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Journal articles on the topic 'Weather Mathematical models'
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1
BISWAS, B. C. "Forecasting for agricultural application." MAUSAM 41, no. 2 (February 22, 2022): 188–93. http://dx.doi.org/10.54302/mausam.v41i2.2630.
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Methods for agro-meteorological forecasts are mainly based on crop-weather relationship and statistical/mathematical models. Models developed from historic data make it possible to obtain the expected values fairly in advance so that appropriate action may be taken to avail of beneficial aspect of weather and minimise or avoid detrimental effect. Validity of these models under different conditions is imperative as the climatic conditions of general field may be quite different from those of experimental one. This paper discusses the work done on the above aspects.
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Hewage, Pradeep, Ardhendu Behera, Marcello Trovati, Ella Pereira, Morteza Ghahremani, Francesco Palmieri, and Yonghuai Liu. "Temporal convolutional neural (TCN) network for an effective weather forecasting using time-series data from the local weather station." Soft Computing 24, no. 21 (April 23, 2020): 16453–82. http://dx.doi.org/10.1007/s00500-020-04954-0.
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Abstract Non-predictive or inaccurate weather forecasting can severely impact the community of users such as farmers. Numerical weather prediction models run in major weather forecasting centers with several supercomputers to solve simultaneous complex nonlinear mathematical equations. Such models provide the medium-range weather forecasts, i.e., every 6 h up to 18 h with grid length of 10–20 km. However, farmers often depend on more detailed short-to medium-range forecasts with higher-resolution regional forecasting models. Therefore, this research aims to address this by developing and evaluating a lightweight and novel weather forecasting system, which consists of one or more local weather stations and state-of-the-art machine learning techniques for weather forecasting using time-series data from these weather stations. To this end, the system explores the state-of-the-art temporal convolutional network (TCN) and long short-term memory (LSTM) networks. Our experimental results show that the proposed model using TCN produces better forecasting compared to the LSTM and other classic machine learning approaches. The proposed model can be used as an efficient localized weather forecasting tool for the community of users, and it could be run on a stand-alone personal computer.
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Jing, Li, Liu Ying-di, Li Hu-ming, and Chen Gong-xi. "Mathematical models for some ecophysiological characteristics in different weather condition in moss (Plagiomnium acutum)." Wuhan University Journal of Natural Sciences 5, no. 1 (March 2000): 123–26. http://dx.doi.org/10.1007/bf02828327.
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Pooja, S. B., and Siva R. V. Balan. "An Investigation Study on Clustering and Classification Techniques for Weather Forecasting." Journal of Computational and Theoretical Nanoscience 16, no. 2 (February 1, 2019): 417–21. http://dx.doi.org/10.1166/jctn.2019.7742.
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Weather forecasting is the prediction of atmosphere state for particular location by using principles of physics provided by many statistical and empirical techniques. Weather forecasts are frequently made by collecting quantitative data about current state of atmosphere through scientific understanding of atmospheric processes to illustrate how atmosphere changes in future. Current weather conditions are collected through the observation from the ground, ships, aircraft, radio sounds and satellites. The information is transmitted to the meteorological centers where the data are collected and examined for prediction. There are diverse techniques included in weather forecasting, from relatively simple observation of sky to complex computerized mathematical models. But, the existing techniques failed to predict the weather with higher accuracy and lesser time. In order to improve the prediction performance, the machine learning and ensemble techniques are introduced.
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Duan, Q., Z. Di, J. Quan, C. Wang, W. Gong, Y. Gan, A. Ye, et al. "Automatic Model Calibration: A New Way to Improve Numerical Weather Forecasting." Bulletin of the American Meteorological Society 98, no. 5 (May 1, 2017): 959–70. http://dx.doi.org/10.1175/bams-d-15-00104.1.
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Abstract Weather forecasting skill has been improved over recent years owing to advances in the representation of physical processes by numerical weather prediction (NWP) models, observational systems, data assimilation and postprocessing, new computational capability, and effective communications and training. There is an area that has received less attention so far but can bring significant improvement to weather forecasting—the calibration of NWP models, a process in which model parameters are tuned using certain mathematical methods to minimize the difference between predictions and observations. Model calibration of the NWP models is difficult because 1) there are a formidable number of model parameters and meteorological variables to tune, and 2) a typical NWP model is very expensive to run, and conventional model calibration methods require many model runs (up to tens of thousands) or cannot handle the high dimensionality of NWP models. This study demonstrates that a newly developed automatic model calibration platform can overcome these difficulties and improve weather forecasting through parameter optimization. We illustrate how this is done with a case study involving 5-day weather forecasting during the summer monsoon in the greater Beijing region using the Weather Research and Forecasting Model. The keys to automatic model calibration are to use global sensitivity analysis to screen out the most important parameters influencing model performance and to employ surrogate models to reduce the need for a large number of model runs. Through several optimization and validation studies, we have shown that automatic model calibration can improve precipitation and temperature forecasting significantly according to a number of performance measures.
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Du, Hailiang, and Leonard A. Smith. "Pseudo-Orbit Data Assimilation. Part II: Assimilation with Imperfect Models." Journal of the Atmospheric Sciences 71, no. 2 (January 31, 2014): 483–95. http://dx.doi.org/10.1175/jas-d-13-033.1.
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Abstract Data assimilation and state estimation for nonlinear models is a challenging task mathematically. Performing this task in real time, as in operational weather forecasting, is even more challenging as the models are imperfect: the mathematical system that generated the observations (if such a thing exists) is not a member of the available model class (i.e., the set of mathematical structures admitted as potential models). To the extent that traditional approaches address structural model error at all, most fail to produce consistent treatments. This results in questionable estimates both of the model state and of its uncertainty. A promising alternative approach is proposed to produce more consistent estimates of the model state and to estimate the (state dependent) model error simultaneously. This alternative consists of pseudo-orbit data assimilation with a stopping criterion. It is argued to be more efficient and more coherent than one alternative variational approach [a version of weak-constraint four-dimensional variational data assimilation (4DVAR)]. Results that demonstrate the pseudo-orbit data assimilation approach can also outperform an ensemble Kalman filter approach are presented. Both comparisons are made in the context of the 18-dimensional Lorenz96 flow and the two-dimensional Ikeda map. Many challenges remain outside the perfect model scenario, both in defining the goals of data assimilation and in achieving high-quality state estimation. The pseudo-orbit data assimilation approach provides a new tool for approaching this open problem.
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Bi, HuiJing, HongYan Zhang, YueMei Jiang, and XiLan Zhao. "A New Security Warning Model about Power Grid." MATEC Web of Conferences 160 (2018): 04005. http://dx.doi.org/10.1051/matecconf/201816004005.
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In recent years, lots of power grid blackouts at home and abroad indicates that the meteorological disasters caused by external meteorological conditions has gradually rose to major contradiction of power grid security. Basing on release information of meteorology, lightning monitoring information and power transmission equipment monitoring information, it established a weather warning model about power grid security, combined with real-time security analysis. Firstly, mathematical models of various types of weather conditions and weather risk assessment model grid were built, then actual operating conditions of a certain regional power grid and model results were compared, the comparison result prove the accuracy of the warning model, and provides a strong recommendation for decision-making.
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Ilie, Constantin Ovidiu, Dănuț Grosu, Oana Mocian, Radu Vilău, and Daniela Bartiș. "Using Statistically Based Modeling for Vehicle Dynamics." Advanced Materials Research 1036 (October 2014): 564–67. http://dx.doi.org/10.4028/www.scientific.net/amr.1036.564.
