
In this overview paper, we consider interactive digital narratives (IDN) as a means to represent and enable understanding of complex topics both at the public level (e.g. global warming, the COVID-19 pandemic, migration, or e-mobility) and at the personal level (trauma and other mental health issues, interpersonal relationships). We discuss scholarly approaches to complexity, limitations of traditional media to represent complex issues, and describe the potential of IDN in this regard and what aspects need further work in research and beyond, which serve as the starting point for the EU COST action INDCOR.
... However, precipitation nowcasting cannot just rely on video-like radar echo frames which based on recent observations. On the one hand, the existing models which rely solely on radar observation image are hard to make precision long-term prediction because of the chaotic character of the climatic system [12]. On the other hand, with the development of global/regional assimilation and prediction system (GRAPES) developed by the Chinese Academy of Meteorological Sciences (CAMS), huge amount of simulated meteorological data can be obtained by four-dimensional variational assimilation system based on non-hydrostatic grid model [13]. ...
Accurate rainfall prediction is conductive to human life and disaster prevention. Meanwhile, deep learning methods are confirmed to be helpful to improve the accuracy of weather prediction. A novel data-driven neural network is proposed in this work referred as RainfallNet which introduces fusion module based on both radar echo observations and numerical weather prediction (NWP) data. The architecture of the network includes three elements: (1) dual encoders to extract the spatio-temporal feature of the radar echo images and NWP data respectively, (2) parallel attention mechanism combining channel attention and spatial attention to reveal the contribution of each data source and (3) combined loss function joining structural similarity loss, mean square error and mean absolute error with different weight for each rainfall level to further increase the meteorologically assessment metrics. The experiments on South China dataset demonstrate the effectiveness of our model, achieving superior performance on meteorologically assessment metrics over most existing algorithms.
