Journal articles on the topic 'Dynamic meteorology Mathematics'
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1
Persson, Anders. "Mathematics versus common sense: the problem of how to communicate dynamic meteorology." Meteorological Applications 17, no. 2 (June 2010): 236–42. http://dx.doi.org/10.1002/met.205.
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Egger, Joseph, and Joachim Pelkowski. "The first mathematical models of dynamic meteorology: The Berlin prize contest of 1746." Meteorologische Zeitschrift 17, no. 1 (February 26, 2008): 83–91. http://dx.doi.org/10.1127/0941-2948/2008/0261.
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Huang, Chunli, Xu Zhao, Weihu Cheng, Qingqing Ji, Qiao Duan, and Yufei Han. "Statistical Inference of Dynamic Conditional Generalized Pareto Distribution with Weather and Air Quality Factors." Mathematics 10, no. 9 (April 24, 2022): 1433. http://dx.doi.org/10.3390/math10091433.
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Air pollution is a major global problem, closely related to economic and social development and ecological environment construction. Air pollution data for most regions of China have a close correlation with time and seasons and are affected by multidimensional factors such as meteorology and air quality. In contrast with classical peaks-over-threshold modeling approaches, we use a deep learning technique and three new dynamic conditional generalized Pareto distribution (DCP) models with weather and air quality factors for fitting the time-dependence of the air pollutant concentration and make statistical inferences about their application in air quality analysis. Specifically, in the proposed three DCP models, a dynamic autoregressive exponential function mechanism is applied for the time-varying scale parameter and tail index of the conditional generalized Pareto distribution, and a sufficiently high threshold is chosen using two threshold selection procedures. The probabilistic properties of the DCP model and the statistical properties of the maximum likelihood estimation (MLE) are investigated, simulating and showing the stability and sensitivity of the MLE estimations. The three proposed models are applied to fit the PM2.5 time series in Beijing from 2015 to 2021. Real data are used to illustrate the advantages of the DCP, especially compared to the estimation volatility of GARCH and AIC or BIC criteria. The DCP model involving both the mixed weather and air quality factors performs better than the other two models with weather factors or air quality factors alone. Finally, a prediction model based on long short-term memory (LSTM) is used to predict PM2.5 concentration, achieving ideal results.
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Huang, Chunli, Xu Zhao, Weihu Cheng, Qingqing Ji, Qiao Duan, and Yufei Han. "Statistical Inference of Dynamic Conditional Generalized Pareto Distribution with Weather and Air Quality Factors." Mathematics 10, no. 9 (April 24, 2022): 1433. http://dx.doi.org/10.3390/math10091433.
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Air pollution is a major global problem, closely related to economic and social development and ecological environment construction. Air pollution data for most regions of China have a close correlation with time and seasons and are affected by multidimensional factors such as meteorology and air quality. In contrast with classical peaks-over-threshold modeling approaches, we use a deep learning technique and three new dynamic conditional generalized Pareto distribution (DCP) models with weather and air quality factors for fitting the time-dependence of the air pollutant concentration and make statistical inferences about their application in air quality analysis. Specifically, in the proposed three DCP models, a dynamic autoregressive exponential function mechanism is applied for the time-varying scale parameter and tail index of the conditional generalized Pareto distribution, and a sufficiently high threshold is chosen using two threshold selection procedures. The probabilistic properties of the DCP model and the statistical properties of the maximum likelihood estimation (MLE) are investigated, simulating and showing the stability and sensitivity of the MLE estimations. The three proposed models are applied to fit the PM2.5 time series in Beijing from 2015 to 2021. Real data are used to illustrate the advantages of the DCP, especially compared to the estimation volatility of GARCH and AIC or BIC criteria. The DCP model involving both the mixed weather and air quality factors performs better than the other two models with weather factors or air quality factors alone. Finally, a prediction model based on long short-term memory (LSTM) is used to predict PM2.5 concentration, achieving ideal results.
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Hunt, J. C. R. "Inland and coastal flooding: developments in prediction and prevention." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 363, no. 1831 (June 15, 2005): 1475–91. http://dx.doi.org/10.1098/rsta.2005.1580.