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This paper is a result of a research focused on statistical vehicles dynamics. Its main purpose is to establish mathematical description of vehicle dynamics based on statistically sufficient experimental data and using statistical instruments. The results are analytical expressions and graphical representations that can be used in situations other than those the data were obtained. Experimental research program objective was to obtain a variety of data to define the dynamics of a vehicle. It involved a large number of tests, more than 100, on different runways, pavement, mosaic tiles or asphalt. They were performed in various weather conditions, sunny and warm weather or rain or sleet and snow. The driving style varied between normal and sport ones. The experimental data were used in obtaining mathematical models that define certain dependency between dynamic parameters. There were issued multiple linear regressions with one resulting parameter. If we analyzed the models we issued we notice that the more factorial parameters are involved, the higher the accuracy of the model we get.
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Hjelkrem, Anne-Grete Roer, Andrea Ficke, Unni Abrahamsen, Ingerd Skow Hofgaard, and Guro Brodal. "Prediction of leaf Bloch disease risk in Norwegian spring wheat based on weather factors and host phenology." European Journal of Plant Pathology 160, no. 1 (February 17, 2021): 199–213. http://dx.doi.org/10.1007/s10658-021-02235-6.
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AbstractLeaf blotch diseases (LBD), such as Septoria nodorum bloch (Parastagnospora nodorum), Septoria tritici blotch (Zymoseptoria tritici) and Tan spot (Pyrenophora tritici-repentis) can cause severe yield losses (up to 50%) in Norwegian spring wheat (Triticum aestivum) and are mainly controlled by fungicide applications. A forecasting model to predict disease risk can be an important tool to optimize disease control. The association between specific weather variables and the development of LBD differs between wheat growth stages. In this study, a mathematical model to estimate phenological development of spring wheat was derived based on sowing date, air temperature and photoperiod. Weather factors associated with LBD severity were then identified for selected phenological growth stages by a correlation study of LBD severity data (17 years). Although information regarding host resistance and previous crop were added to the identified weather factors, two purely weather-based risk prediction models (CART, classification and regression tree algorithm) and one black box model (KNN, based on K nearest neighbor algorithm) were most accurate to predict moderate to high LBD severity (>5% infection). The predictive accuracy of these models (76–83%) was compared to that of two existing models used in Norway and Denmark (60 and 61% accuracy, respectively). The newly developed models performed better than the existing models, but still had the tendency to overestimate disease risk. Specificity of the new models varied between 49 and 74% compared to 40 and 37% for the existing models. These new models are promising decision tools to improve integrated LBD management of spring wheat in Norway.
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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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Robin, Libby. "Uncertain Seasons in the El Niño Continent: Local and Global Views." Australia, no. 28/3 (January 15, 2019): 7–19. http://dx.doi.org/10.7311/0860-5734.28.3.02.
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As global climate change shifts seasonal patterns, local and uncertain seasons of Australia have global relevance. Australia’s literature tracks extreme local weather events, exploring ‘slow catastrophes’ and ‘endurance.’ Humanists can change public policy in times when stress is a state of life, by reflecting on the psyches of individuals, rather than the patterns of the state. ‘Probable’ futures, generated by mathematical models that predict nature and economics, have little to say about living with extreme weather. Hope is not easily modelled. The frameworks that enable hopeful futures are qualitatively different. They can explore the unimaginable by offering an ‘interior apprehension.’
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Kudryashov, M. A., O. A. Belousov, V. I. Tetyukhin, M. M. Kiryupin, V. P. Belyaev, I. V. Nagornova, and E. G. Bezzateeva. "Development of antenna system for use in meteorological and climatic control complexes." Journal of Physics: Conference Series 2182, no. 1 (March 1, 2022): 012094. http://dx.doi.org/10.1088/1742-6596/2182/1/012094.
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Abstract The synthesis of a cylindrical antenna array for ground-based mobile weather monitoring complexes, based on quadrifilar radiators, is considered. The basic mathematical expressions for determining the electrodynamic characteristics of both the radiator from the antenna array and the AA itself are presented. Various phenomenological models of these radiators are considered. The approach to the synthesis of phenomenological models of the radiator and antenna array as a whole is described. The results of such synthesis are given, and the main characteristics and values of DG, CG, SWR, RP for the given type of radiator and AA in the corresponding frequency range are obtained. Techniques for using phenomenological models for operational synthesis of electrodynamic structures such as cylindrical AA and quadrifilar radiator are developed and described in detail. The possibility of applying this approach to the synthesis of this type of structures for radar weather monitoring systems is shown.
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Lindblom, E., K. V. Gernaey, M. Henze, and P. S. Mikkelsen. "Integrated modelling of two xenobiotic organic compounds." Water Science and Technology 54, no. 6-7 (September 1, 2006): 213–21. http://dx.doi.org/10.2166/wst.2006.620.
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This paper presents a dynamic mathematical model that describes the fate and transport of two selected xenobiotic organic compounds (XOCs) in a simplified representation of an integrated urban wastewater system. A simulation study, where the xenobiotics bisphenol A and pyrene are used as reference compounds, is carried out. Sorption and specific biological degradation processes are integrated with standardised water process models to model the fate of both compounds. Simulated mass flows of the two compounds during one dry weather day and one wet weather day are compared for realistic influent flow rate and concentration profiles. The wet weather day induces resuspension of stored sediments, which increases the pollutant load on the downstream system. The potential of the model to elucidate important phenomena related to origin and fate of the model compounds is demonstrated.
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Tulunay, Yurdanur, and Ersin Tulunay. "METU Data Driven Forecast Models: From the Window of Space Weather IAU Symposium 335." Proceedings of the International Astronomical Union 13, S335 (July 2017): 328–30. http://dx.doi.org/10.1017/s1743921318000613.
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AbstractSpace weather processes, in general, are non-linear and time-varying. In such cases ‘data driven models’ such as Neural Network, Fuzzy Logic and Genetic Algorithm based models were proved promising to be used in parallel with the mathematical models based on first physical principles. In particular, with the recent developments in ‘big data’ systems, one of the urgent issues is the development of new signal processing techniques to extract manageable, representative data out of the ‘relevant big data’ to be employed in ‘training’, ‘testing’ and validation phases of model construction. Since 1990, under the EU Frame Work Program Actions, we have developed such models for nowcasting, forecasting, warning and also for filling the data gaps on space weather cases including prediction of orbital spacecraft parameters. In particular, some typical, illustrative examples include the forecasting of the ionospheric critical frequencies foF2, during disturbed conditions, such as solar storms and extreme events; GPS total electon content(TEC); solar flare index during solar maximum and the construction of solar EUV flux variations. The associated input data organisation and the typical errors which have been within the acceptable operational expectations are summarised in terms of absolute values, percent and RMS. The aim of the paper is to show that the data driven approaches are promising for the forecasting of space weather.
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Afrifa, Stephen, Tao Zhang, Peter Appiahene, and Vijayakumar Varadarajan. "Mathematical and Machine Learning Models for Groundwater Level Changes: A Systematic Review and Bibliographic Analysis." Future Internet 14, no. 9 (August 30, 2022): 259. http://dx.doi.org/10.3390/fi14090259.
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With the effects of climate change such as increasing heat, higher rainfall, and more recurrent extreme weather events including storms and floods, a unique approach to studying the effects of climatic elements on groundwater level variations is required. These unique approaches will help people make better decisions. Researchers and stakeholders can attain these goals if they become familiar with current machine learning and mathematical model approaches to predicting groundwater level changes. However, descriptions of machine learning and mathematical model approaches for forecasting groundwater level changes are lacking. This study picked 117 papers from the Scopus scholarly database to address this knowledge gap. In a systematic review, the publications were examined using quantitative and qualitative approaches, and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was chosen as the reporting format. Machine learning and mathematical model techniques have made significant contributions to predicting groundwater level changes, according to the study. However, the domain is skewed because machine learning has been more popular in recent years, with random forest (RF) methods dominating, followed by the methods of support vector machine (SVM) and artificial neural network (ANN). Machine learning ensembles have also been found to help with aspects of computational complexity, such as performance and training times. Furthermore, compared to mathematical model techniques, machine learning approaches achieve higher accuracies, according to our research. As a result, it is advised that academics employ new machine learning techniques while also considering mathematical model approaches to predicting groundwater level changes.