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We review the scientific and engineering understanding of various types of inland and coastal flooding by considering the different causes and dynamic processes involved, especially in extreme events. Clear progress has been made in the accuracy of numerical modelling of meteorological causes of floods, hydraulics of flood water movement and coastal wind–wave-surge. Probabilistic estimates from ensemble predictions and the simultaneous use of several models are recent techniques in meteorological prediction that could be considered for hydraulic and oceanographic modelling. The contribution of remotely sensed data from aircraft and satellites is also considered. The need to compare and combine statistical and computational modelling methodologies for long range forecasts and extreme events is emphasized, because this has become possible with the aid of kilometre scale computations and network grid facilities to simulate and analyse time-series and extreme events. It is noted that despite the adverse effects of climatic trends on flooding, appropriate planning of rapidly growing urban areas could mitigate some of the worst effects. However, resources for flood prevention, including research, have to be considered in relation to those for other natural disasters. Policies have to be relevant to the differing geology, meteorology and cultures of the countries affected.
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Poul, Lukáš. "On dynamics of fluids in meteorology." Central European Journal of Mathematics 6, no. 3 (May 27, 2008): 422–38. http://dx.doi.org/10.2478/s11533-008-0032-x.
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Palmer, T. N. "Edward Norton Lorenz. 23 May 1917 — 16 April 2008." Biographical Memoirs of Fellows of the Royal Society 55 (January 2009): 139–55. http://dx.doi.org/10.1098/rsbm.2009.0004.
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Ed Lorenz was a pioneer of chaos theory; he provided the first realization of a strange attractor based on a mathematical model of just three coupled differential equations. In addition, Ed made many seminal contributions to theoretical meteorology, not only in studies of the predictability of weather and climate, but also in advancing our basic understanding of the dynamics and thermodynamics of climate.
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Ahmed, Shady E., Suraj Pawar, and Omer San. "PyDA: A Hands-On Introduction to Dynamical Data Assimilation with Python." Fluids 5, no. 4 (November 29, 2020): 225. http://dx.doi.org/10.3390/fluids5040225.
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Dynamic data assimilation offers a suite of algorithms that merge measurement data with numerical simulations to predict accurate state trajectories. Meteorological centers rely heavily on data assimilation to achieve trustworthy weather forecast. With the advance in measurement systems, as well as the reduction in sensor prices, data assimilation (DA) techniques are applicable to various fields, other than meteorology. However, beginners usually face hardships digesting the core ideas from the available sophisticated resources requiring a steep learning curve. In this tutorial, we lay out the mathematical principles behind DA with easy-to-follow Python module implementations so that this group of newcomers can quickly feel the essence of DA algorithms. We explore a series of common variational, and sequential techniques, and highlight major differences and potential extensions. We demonstrate the presented approaches using an array of fluid flow applications with varying levels of complexity.
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MAJDA, ANDREW J., YULONG XING, and MAJID MOHAMMADIAN. "Moist multi-scale models for the hurricane embryo." Journal of Fluid Mechanics 657 (June 30, 2010): 478–501. http://dx.doi.org/10.1017/s0022112010001515.
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Determining the finite-amplitude preconditioned states in the hurricane embryo, which lead to tropical cyclogenesis, is a central issue in contemporary meteorology. In the embryo there is competition between different preconditioning mechanisms involving hydrodynamics and moist thermodynamics, which can lead to cyclogenesis. Here systematic asymptotic methods from applied mathematics are utilized to develop new simplified moist multi-scale models starting from the moist anelastic equations. Three interesting multi-scale models emerge in the analysis. The balanced mesoscale vortex (BMV) dynamics and the microscale balanced hot tower (BHT) dynamics involve simplified balanced equations without gravity waves for vertical vorticity amplification due to moist heat sources and incorporate nonlinear advective fluxes across scales. The BMV model is the central one for tropical cyclogenesis in the embryo. The moist mesoscale wave (MMW) dynamics involves simplified equations for mesoscale moisture fluctuations, as well as linear hydrostatic waves driven by heat sources from moisture and eddy flux divergences. A simplified cloud physics model for deep convection is introduced here and used to study moist axisymmetric plumes in the BHT model. A simple application in periodic geometry involving the effects of mesoscale vertical shear and moist microscale hot towers on vortex amplification is developed here to illustrate features of the coupled multi-scale models. These results illustrate the use of these models in isolating key mechanisms in the embryo in a simplified content.
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Barsegian, Grigor A. "Turbulence of Real Functions." gmj 15, no. 2 (June 2008): 225–40. http://dx.doi.org/10.1515/gmj.2008.225.
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Abstract The concept of 𝐴-level sets of real functions 𝑢(𝑥, 𝑦) (i.e., the solutions of 𝑢(𝑥, 𝑦) = 𝐴 = const) in a given domain admits numerous interpretations in applied sciences: level sets are potential lines, streamlines in hydrodynamics, meteorology and electromagnetics, isobars in gas-dynamics, isotherms in thermodynamics, etc. In fact, the level sets of 𝑢 considered for all values 𝐴 make the “map” of this function and their interpretations in different sciences make the “maps” of the corresponding processes. In this paper we study the geometry of these maps for broad classes of functions and arbitrary values 𝐴. In particular, we study how much twisted or, speaking in general, how turbulent these maps are. The concepts and results admit some immediate interpretations and can be stated in terms of flow rotation and turbulence. The study gives a new, in fact a geometric description of these applied phenomena.
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Fakhruddin, Muhammad, Hangga Wicaksono, Fauzan Baananto, Hilmi Iman Firmansyah, Nurlia Pramita Sari, Mochamad Muzaki, Khelvindra Rizky Akbarsyah D, and Noveri Dwi Hardyanto. "OPTIMASI AERODINAMIKA BODI MOBIL HEMAT ENERGI KEN DEDES ELECTRIC EVO 3 MENGGUNAKAN METODE COMPUTATIONAL FLUID DYNAMICS (CFD)." Eksergi 17, no. 1 (January 24, 2021): 36. http://dx.doi.org/10.32497/eksergi.v17i1.2219.
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Aerodynamics is a branch of science that discusses the movement of an object in the air. Aerodynamics comes from the words aero = air and dynamics = force of motion. The study of air forces is a branch of fluid mechanics. This study is a continuation of the study of hydrodynamics, where the science of the motion of air has a close relationship with other sciences. Physics, mathematics, mechanics, meteorology and others are branches of science that are closely related to aerodynamics. Where in the science of aerodynamics, it discusses the principle of stationary air, specifically about the changes experienced by the air when there is a change in geometry. In this study, CFD analysis was carried out to inspect and optimize the airflow through the energy-efficient car body "Ken dedes Evo 3" Malang State Polytechnic to participate in energy-efficient car competitions by following the regulations and packaging requirements in energy-efficient car contests. The aerodynamic analysis of the energy-efficient car was carried out using the ANSYS simulation software. This aerodynamic research aims to reduce the drag coefficient and lift coefficient of energy-efficient cars. In the end, the energy-efficient car Ken Dedes Electric Evo 3 has an improved drag coefficient of 0.03 and a lift coefficient of 0.034. This is obtained from the simulation only on the car body.
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ALAM, MAHBOOB, and MOHD AMJAD. "A precipitation forecasting model using machine learning on big data in clouds environment." MAUSAM 72, no. 4 (November 1, 2021): 781–90. http://dx.doi.org/10.54302/mausam.v72i4.3546.
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Numerical weather prediction (NWP) has long been a difficult task for meteorologists. Atmospheric dynamics is extremely complicated to model, and chaos theory teaches us that the mathematical equations used to predict the weather are sensitive to initial conditions; that is, slightly perturbed initial conditions could yield very different forecasts. Over the years, meteorologists have developed a number of different mathematical models for atmospheric dynamics, each making slightly different assumptions and simplifications, and hence each yielding different forecasts. It has been noted that each model has its strengths and weaknesses forecasting in different situations, and hence to improve performance, scientists now use an ensemble forecast consisting of different models and running those models with different initial conditions. This ensemble method uses statistical post-processing; usually linear regression. Recently, machine learning techniques have started to be applied to NWP. Studies of neural networks, logistic regression, and genetic algorithms have shown improvements over standard linear regression for precipitation prediction. Gagne et al proposed using multiple machine learning techniques to improve precipitation forecasting. They used Breiman’s random forest technique, which had previously been applied to other areas of meteorology. Performance was verified using Next Generation Weather Radar (NEXRAD) data. Instead of using an ensemble forecast, it discusses the usage of techniques pertaining to machine learning to improve the precipitation forecast.