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Li, Yuanfei, and Peng Zeng. "Continuous Dependence on the Heat Source of 2D Large-Scale Primitive Equations in Oceanic Dynamics." Symmetry 13, no. 10 (October 18, 2021): 1961. http://dx.doi.org/10.3390/sym13101961.
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In this paper, we consider the initial-boundary value problem for the two-dimensional primitive equations of the large-scale oceanic dynamics. These models are often used to predict weather and climate change. Using the differential inequality technique, rigorous a priori bounds of solutions and the continuous dependence on the heat source are established. We show the application of symmetry in mathematical inequalities in practice.
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Samuel, Mbelle Bisong, Paune Felix, Youmene Nongosso Miguel, Tambere Samam Cyrille, and Pierre Kisito Talla. "STUDY AND SIMULATION OF THE FUEL CONSUMPTION OF A VEHICLE WITH RESPECT TO AMBIENT TEMPERATURE AND WEATHER CONDITIONS." International Journal of Engineering Technologies and Management Research 7, no. 1 (January 31, 2020): 24–35. http://dx.doi.org/10.29121/ijetmr.v7.i1.2020.480.
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The consumption of fuel in vehicles depends on many factors such as the state of the roads, the state of the engine and the driver’s behavior. A mathematical model for evaluating vehicle fuel consumption on a 100 km interval at standard operating weather conditions was developed. This mathematical model developed took into consideration many factors, but the main factors were those related to weather conditions and temperature. Here a new simulation program for determining the influence of temperature and weather conditions on fuel consumption is built using the software Matlab. For efficient simulations the model uses a set of data for an SUV and then makes varying only the parameters that are related to weather and temperature for the simulation. During the simulation process, a set of 10 vehicle models and 8 roads conditions were chosen to run down the simulations and only the parameters of temperature, the drag coefficient and coefficient of rolling resistances respectively were subjected to variations during each of the simulations. Upon simulation, different results were obtained for the different parameters considered. For every 15% drop in temperature, 0.1litre, 0.12litre and 0.04litre increase in fuel consumption for the set of parameters chosen was noticed. These results were analyzed and interpreted with the help of Microsoft Excel and were found to be satisfactory given that it permits manufacturers and car users to have a notion of the impact of ambient temperature and weather conditions on fuel consumption, thereby promoting optimum usage of fuel, hence reducing the effect of greenhouse emissions in the atmosphere.
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SHAPOVALOV, Vitaly A., Aida A. ADZHIEVA, Lyudmila M. FEDCHENKO, and Egor A. KOVALEV. "Mathematical Modeling of Formation of Transparency Regions in Supercooled Stratiform Clouds and Fogs." Journal of Environmental Management and Tourism 9, no. 1 (June 19, 2018): 17. http://dx.doi.org/10.14505//jemt.v9.1(25).03.
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We developed models of active influence on clouds using crystallization reagents to ensure transparency of the atmosphere. Numerical modeling of various versions of influence on stratiform clouds at aviation seeding was performed. Variation of characteristics of supercooled fogs when bringing man-made crystals was studied. The determination of reagents application rates, estimating impact effect and some other issues were solved using the results of modelling of clouds evolution (both natural and under active influence). Based on generalization of the results of numerical simulation of cloud evolution, the proposals for improvement of cloud seeding technology under different weather conditions are developed.
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Brentan, B. M., G. Meirelles, M. Herrera, E. Luvizotto, and J. Izquierdo. "Correlation Analysis of Water Demand and Predictive Variables for Short-Term Forecasting Models." Mathematical Problems in Engineering 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/6343625.
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Operational and economic aspects of water distribution make water demand forecasting paramount for water distribution systems (WDSs) management. However, water demand introduces high levels of uncertainty in WDS hydraulic models. As a result, there is growing interest in developing accurate methodologies for water demand forecasting. Several mathematical models can serve this purpose. One crucial aspect is the use of suitable predictive variables. The most used predictive variables involve weather and social aspects. To improve the interrelation knowledge between water demand and various predictive variables, this study applies three algorithms, namely, classical Principal Component Analysis (PCA) and machine learning powerful algorithms such as Self-Organizing Maps (SOMs) and Random Forest (RF). We show that these last algorithms help corroborate the results found by PCA, while they are able to unveil hidden features for PCA, due to their ability to cope with nonlinearities. This paper presents a correlation study of three district metered areas (DMAs) from Franca, a Brazilian city, exploring weather and social variables to improve the knowledge of residential demand for water. For the three DMAs, temperature, relative humidity, and hour of the day appear to be the most important predictive variables to build an accurate regression model.
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Majda, Andrew J., and Samuel N. Stechmann. "Stochastic models for convective momentum transport." Proceedings of the National Academy of Sciences 105, no. 46 (November 17, 2008): 17614–19. http://dx.doi.org/10.1073/pnas.0806838105.
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The improved parameterization of unresolved features of tropical convection is a central challenge in current computer models for long-range ensemble forecasting of weather and short-term climate change. Observations, theory, and detailed smaller-scale numerical simulations suggest that convective momentum transport (CMT) from the unresolved scales to the resolved scales is one of the major deficiencies in contemporary computer models. Here, a combination of mathematical and physical reasoning is utilized to build simple stochastic models that capture the significant intermittent upscale transports of CMT on the large scales due to organized unresolved convection from squall lines. Properties of the stochastic model for CMT are developed below in a test column model environment for the large-scale variables. The effects of CMT from the stochastic model on a large-scale convectively coupled wave in an idealized setting are presented below as a nontrivial test problem. Here, the upscale transports from stochastic effects are significant and even generate a large-scale mean flow which can interact with the convectively coupled wave.
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Soldatenko, Sergei A., and Rafael M. Yusupov. "The Determination of Feasible Control Variables for Geoengineering and Weather Modification Based on the Theory of Sensitivity in Dynamical Systems." Journal of Control Science and Engineering 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/1547462.
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Geophysical cybernetics allows for exploring weather and climate modification (geoengineering) as an optimal control problem in which the Earth’s climate system is considered as a control system and the role of controller is given to human operators. In mathematical models used in climate studies control actions that manipulate the weather and climate can be expressed via variations in model parameters that act as controls. In this paper, we propose the “instability-sensitivity” approach that allows for determining feasible control variables in geoengineering. The method is based on the sensitivity analysis of mathematical models that describe various types of natural instability phenomena. The applicability of this technique is illustrated by a model of atmospheric baroclinic instability since this physical mechanism plays a significant role in the general circulation of the atmosphere and, consequently, in climate formation. The growth rate of baroclinic unstable waves is taken as an indicator of control manipulations. The information obtained via calculated sensitivity coefficients is very beneficial for assessing the physical feasibility of methods of control of the large-scale atmospheric dynamics and for designing optimal control systems for climatic processes. It also provides insight into potential future changes in baroclinic waves, as a result of a changing climate.
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Wang, Q. J., Tony Zhao, Qichun Yang, and David Robertson. "A Seasonally Coherent Calibration (SCC) Model for Postprocessing Numerical Weather Predictions." Monthly Weather Review 147, no. 10 (September 24, 2019): 3633–47. http://dx.doi.org/10.1175/mwr-d-19-0108.1.