This paper is to present an approach for mapping of precipitation data. The project attempts to arrive at a machine learning method which is optimal and data driven for predicting precipitation levels that aids farmers thereby aiming to provide benefits to the agricultural domain.
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Read, P. L. "Raymond Hide. 17 May 1929—6 September 2016." Biographical Memoirs of Fellows of the Royal Society 67 (August 21, 2019): 191–215. http://dx.doi.org/10.1098/rsbm.2019.0016.
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Raymond Hide was a physicist who worked at the interfaces between fundamental hydrodynamics, magnetohydrodynamics (MHD), the geophysics of the Earth's interior, atmosphere and oceans and those of other planets. He received his PhD from the University of Cambridge, and spent the majority of his career at the Met Office and then the University of Oxford. In laboratory studies of sloping thermal convection carried out at Cambridge in the early 1950s he discovered various regimes of vacillation and other multiply-periodic intransitive flows as well as aperiodic flows, now recognized as a form of geostrophic turbulence. These findings influenced seminal mathematical studies of what came to be known as deterministic chaos, and provided a paradigm for interpreting large-scale flows in the atmospheres of the Earth and other planets. Related contributions include general theoretical results tested by crucial laboratory experiments on boundary layers, Taylor columns and detached shear layers. His contributions to MHD include the concepts of potential magnetic field and magnetic superhelicity. He also initiated research on the dynamo origin of the magnetic fields of Jupiter and other major planets and its implications for their internal structure and dynamics. His extensive research on fluctuations of the Earth's rotation led to new developments in areas as diverse as meteorology and climatology and studies of the structure and dynamics of the Earth's deep interior.
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Steinle, Peter, Chris Tingwell, and Sergei Soldatenko. "Observation Impact Assessment on the Prediction of the Earth System Dynamics Using the Adjoint-Based Method." SPIIRAS Proceedings 6, no. 61 (December 1, 2018): 5–28. http://dx.doi.org/10.15622/sp.61.1.
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Mathematical models of the Earth system and its components represent one of the most powerful and effective instruments applied to explore the Earth system's behaviour in the past and present, and to predict its future state considering external influence. These models are critically reliant on a large number of various observations (in situ and remotely sensed) since the prediction accuracy is determined by, amongst other things, the accuracy of the initial state of the system in question, which, in turn, is defined by observational data provided by many different instrument types. The development of an observing network is very costly, hence the estimation of the effectiveness of existing observation network and the design of a prospective one, is very important. The objectives of this paper are (1) to present the adjoint-based approach that allows us to estimate the impact of various observations on the accuracy of prediction of the Earth system and its components, and (2) to illustrate the application of this approach to two coupled low-order chaotic dynamical systems and to the ACCESS (Australian Community Climate and Earth System Simulator) global model used operationally in the Australian Bureau of Meteorology. The results of numerical experiments show that by using the adjoint-based method it is possible to rank the observations by the degree of their importance and also to estimate the influence of target observations on the quality of predictions.
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Grudzien, Colin, and Marc Bocquet. "A fast, single-iteration ensemble Kalman smoother for sequential data assimilation." Geoscientific Model Development 15, no. 20 (October 20, 2022): 7641–81. http://dx.doi.org/10.5194/gmd-15-7641-2022.
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Abstract. Ensemble variational methods form the basis of the state of the art for nonlinear, scalable data assimilation, yet current designs may not be cost-effective for real-time, short-range forecast systems. We propose a novel estimator in this formalism that is designed for applications in which forecast error dynamics is weakly nonlinear, such as synoptic-scale meteorology. Our method combines the 3D sequential filter analysis and retrospective reanalysis of the classic ensemble Kalman smoother with an iterative ensemble simulation of 4D smoothers. To rigorously derive and contextualize our method, we review related ensemble smoothers in a Bayesian maximum a posteriori narrative. We then develop and intercompare these schemes in the open-source Julia package DataAssimilationBenchmarks.jl, with pseudo-code provided for their implementations. This numerical framework, supporting our mathematical results, produces extensive benchmarks demonstrating the significant performance advantages of our proposed technique. Particularly, our single-iteration ensemble Kalman smoother (SIEnKS) is shown to improve prediction/analysis accuracy and to simultaneously reduce the leading-order computational cost of iterative smoothing in a variety of test cases relevant for short-range forecasting. This long work presents our novel SIEnKS and provides a theoretical and computational framework for the further development of ensemble variational Kalman filters and smoothers.
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Xu, X., and R. I. Nigmatulin. "On Linear Instability of Atmospheric Quasi-hydrostatic Equations in Response to Small Shortwave Perturbations." Lobachevskii Journal of Mathematics 42, no. 9 (September 2021): 2237–56. http://dx.doi.org/10.1134/s1995080221090298.
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Abstract A set of 3-dimensional atmospheric-dynamics equations with quasi-hydrostatic approximation is proposed and justified with the practical goal to optimize atmospheric modelling at scales ranging from meso meteorology to global climate. Sound waves are filtered by applying the quasi-hydrostatic approximation. In the closed system of hydro/thermodynamic equations, the inertial forces are negligibly small compared to gravity forces, and the asymptotically exact equation for vertical velocity is obtained. Investigation of the stability of solutions to this system in response to small shortwave perturbations has shown that solutions have the property of shortwave instability. There are situations when the increment of the perturbation amplitude tends to infinity, corresponding to absolute instability. It means that the Cauchy problem for such equations may be ill-posed. Its formulation can become conditionally correct if solutions are sought in a limited class of sufficiently smooth functions whose Fourier harmonics tend to zero reasonably quickly when the wavelengths of the perturbations approach zero. Thus, the numerical scheme for the quasi-hydrostatic equations using the finite-difference method requires an adequately selected pseudo-viscosity to eliminate the instability caused by perturbations with wavelengths of the order of the grid size. The result is useful for choosing appropriate vertical and horizontal grid sizes for modelling to avoid shortwave instability associated with the property of the system of equations. Implementation of pseudo-viscosities helps to smoothen or suppress the perturbations that occur during modelling.
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Darvishi, M., and G. Ahmadi. "Data assimilation techniques and modelling uncertainty in geosciences." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-2/W3 (October 22, 2014): 85–90. http://dx.doi.org/10.5194/isprsarchives-xl-2-w3-85-2014.
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"You cannot step into the same river twice". Perhaps this ancient quote is the best phrase to describe the dynamic nature of the earth system. If we regard the earth as a several mixed systems, we want to know the state of the system at any time. The state could be time-evolving, complex (such as atmosphere) or simple and finding the current state requires complete knowledge of all aspects of the system. On one hand, the Measurements (in situ and satellite data) are often with errors and incomplete. On the other hand, the modelling cannot be exact; therefore, the optimal combination of the measurements with the model information is the best choice to estimate the true state of the system. Data assimilation (DA) methods are powerful tools to combine observations and a numerical model. Actually, DA is an interaction between uncertainty analysis, physical modelling and mathematical algorithms. DA improves knowledge of the past, present or future system states. DA provides a forecast the state of complex systems and better scientific understanding of calibration, validation, data errors and their probability distributions. Nowadays, the high performance and capabilities of DA have led to extensive use of it in different sciences such as meteorology, oceanography, hydrology and nuclear cores. In this paper, after a brief overview of the DA history and a comparison with conventional statistical methods, investigated the accuracy and computational efficiency of two main classical algorithms of DA involving stochastic DA (BLUE and Kalman filter) and variational DA (3D and 4D-Var), then evaluated quantification and modelling of the errors. Finally, some of DA applications in geosciences and the challenges facing the DA are discussed.