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Abstract Statistical calibration of forecasts from numerical weather prediction (NWP) models aims to produce forecasts that are unbiased, reliable in ensemble spread, and as skillful as possible. We suggest that the calibrated forecasts should also be coherent in climatology, including seasonality, consistent with observations. This is especially important when forecasts approach climatology as forecast skill becomes low, such as at long lead times. However, it is challenging to achieve these aims when data available to establish sophisticated calibration models are limited. Many NWP models have only a short period of archived data, typically one year or less, when they become officially operational. In this paper, we introduce a seasonally coherent calibration (SCC) model for working effectively with limited archived NWP data. Detailed rationale and mathematical formulations are presented. In the development of the model, three issues are resolved. These are 1) constructing a calibration model that is sophisticated enough to allow for seasonal variation in the statistical characteristics of raw forecasts and observations, 2) bringing climatology that is representative of long-term statistics into the calibration model, and 3) reducing the number of model parameters through sensible reparameterization to make the model workable with short NWP dataset. A case study is conducted to examine model assumptions and evaluate model performance. We find that the model assumptions are sound, and the developed SCC model produces well-calibrated forecasts.
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Czaplewski, Krzysztof, and Piotr Zwolan. "A Vessel's Mathematical Model and its Real Counterpart: A Comparative Methodology Based on a Real-world Study." Journal of Navigation 69, no. 6 (May 3, 2016): 1379–92. http://dx.doi.org/10.1017/s0373463316000230.
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Navigation and manoeuvring simulators are increasingly being used in research centres to conduct complex navigation experiments and analyses. The structure and capabilities of simulation software make it possible to reproduce any conditions, including weather conditions. The complex mathematical models of marine environmental conditions that are being implemented nowadays take into account various sea wave models, which makes simulation tests more realistic. This paper deals with issues related to evaluating and verifying vessel simulation models based on real-world studies. As a result of the present research project, a methodology for comparing vessel simulation models with their real-life counterparts was developed. A measurement platform was created for the purpose of carrying out real-world studies; it is available at the Institute of Marine Navigation and Hydrography of the Polish Naval Academy. One important research step involved developing a procedural algorithm for making real-world measurements. This paper presents the results of using this platform in comparative tests of the manoeuvring elements of real and simulated vessels.
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Vasilevich and Nikanorova. "ANALYTICAL MATHEMATICAL MODELS OF THE POPULATION OF ARTHROPODS IN THE NON-BLACK EARTH ZONE." THEORY AND PRACTICE OF PARASITIC DISEASE CONTROL, no. 22 (May 19, 2021): 128–32. http://dx.doi.org/10.31016/978-5-6046256-1-3.2021.22.128-132.
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The article provides an example of mathematical analytical modeling of the population size of blood-sucking arthropods on the example of mosquitoes and ixodid ticks that inhabit the Kaluga Region. The presented analytical mathematical models make it possible to clearly assess the influence of environmental factors on parasite populations. The following factors were taken into account: average temperature (monthly and yearly, t, oС); average precipitation (monthly and yearly, S, mm); mean atmospheric pressure (P, mm Hg) for mosquitoes, and monthly average temperature (t, o С), monthly mean relative humidity (Q, %), and mean atmospheric pressure (P, mm Hg) for ixodid ticks. The analysis of the obtained models shows that under weather conditions when monthly mean values of the considered factors are at a zero level, the estimated number of ixodid ticks and mosquitoes will be 1150 and 1529 individuals in the control area per year. The population of ixodid ticks is most significantly influenced by the mean atmospheric pressure; its influence is twice as strong as monthly mean humidity and 6.4 times stronger than the influence of monthly average temperature. The "+" sign indicates that the higher the atmospheric pressure is, the more active ticks are observed. Monthly average precipitation has the greatest effect on the mosquito population.
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Szabolcsi, Róbert. "Numerical Analyis of the Low-Altitude Air Turbulence Mathematical Models Used in Modelling of the Spatial Motion of the Small Unmanned Aerial Vehicles." International conference KNOWLEDGE-BASED ORGANIZATION 23, no. 3 (June 27, 2017): 120–30. http://dx.doi.org/10.1515/kbo-2017-0165.
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Abstract Unmanned Aerial Vehicles designed and manufactured often with unknown origin but available in sale of the model aircraft market rarely deals with weather circumstances and conditions defining clearances for safe air operations of the UAV. UAVs used in low-altitude flight missions are often threatened by atmospheric turbulences leading either to high angle-of-attack (AoA) or leading to the stall of the UAV. There are many mathematical models well-known and widely applied in piloted aircraft aviation when to simulate atmospheric turbulences affecting spatial motion of the aircraft. This paper targets to evaluate and simulate numerically the low-altitude air turbulences, and, to examine the vertical gust speed of the small UAV. The UAV behaviour examined numerically will support to find weather clearances ensuring UAV flight safety having the level equal or higher to that level of manned aircraft regulated well-before. A computer code in MATLAB environment is created to support numerical analysis of the small UAV behaviour in low altitude atmospheric turbulence.
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Zukowski, Miroslaw, and Walery Jezierski. "New Deterministic Mathematical Model for Estimating the Useful Energy Output of a Medium-Sized Solar Domestic Hot Water System." Energies 14, no. 10 (May 11, 2021): 2753. http://dx.doi.org/10.3390/en14102753.
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According to the authors of this paper, the mathematical point of view allows us to see what sometimes cannot be seen from the designer’s point of view. The aim of this study was to estimate the influence of the most important parameters (volume of heat storage tanks, daily consumption of domestic hot water, optical efficiency, heat loss coefficient, and total area of a solar collector) on the thermal power output of solar domestic hot water (SDHW) system in European climatic conditions. Three deterministic mathematical models of these relationships for Madrid, Budapest, and Helsinki were created. The database for the development of these models was carried out using computer simulations made in the TRNSYS software environment. The SDHW system located at the Bialystok University of Technology (Poland) was the source of the measurement results used to validate the simulation model. The mathematical optimization procedure showed that the maximum annual useful energy output that can be obtained from 1 m2 of gross collector area is 1303 kWh in the case of Madrid, 918.5 kWh for Budapest, and 768 kWh for Helsinki weather conditions.
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Adekunle, Timothy Oluseun. "Summer performance, comfort, and heat stress in structural timber buildings under moderate weather conditions." Smart and Sustainable Built Environment 8, no. 3 (July 3, 2019): 220–42. http://dx.doi.org/10.1108/sasbe-11-2018-0059.
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Purpose The purpose of this paper is to examine the Summer performance, comfort, and heat stress in structural timber buildings. The research utilises building simulation as a tool to investigate the performance of the case study buildings under non-extreme weather conditions. Design/methodology/approach The research explores three UK sites using the test reference year (TRY) weather files for the current and future weather conditions. The study focuses on the Summer performance and heat stress in non-extreme weather conditions; therefore, the Design Summer Year (DSY) weather files are not used for the simulations. The simulation data are calibrated and validated using the measured data from the field study. Findings The results revealed the mean predicted temperatures varied from 20.2–20.8°C for the 2000s. The mean temperatures for the 2030s ranged from 23.1 to 24.2°C. Higher temperatures are predicted at the buildings in the Southeast site than the Midlands and the Northwest sites. The results revealed that there is no significant improvement in the thermal environment when the floor area and the floor-to-ceiling height are increased. However, the study showed that the integration of different design interventions can improve the future performance and resilience of the buildings in various weather conditions. Research limitations/implications By applying the wet-bulb globe temperature (WBGT) and the Universal Thermal Comfort Index (UTCI) mathematical models to calculate the heat stress at the buildings, the study proposes the WBGT of 20.0°C and the UTCI of 24.1°C as possible heat stress indicators for occupants of the buildings in the 2030s. Practical implications On the one hand, the results revealed the maximum temperatures in some of the case study buildings exceed the comfort threshold (28°C). On the other hand, the study showed that occupants of the buildings are not prone to extreme Summertime overheating and heat stress under moderate weather conditions. However, different outcomes may be predicted if DSY weather files for the selected sites are considered. Originality/value This study is the first reported work to explore building simulation and mathematical equations to investigate Summer performance, comfort and heat stress indexes in timber buildings under moderate weather conditions in different regional sites in the UK.
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Li, Ke Hua, Jin Yong Yu, and Jun Wei Lei. "Research on Modeling and Simulation of Sonar Performance Using Simulink." Applied Mechanics and Materials 138-139 (November 2011): 804–9. http://dx.doi.org/10.4028/www.scientific.net/amm.138-139.804.
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With respect to different weather and sea conditions, the acoustic model under ocean environment was proposed by analyzing the interaction characteristics between sonar and environment. Based on the sonar equation, the performance mathematical models of active and passive sonar were produced with considering acoustic characteristics of the target and parameters of the marine environment. Simulation results of target detection performance for different target types, sonar parameters and sea conditions show the validity of the proposed model.
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Sednev, I., and S. Menon. "Analyzing numerics of bulk microphysics schemes in community models: warm rain processes." Geoscientific Model Development 5, no. 4 (August 3, 2012): 975–87. http://dx.doi.org/10.5194/gmd-5-975-2012.
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Abstract. Implementation of bulk cloud microphysics (BLK) parameterizations in atmospheric models of different scales has gained momentum in the last two decades. Utilization of these parameterizations in cloud-resolving models when timesteps used for the host model integration are a few seconds or less is justified from the point of view of cloud physics. However, mechanistic extrapolation of the applicability of BLK schemes to the regional or global scales and the utilization of timesteps of hundreds up to thousands of seconds affect both physics and numerics. We focus on the mathematical aspects of BLK schemes, such as stability and positive-definiteness. We provide a strict mathematical definition for the problem of warm rain formation. We also derive a general analytical condition (SM-criterion) that remains valid regardless of parameterizations for warm rain processes in an explicit Eulerian time integration framework used to advanced finite-difference equations, which govern warm rain formation processes in microphysics packages in the Community Atmosphere Model and the Weather Research and Forecasting model. The SM-criterion allows for the existence of a unique positive-definite stable mass-conserving numerical solution, imposes an additional constraint on the timestep permitted due to the microphysics (like the Courant-Friedrichs-Lewy condition for the advection equation), and prohibits use of any additional assumptions not included in the strict mathematical definition of the problem under consideration. By analyzing the numerics of warm rain processes in source codes of BLK schemes implemented in community models we provide general guidelines regarding the appropriate choice of time steps in these models.
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Olinda, R. A., J. Blanchet, C. A. C. dos Santos, V. A. Ozaki, and P. J. Ribeiro Jr. "Spatial extremes modeling applied to extreme precipitation data in the state of Paraná." Hydrology and Earth System Sciences Discussions 11, no. 11 (November 17, 2014): 12731–64. http://dx.doi.org/10.5194/hessd-11-12731-2014.
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Abstract. Most of the mathematical models developed for rare events are based on probabilistic models for extremes. Although the tools for statistical modeling of univariate and multivariate extremes are well developed, the extension of these tools to model spatial extremes includes an area of very active research nowadays. A natural approach to such a modeling is the theory of extreme spatial and the max-stable process, characterized by the extension of infinite dimensions of multivariate extreme value theory, and making it possible then to incorporate the existing correlation functions in geostatistics and therefore verify the extremal dependence by means of the extreme coefficient and the Madogram. This work describes the application of such processes in modeling the spatial maximum dependence of maximum monthly rainfall from the state of Paraná, based on historical series observed in weather stations. The proposed models consider the Euclidean space and a transformation referred to as space weather, which may explain the presence of directional effects resulting from synoptic weather patterns. This method is based on the theorem proposed for de Haan and on the models of Smith and Schlather. The isotropic and anisotropic behavior of these models is also verified via Monte Carlo simulation. Estimates are made through pairwise likelihood maximum and the models are compared using the Takeuchi Information Criterion. By modeling the dependence of spatial maxima, applied to maximum monthly rainfall data from the state of Paraná, it was possible to identify directional effects resulting from meteorological phenomena, which, in turn, are important for proper management of risks and environmental disasters in countries with its economy heavily dependent on agribusiness.
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Rodriguez, Ricardo Oses, Claudia Oses Llanes, and Rigoberto Fimia Duarte. "How the Chaos Theory is Defeated in the Yabu Meteorological Station, Cuba." Journal of Biomedical Research & Environmental Sciences 2, no. 10 (October 2021): 1059–66. http://dx.doi.org/10.37871/jbres1348.
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In this work, 8 weather variables were modeled at the Yabu meteorological station, Cuba, a daily database from the Yabu meteorological station, Cuba, of extreme temperatures, extreme humidity and their average value, precipitation, was used. The force of the wind and the cloudiness corresponding to the period from 1977 to 2021, a linear mathematical model is obtained through the methodology of Regressive Objective Regression (ROR) for each variable that explains their behavior, depending on these 15, 13, 10 and 8 years in advance. It is concluded that these models allow the long-term forecast of the weather, opening a new possibility for the forecast, concluding that the chaos in time can be overcome if this way of predicting is used, the calculation of the mean error regarding the forecast of persistence in temperatures, wind force and cloud cover, while the persistence model is better in humidity, this allows to have valuable information in the long term of the weather in a locality, which results in a better decision making in the different aspects of the economy and society that are impacted by the weather forecast. It is the first time that an ROR model has been applied to the weather forecast processes for a specific day 8, 10, 13 and 15 years in advance.
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Rodríguez, Ricardo Osés. "Chaos Theory of Mathematics as seen from a New Perspective for Weather Forecasting." Bioscience Biotechnology Research Communications 15, no. 3 (September 30, 2022): 390–98. http://dx.doi.org/10.21786/bbrc/15.3.4.
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In this work, 8 meteorological variables were modeled in the Yabú station, Cuba, for which the daily database of this meteorological station was used, where the meteorological variables were taken into account are: extreme temperatures, extreme humidity and its average value, precipitation, wind force and cloudiness corresponding to the period 1977 to 2021. A linear mathematical model was obtained using the Objective Regressive Regression (ORR) methodology for each variable, which explains its behavior according to these variables, 15, 13, 10 and 8 years in advance. The calculation of the mean error with respect to the persistence forecast in temperatures, wind strength and cloudiness, as well as the persistence model was better with respect to humidity, this allows having valuable long-term information of the weather in a locality, which results in better decision making in the different aspects of the economy and society that are impacted by the weather forecast. It is concluded that these models allow long-term weather forecasting, opening a new possibility for forecasting, so that weather chaos can be overcome if this way of forecasting is used; moreover, it is the first time that an ORR model is applied to weather forecasting processes for a specific day so many years in advance.
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Ivanova, Iryna, Maryna Serdiuk, Vira Malkina, Iryna Bandura, Ihor Kovalenko, Tetiana Tymoshchuk, Oksana Tonkha, Oleksandr Tsyz, Mikhailo Mushtruk, and Alina Omelian. "The study of soluble solids content accumulation dynamics under the influence of weather factors in the fruits of cherries." Potravinarstvo Slovak Journal of Food Sciences 15 (April 28, 2021): 350–59. http://dx.doi.org/10.5219/1554.