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Situmbeko, Shadreck M., and Freddie L. Inambao. "System and component modelling of a low temperature solar thermal energy conversion cycle." Journal of Energy in Southern Africa 24, no. 4 (November 1, 2013): 51–62. http://dx.doi.org/10.17159/2413-3051/2013/v24i4a3146.
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Solar thermal energy (STE) technology refers to the conversion of solar energy to readily usable energy forms. The most important component of a STE technology is the collectors; these absorb the shorter wavelength solar energy (400-700nm) and convert it into usable, longer wavelength (about 10 times as long) heat energy. Depending on the quality (temperature and intensity) of the resulting thermal energy, further conversions to other energy forms such as electrical power may follow. Currently some high temperature STE technologies for electricity production have attained technical maturity; technologies such as parabolic dish (commercially available), parabolic trough and power tower are only hindered by unfavourable market factors including high maintenance and operating costs. Low temperature STEs have so far been restricted to water and space heating; however, owing to their lower running costs and almost maintenance free operation, although operating at lower efficiencies, may hold a key to future wider usage of solar energy. Low temperature STE conversion technology typically uses flat plate and low concentrating collectors such as parabolic troughs to harness solar energy for conversion to mechanical and/or electrical energy. These collector systems are relatively cheaper, simpler in construction and easier to operate due to the absence of complex solar tracking equipment. Low temperature STEs operate within temperatures ranges below 300oC. This research work is geared towards developing feasible low temperature STE conversion technology for electrical power generation. Preliminary small-scale concept plants have been designed at 500Wp and 10KWp. Mathematical models of the plant systems have been developed and simulated on the EES (Engineering Equation Solver) platform. Fourteen candidate working fluids and three cycle configurations have been analysed with the models. The analyses included a logic model selector through which an optimal conversion cycle configuration and working fluid mix was established. This was followed by detailed plant component modelling; the detailed component model for the solar field was completed and was based on 2-dimensional segmented thermal network, heat transfer and thermo fluid dynamics analyses. Input data such as solar insolation, ambient temperature and wind speed were obtained from the national meteorology databases. Detailed models of the other cycle components are to follow in next stage of the research. This paper presents findings of the system and solar field component.
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Frigo, Everton, Francesco Antonelli, Djeniffer S. S. da Silva, Pedro C. M. Lima, Igor I. G. Pacca, and José V. Bageston. "Effects of solar activity and galactic cosmic ray cycles on the modulation of the annual average temperature at two sites in southern Brazil." Annales Geophysicae 36, no. 2 (April 3, 2018): 555–64. http://dx.doi.org/10.5194/angeo-36-555-2018.
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Abstract. Quasi-periodic variations in solar activity and galactic cosmic rays (GCRs) on decadal and bidecadal timescales have been suggested as a climate forcing mechanism for many regions on Earth. One of these regions is southern Brazil, where the lowest values during the last century were observed for the total geomagnetic field intensity at the Earth's surface. These low values are due to the passage of the center of the South Atlantic Magnetic Anomaly (SAMA), which crosses the Brazilian territory from east to west following a latitude of ∼ 26∘. In areas with low geomagnetic intensity, such as the SAMA, the incidence of GCRs is increased. Consequently, possible climatic effects related to the GCRs tend to be maximized in this region. In this work, we investigate the relationship between the ∼ 11-year and ∼ 22-year cycles that are related to solar activity and GCRs and the annual average temperature recorded between 1936 and 2014 at two weather stations, both located near a latitude of 26∘ S but at different longitudes. The first of these stations (Torres – TOR) is located in the coastal region, and the other (Iraí – IRA) is located in the interior, around 450 km from the Atlantic Ocean. Sunspot data and the solar modulation potential for cosmic rays were used as proxies for the solar activity and the GCRs, respectively. Our investigation of the influence of decadal and bidecadal cycles in temperature data was carried out using the wavelet transform coherence (WTC) spectrum. The results indicate that periodicities of 11 years may have continuously modulated the climate at TOR via a nonlinear mechanism, while at IRA, the effects of this 11-year modulation period were intermittent. Four temperature maxima, separated by around 20 years, were detected in the same years at both weather stations. These temperature maxima are almost coincident with the maxima of the odd solar cycles. Furthermore, these maxima occur after transitions from even to odd solar cycles, that is, after some years of intense GCR flux. The obtained results offer indirect mathematical evidence that solar activity and GCR variations contributed to climatic changes in southern Brazil during the last century. A comparison of the results obtained for the two weather stations indicates that the SAMA also contributes indirectly to these temperature variations. The contribution of other mechanisms also related to solar activity cannot be excluded. Keywords. Meteorology and atmospheric dynamics (climatology)
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Eklund, Lina, Ole Magnus Theisen, Matthias Baumann, Andreas Forø Tollefsen, Tobias Kuemmerle, and Jonas Østergaard Nielsen. "Societal drought vulnerability and the Syrian climate-conflict nexus are better explained by agriculture than meteorology." Communications Earth & Environment 3, no. 1 (April 6, 2022). http://dx.doi.org/10.1038/s43247-022-00405-w.