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High tasting assessment of the fruit of sweet cherry is due to the favorable soluble solids content (SSC). The weather parameters and varietal features during the formation of fruit have the dominant influence on the accumulation of soluble solids. This issue has gained new relevance in the context of global climate change. The research aimed to develop a dependence of the accumulation of soluble solids of the various sorts of sweet cherries on the weather conditions of the South Steppe zone of Ukraine. Statistical analysis of the values of soluble solids in sweet cherry fruit was performed according to the average indicators of three groups of cultivars. To achieve this goal, the laboratory, factor, correlation, and regression analyses were carried out. The mathematical model was built with the application of factor and regression analysis methods, with the principal component analysis being used. The factor and regression analysis methods became the basis for the linear regression model of dependence of SSC fund accumulation on the influence of climatic parameters for the cultivar types of the three ripening terms. Based on the constructed regression models, we analyzed the degree of influence of the weather parameters on the SSC indicator by calculating the coefficients of Δi relative influence. The largest influence was set for the group of temperature and humidity parameters with the maximum share of Δi ≥9.50%. It was mathematically substantiated that the weather parameters of the last month of fruit formation had the greatest influence on the accumulation of SSC in the sweet cherry fruit, regardless of the period of ripening. For early and medium ripening sweet cherries, those were the weather parameters for May, and for those of late-term of ripening June parameters were of the maximum value.
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Candela, Angela, Gabriele Freni, Giorgio Mannina, and Gaspare Viviani. "Receiving water body quality assessment: an integrated mathematical approach applied to an Italian case study." Journal of Hydroinformatics 14, no. 1 (April 23, 2011): 30–47. http://dx.doi.org/10.2166/hydro.2011.099.
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This study presents a basin-scale approach to the analysis of receiving water body quality considering both point and non-point pollution sources. In particular, this paper describes an extensive data gathering campaign carried out in the Nocella catchment, which is an agricultural and semi-urbanised basin located in Sicily, Italy. Two sewer systems, two wastewater treatment plants and a river reach were monitored during both dry and wet weather periods. A mathematical model of the entire integrated system was also created. Specifically, a detailed modelling approach was developed by employing three well known models: Storm Water Management Model, GPS-X and Soil and Water Assessment Tool. The study proposed a comprehensive modelling approach to analyse the importance of diffuse and concentrated polluting sources on receiving water quality. The study demonstrated that point pollution loads can be more influential during wet periods by an order of magnitude compared with the dry weather period. In the long term, diffuse and point pollution sources were demonstrated to affect river quality and they have both to be considered. The use of the proposed integrated model-based approach may support water managers in decision making about which strategies should be preferred with the aim of water quality preservation.
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Zhu, Tao. "Research and Simulation of Optical Measurement Model in Chemical Pollution of Water." Applied Mechanics and Materials 644-650 (September 2014): 1042–45. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.1042.
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Currently, the chemical pollution of water detection is mainly based on optical method. However, the optical detector is easily restricted by light and weather conditions. Optical measurement can be set on the target feature point, such as the laser reflection apparatus in laser measurement. This article applied the optical measurement method to research detection of water chemical pollution. Use theoretical and mathematical models of optical measurement method to perform the detection of chemical contamination in water. Optical measurement method is the trend of future development.
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Chen, Xin, Prateek Bahl, Charitha de Silva, David Heslop, Con Doolan, Samsung Lim, and C. Raina MacIntyre. "Systematic Review and Evaluation of Mathematical Attack Models of Human Inhalational Anthrax for Supporting Public Health Decision Making and Response." Prehospital and Disaster Medicine 35, no. 4 (June 4, 2020): 412–19. http://dx.doi.org/10.1017/s1049023x20000734.
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AbstractBackground:Anthrax is a potential biological weapon and can be used in an air-borne or mail attack, such as in the attack in the United States in 2001. Planning for such an event requires the best available science. Since large-scale experiments are not feasible, mathematical modelling is a crucial tool to inform planning. The aim of this study is to systematically review and evaluate the approaches to mathematical modelling of inhalational anthrax attack to support public health decision making and response.Methods:A systematic review of inhalational anthrax attack models was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. The models were reviewed based on a set of defined criteria, including the inclusion of atmospheric dispersion component and capacity for real-time decision support.Results:Of 13 mathematical modelling studies of human inhalational anthrax attacks, there were six studies that took atmospheric dispersion of anthrax spores into account. Further, only two modelling studies had potential utility for real-time decision support, and only one model was validated using real data.Conclusion:The limited modelling studies available use widely varying methods, assumptions, and data. Estimation of attack size using different models may be quite different, and is likely to be under-estimated by models which do not consider weather conditions. Validation with available data is crucial and may improve models. Further, there is a need for both complex models that can provide accurate atmospheric dispersion modelling, as well as for simpler modelling tools that provide real-time decision support for epidemic response.
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Wakeham, Alison J., Gary Keane, and Roy Kennedy. "Field Evaluation of a Competitive Lateral-Flow Assay for Detection of Alternaria brassicae in Vegetable Brassica Crops." Plant Disease 100, no. 9 (September 2016): 1831–39. http://dx.doi.org/10.1094/pdis-10-15-1211-re.
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On-site detection of inoculum of polycyclic plant pathogens could potentially contribute to management of disease outbreaks. A 6-min, in-field competitive immunochromatographic lateral flow device (CLFD) assay was developed for detection of Alternaria brassicae (the cause of dark leaf spot in brassica crops) in air sampled above the crop canopy. Visual recording of the test result by eye provides a detection threshold of approximately 50 dark leaf spot conidia. Assessment using a portable reader improved test sensitivity. In combination with a weather-driven infection model, CLFD assays were evaluated as part of an in-field risk assessment to identify periods when brassica crops were at risk from A. brassicae infection. The weather-driven model overpredicted A. brassicae infection. An automated 7-day multivial cyclone air sampler combined with a daily in-field CLFD assay detected A. brassicae conidia air samples from above the crops. Integration of information from an in-field detection system (CLFD) with weather-driven mathematical models predicting pathogen infection have the potential for use within disease management systems.
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Gibbon, J. D., and D. D. Holm. "Extreme events in solutions of hydrostatic and non-hydrostatic climate models." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 369, no. 1939 (March 28, 2011): 1156–79. http://dx.doi.org/10.1098/rsta.2010.0244.
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Initially, this paper reviews the mathematical issues surrounding hydrostatic primitive equations (HPEs) and non-hydrostatic primitive equations (NPEs) that have been used extensively in numerical weather prediction and climate modelling. A new impetus has been provided by a recent proof of the existence and uniqueness of solutions of viscous HPEs on a cylinder with Neumann-like boundary conditions on the top and bottom. In contrast, the regularity of solutions of NPEs remains an open question. With this HPE regularity result in mind, the second issue examined in this paper is whether extreme events are allowed to arise spontaneously in their solutions. Such events could include, for example, the sudden appearance and disappearance of locally intense fronts that do not involve deep convection. Analytical methods are used to show that for viscous HPEs, the creation of small-scale structures is allowed locally in space and time at sizes that scale inversely with the Reynolds number.
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Orosa, José A., Ángel M. Costa, Diego Vergara, and Feliciano Fraguela. "The Influence of Climate Parameters on Maintenance of Wind Farms—A Galician Case Study." Sensors 21, no. 1 (December 23, 2020): 40. http://dx.doi.org/10.3390/s21010040.
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There are different monitoring procedures in wind farms with two main objectives: (i) to improve energy production by the capability of the national electrical network and (ii) to reduce the stooped hours due to preventive and or corrective maintenance activities. In this sense, different sensors are employed to sample in real-time the working conditions of equipment, the electrical production and the weather conditions. Despite this, just the anemometer measurement can be related to the more important errors of interruption of power regulation and anemometer errors. Both errors are related to gusty winds and contribute to more than 33% of the cost of a wind farm. The present paper reports some mathematical relations between weather and maintenance but there are no extreme values of each variable that let us predict a near failure and its corresponding loss of working hours. To achieve this, statistical analysis identifies the relation between weather variables and errors and different models are obtained. What is more, due to the difficulty and economic implications involving the implementation of complex algorithms and techniques of artificial intelligence, it is still a challenge to optimize this process. Finally, the obtained results show a particular case study that can be extrapolated to other wind farms after different case studies to adjust the model to different weather regions, and serve as a useful tool for weather maintenance.
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Miao, Xin, David Banister, Yanhong Tang, Min Li, and Bao Xi. "MAINTAINING THE TRANSPORT SYSTEM UNDER EXTREME WEATHER EVENTS: A DUAL-NETWORK PERSPECTIVE." Technological and Economic Development of Economy 19, Supplement_1 (January 28, 2014): S342—S359. http://dx.doi.org/10.3846/20294913.2013.879748.
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Recent years have seen an increase in the frequency of extreme weather events globally, and these have resulted in severe impacts on the transport system. To the means by which the transport system can be maintained under extreme weather events is an emerging topic in transport studies, and this is augmented by a growing concern about climate change. This paper considers transport system as dual-network composed of an interrelated operation level and management level that has some similarities with the theory behind the Wardrop Principle. Evidence from the case study on the snow event in South China in early 2008 is used to draw the dual-network formulation to generalise the law of maintaining the transport system under extreme weather. The mathematical models of the dual-network focus on entropic dynamics in the operation network and matching control activities in the management network. Quantitative evidence is provided to prove the methodology. Interactions through the form of information communication and organisational collaboration within and between networks are highlighted. Incentive mechanisms are emphasised for achieving effective anticipation, prevention and collaboration to coping with extreme weather events. This paper contributes to a better understanding about the role of networks, collective behaviour, information interchange and inter-organisational collaboration in influencing the maintenance of transport system under extreme weather conditions.
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Briz-Redón, Álvaro, and Ángel Serrano-Aroca. "The effect of climate on the spread of the COVID-19 pandemic: A review of findings, and statistical and modelling techniques." Progress in Physical Geography: Earth and Environment 44, no. 5 (August 4, 2020): 591–604. http://dx.doi.org/10.1177/0309133320946302.
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The new SARS-CoV-2 coronavirus has spread rapidly around the world since it was first reported in humans in Wuhan, China, in December 2019 after being contracted from a zoonotic source. This new virus produces the so-called coronavirus 2019 or COVID-19. Although several studies have supported the epidemiological hypothesis that weather patterns may affect the survival and spread of droplet-mediated viral diseases, the most recent have concluded that summer weather may offer partial or no relief of the COVID-19 pandemic to some regions of the world. Some of these studies have considered only meteorological variables, while others have included non-meteorological factors. The statistical and modelling techniques considered in this research line have included correlation analyses, generalized linear models, generalized additive models, differential equations, or spatio-temporal models, among others. In this paper we provide a systematic review of the recent literature on the effects of climate on COVID-19’s global expansion. The review focuses on both the findings and the statistical and modelling techniques used. The disparate findings reported seem to indicate that the estimated impact of hot weather on the transmission risk is not large enough to control the pandemic, although the wide range of statistical and modelling approaches considered may have partly contributed to the inconsistency of the findings. In this regard, we highlight the importance of being aware of the limitations of the different mathematical approaches, the influence of choosing geographical units and the need to analyse COVID-19 data with great caution. The review seems to indicate that governments should remain vigilant and maintain the restrictions in force against the pandemic rather than assume that warm weather and ultraviolet exposure will naturally reduce COVID-19 transmission.
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Zheng, Muzi, Brian Leib, Wesley Wright, and Paul Ayers. "Neural Models to Predict Temperature and Natural Ventilation in a High Tunnel." Transactions of the ASABE 62, no. 3 (2019): 761–69. http://dx.doi.org/10.13031/trans.12781.
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Abstract. As a response to the rising demand for local food, high tunnels (HTs) can help small producers become more profitable through crop protection and extension of the growing season. Proper ventilation that responds to changes in outside weather conditions can remove excess heat and humidity inside HTs and lead to better solar energy utilization while maintaining a favorable growth environment. Rather than depending on complex mathematical models, this study investigated an artificial neural network (ANN) for predicting the inside air temperature and ventilation rate of a HT. Energy balance calculations and measured values were compared to the ANN. Results showed that the average air temperature from an array of 15 thermistors inside the HT was predicted more accurately in terms of mean square error (MSE = 1.7°C2) and mean absolute error (MAE = 1.0°C) than a single inside temperature at the center of the HT (MSE = 17.7°C2, MAE = 3.3°C). Relative humidity and wind direction had the least significant impacts on the prediction of inside air temperature, and only four outside weather inputs were found to have significant impacts on the prediction of inside temperature: outside air temperature, door opening level, solar radiation, and wind speed. Moreover, the optimal ANN structure was determined as 29, 25, and 13 neurons in a single hidden layer and 30 neurons in two hidden layers for prediction of inside air temperature, ventilation rate based on measurement, door opening level, and ventilation rate based on modeling, respectively. Keywords: Air temperature control, Artificial neural network, High tunnel, Ventilation.
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Kanso, A., M. C. Gromaire, E. Gaume, B. Tassin, and G. Chebbo. "Bayesian approach for the calibration of models: application to an urban stormwater pollution model." Water Science and Technology 47, no. 4 (February 1, 2003): 77–84. http://dx.doi.org/10.2166/wst.2003.0225.
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In environmental modelling, estimating the confidence level in conceptual model parameters is necessary but difficult. Having a realistic estimation of the uncertainties related to the parameters is necessary i) to assess the possible origin of the calibration difficulties (correlation between model parameters for instance), and ii) to evaluate the prediction confidence limits of the calibrated model. In this paper, an application of the Metropolis algorithm, a general Monte Carlo Markov chain sampling method, for the calibration of a four-parameter lumped urban stormwater quality model is presented. Unlike traditional optimisation approaches, the Metropolis algorithm identifies not only a “best parameter set”, but a probability distribution of parameters according to measured data. The studied model includes classical formulations for the pollutant accumulation during dry weather period and their washoff during a rainfall event. Results indicate mathematical shortcomings in the pollutant accumulation formulation used.
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Balaji, V. "Climbing down Charney’s ladder: machine learning and the post-Dennard era of computational climate science." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, no. 2194 (February 15, 2021): 20200085. http://dx.doi.org/10.1098/rsta.2020.0085.
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The advent of digital computing in the 1950s sparked a revolution in the science of weather and climate. Meteorology, long based on extrapolating patterns in space and time, gave way to computational methods in a decade of advances in numerical weather forecasting. Those same methods also gave rise to computational climate science, studying the behaviour of those same numerical equations over intervals much longer than weather events, and changes in external boundary conditions. Several subsequent decades of exponential growth in computational power have brought us to the present day, where models ever grow in resolution and complexity, capable of mastery of many small-scale phenomena with global repercussions, and ever more intricate feedbacks in the Earth system. The current juncture in computing, seven decades later, heralds an end to what is called Dennard scaling, the physics behind ever smaller computational units and ever faster arithmetic. This is prompting a fundamental change in our approach to the simulation of weather and climate, potentially as revolutionary as that wrought by John von Neumann in the 1950s. One approach could return us to an earlier era of pattern recognition and extrapolation, this time aided by computational power. Another approach could lead us to insights that continue to be expressed in mathematical equations. In either approach, or any synthesis of those, it is clearly no longer the steady march of the last few decades, continuing to add detail to ever more elaborate models. In this prospectus, we attempt to show the outlines of how this may unfold in the coming decades, a new harnessing of physical knowledge, computation and data. This article is part of the theme issue ‘Machine learning for weather and climate modelling’.
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Rayburn, Edward B. "Validating a Simple Mechanistic Model That Describes Weather Impact on Pasture Growth." Plants 10, no. 9 (August 24, 2021): 1754. http://dx.doi.org/10.3390/plants10091754.