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AbstractDroughts are often suspected to increase the risk of violent conflict through agricultural production shocks, and existing studies often explore these links through meteorological proxies. In Syria, an alleged agricultural collapse caused by drought is assumed to have contributed to increased migration and the conflict outbreak in 2011. Here we use satellite derived cropland and climate data to study land use dynamics in relation to drought and conflict in Syria. We show that claims of an agricultural collapse cannot be substantiated as croplands saw a fast recovery after the 2007–2009 drought. Our study highlights the importance of considering land-use dynamics for understanding linkages between meteorological droughts, agricultural impacts, migration and conflict. Furthermore, our results suggest that the influential drought-migration-conflict narrative for Syria needs to be reexamined, with implications for wider discussions of how climate change might alter conflict risk.
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Iskakova, N. B., А. S. Rysbek, and N. S. Serik. "APPROXIMATE SOLUTIONS OF SOME NONLINEAR PROBLEMS FOR THE MONGE-AMPERE EQUATIO." BULLETIN Series of Physics & Mathematical Sciences, March 10, 2020, 97–105. http://dx.doi.org/10.51889/2020-1.1728-7901.16.
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Due to numerous applications in various fields of science, including gas dynamics, meteorology, differential geometry, and others, the Monge – ampere equation is one of the most intensively studied equations of nonlinear mathematical physics.In this report, we study a nonlinear boundary value problem for the inhomogeneous Monge-ampere equation, the right part of which contains power nonlinearities in derivatives and arbitrary nonlinearity from the desired function.Based on linearization, the studied boundary value problems are reduced to a system of ordinary first-order differential equations with initial conditions that depend on the parameter.Methods for constructing exact and approximate solutions of some boundary value problems for the Monge-ampere equation are proposed.Using the Mathcad software package, numerical implementation of methods for constructing approximate solutions of the obtained systems of ordinary differential equations with a parameter is performed.Three-dimensional graphs of exact and approximate solutions of the problems under consideration in the Grafikus service are constructed.
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"Effectiveness of the Water Resources Use and Protection in Loriya Marza." Water sector of Russia: problems, technologies, management, no. 5, 2018 (2018). http://dx.doi.org/10.35567/1999-4508-2018-5-6.
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The paper analyzes the current use of the Loriya Marza water resources and the priorities of their protection. We used actual data of Ministry of Emergencies of Republic of Armenia «Service on Hydro/meteorology and Active Impact on Atmosphere Phenomena» and National Statistical Service, as well as all relevant researches, reports, legislative and regulatory acts and other materials. We used mathematical statistics methods, too. The conducted investigations/observations data has provided evidences of non-uniform spatial-temporal distribution of water resources in Loria Marza. Thus, the intensive irrigation period (July-September) coincides with the summer/fall low water period. When 15-20% of the annual flow passes through the rivers. In this period, the rivers contain very small quantity of water and in the case of mandatory water abstraction, their extinction is possible at some reaches, as a result, ecological balance of the aquatic ecosystem becomes disturbed. On the other hand, reduction of the runoff is observed on the territory under study during the low water period. As a rule, this period is characterized by the highest demand for water and this requires development of the concrete methods of water resources use regulation. To use and protect water resource effectively, it is necessary to know the dynamics of the runoff within-year and spatial distribution.