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Mathematical models have many uses. When input data is limited, simple models are required. This occurs in pasture agriculture when managers typically only have access to temperature and rainfall values. A simple pasture growth model was developed that requires only latitude and daily maximum and minimum temperature and rainfall. The accuracy of the model was validated using ten site-years of measured pasture growth at a site under continuous stocking where management controlled the height of grazing (HOG) and a site under rotational stocking at a West Virginia University farm (WVU). Relative forage growth, expressed as a fraction of maximum growth observed at the sites, was reasonably accurate. At the HOG site constant bias in relative growth was not different from zero. There was a year effect due to the weather station used for predicting growth at HOG being 1.8 km from the pasture. However, the error was only about 10-percent. At the WVU site constant bias for relative growth was not different from zero and year effect was eliminated when adjusted for nitrogen status of the treatments. This simple model described relative pasture growth within 10-percent of average for a given site, environment, and management using only daily weather inputs that are readily available. Using predictions of climate change impact on temperature and rainfall frequency and intensity this model can be used to predict the impact on pasture growth.
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Xu, Chuer, Ailing Liang, and Wen Wang. "Research on Glass Classification Based on K-means Clustering and Decision Tree." Highlights in Science, Engineering and Technology 21 (December 4, 2022): 137–47. http://dx.doi.org/10.54097/hset.v21i.3149.
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Ancient glass is very vulnerable to weather due to the influence of the burial environment, and its chemical composition proportion changes, which makes it impossible to correctly judge its category. In this paper, mathematical models are established based on decision tree and k-means clustering, respectively, to analyze the classification rules of high potassium glass and lead barium glass, and to find out the classification method for sub classification of the two types of glass. Finally, the mode of distinguishing the types of class cultural relics is established, which has guided significance for the identification of class cultural relics.
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Chigarev, Anatoliy V., Michael A. Zhuravkov, and Vitaliy A. Chigarev. "Deterministic and stochastic models of infection spread and testing in an isolated contingent." Journal of the Belarusian State University. Mathematics and Informatics, no. 3 (November 19, 2021): 57–67. http://dx.doi.org/10.33581/2520-6508-2021-3-57-67.
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The mathematical SIR model generalisation for description of the infectious process dynamics development by adding a testing model is considered. The proposed procedure requires the expansion of states’ space dimension due to variables that cannot be measured directly, but allow you to more adequately describe the processes that occur in real situations. Further generalisation of the SIR model is considered by taking into account randomness in state estimates, forecasting, which is achieved by applying the stochastic differential equations methods associated with the application of the Fokker – Planck – Kolmogorov equations for posterior probabilities. As COVID-19 practice has shown, the widespread use of modern means of identification, diagnosis and monitoring does not guarantee the receipt of adequate information about the individual’s condition in the population. When modelling real epidemic processes in the initial stages, it is advisable to use heuristic modelling methods, and then refine the model using mathematical modelling methods using stochastic, uncertain-fuzzy methods that allow you to take into account the fact that flow, decision-making and control occurs in systems with incomplete information. To develop more realistic models, spatial kinetics must be taken into account, which, in turn, requires the use of systems models with distributed parameters (for example, models of continua mechanics). Obviously, realistic models of epidemics and their control should include models of economic, sociodynamics. The problems of forecasting epidemics and their development will be no less difficult than the problems of climate change forecasting, weather forecast and earthquake prediction.
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Lo Brano, Valerio, Giuseppina Ciulla, and Mariavittoria Di Falco. "Artificial Neural Networks to Predict the Power Output of a PV Panel." International Journal of Photoenergy 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/193083.
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The paper illustrates an adaptive approach based on different topologies of artificial neural networks (ANNs) for the power energy output forecasting of photovoltaic (PV) modules. The analysis of the PV module’s power output needed detailed local climate data, which was collected by a dedicated weather monitoring system. The Department of Energy, Information Engineering, and Mathematical Models of the University of Palermo (Italy) has built up a weather monitoring system that worked together with a data acquisition system. The power output forecast is obtained using three different types of ANNs: a one hidden layer Multilayer perceptron (MLP), a recursive neural network (RNN), and a gamma memory (GM) trained with the back propagation. In order to investigate the influence of climate variability on the electricity production, the ANNs were trained using weather data (air temperature, solar irradiance, and wind speed) along with historical power output data available for the two test modules. The model validation was performed by comparing model predictions with power output data that were not used for the network's training. The results obtained bear out the suitability of the adopted methodology for the short-term power output forecasting problem and identified the best topology.
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Finkel, Justin, Dorian S. Abbot, and Jonathan Weare. "Path Properties of Atmospheric Transitions: Illustration with a Low-Order Sudden Stratospheric Warming Model." Journal of the Atmospheric Sciences 77, no. 7 (July 1, 2020): 2327–47. http://dx.doi.org/10.1175/jas-d-19-0278.1.
Full textAbstract:
AbstractMany rare weather events, including hurricanes, droughts, and floods, dramatically impact human life. To accurately forecast these events and characterize their climatology requires specialized mathematical techniques to fully leverage the limited data that are available. Here we describe transition path theory (TPT), a framework originally developed for molecular simulation, and argue that it is a useful paradigm for developing mechanistic understanding of rare climate events. TPT provides a method to calculate statistical properties of the paths into the event. As an initial demonstration of the utility of TPT, we analyze a low-order model of sudden stratospheric warming (SSW), a dramatic disturbance to the polar vortex that can induce extreme cold spells at the surface in the midlatitudes. SSW events pose a major challenge for seasonal weather prediction because of their rapid, complex onset and development. Climate models struggle to capture the long-term statistics of SSW, owing to their diversity and intermittent nature. We use a stochastically forced Holton–Mass-type model with two stable states, corresponding to radiative equilibrium and a vacillating SSW-like regime. In this stochastic bistable setting, from certain probabilistic forecasts TPT facilitates estimation of dominant transition pathways and return times of transitions. These “dynamical statistics” are obtained by solving partial differential equations in the model’s phase space. With future application to more complex models, TPT and its constituent quantities promise to improve the predictability of extreme weather events through both generation and principled evaluation of forecasts.
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Lucarini, Valerio, and Andrey Gritsun. "A new mathematical framework for atmospheric blocking events." Climate Dynamics 54, no. 1-2 (November 1, 2019): 575–98. http://dx.doi.org/10.1007/s00382-019-05018-2.
Full textAbstract:
Abstract We use a simple yet Earth-like hemispheric atmospheric model to propose a new framework for the mathematical properties of blocking events. Using finite-time Lyapunov exponents, we show that the occurrence of blockings is associated with conditions featuring anomalously high instability. Longer-lived blockings are very rare and have typically higher instability. In the case of Atlantic blockings, predictability is especially reduced at the onset and decay of the blocking event, while a relative increase of predictability is found in the mature phase. The opposite holds for Pacific blockings, for which predictability is lowest in the mature phase. Blockings are realised when the trajectory of the system is in the neighbourhood of a specific class of unstable periodic orbits (UPOs), natural modes of variability that cover the attractor the system. UPOs corresponding to blockings have, indeed, a higher degree of instability compared to UPOs associated with zonal flow. Our results provide a rigorous justification for the classical Markov chains-based analysis of transitions between weather regimes. The analysis of UPOs elucidates that the model features a very severe violation of hyperbolicity, due to the presence of a substantial variability in the number of unstable dimensions, which explains why atmospheric states can differ a lot in term of their predictability. Additionally, such a variability explains the need for performing data assimilation in a state space that includes not only the unstable and neutral subspaces, but also some stable modes. The lack of robustness associated with the violation of hyperbolicity might be a basic cause contributing to the difficulty in representing blockings in numerical models and in predicting how their statistics will change as a result of climate change. This corresponds to fundamental issues limiting our ability to construct very accurate numerical models of the atmosphere, in term of predictability of the both the first and of the second kind in the sense of Lorenz.